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Craters are among the most abundant features on the surface of many planets with great importance for planetary scientists. They reveal chronology information about planets and may be used for autonomous spacecraft navigation and landing. Although numerous research efforts have been carried out in the field of crater detection, existing crater detection algorithms (CDAs) are only helpful in a limited number of applications. A promising crater detection approach involves two main steps: 1) hypothesis generation (HG) and 2) hypothesis verification (HV). During HG, potential crater locations are detected. The validity of the hypothesized crater locations is then tested in a HV step. In this context, we discuss some commonly used algorithms for HG such as highlight-shadow region detection and Hough transform as well as our novel and enhanced algorithms based on interest point detection and convex grouping. A key objective of this paper is to analyze their performance while paying special attention to how they affect the accuracy of the verification step. To deal with different size craters, we focus on multiscale HG. For HV, we have chosen convolutional neural networks which have recently achieved state-of-the-art performance in many computer vision applications. Due to the variation of test sets in the literature, it is often challenging to compare the performance of different CDAs in a fair way. In this paper, we present a comprehensive performance evaluation and comparison of CDAs. Each algorithm has been trained/tested using common data sets generated by a systematic approach. [ABSTRACT FROM AUTHOR]

Image segmentation techniques are challenging to apply to large-size remote sensing imagery. Indeed, if the data to be processed are larger than the computer’s available memory, it must be split into smaller pieces. Without precaution, segmentation errors appear along the edges of these pieces. The goal of this paper is to present a tilewise processing method to overcome this issue for superpixel segmentation, applied in particular to the simple linear iterative clustering algorithm. Incidentally, tilewise methods allow for several pieces of the image to be processed simultaneously, which enables the deployment of these methods in a parallel processing environment. Estimations of the speed-up when using multiple processors are provided. Then, it is demonstrated that the result of the tilewise segmentation is equivalent to the segmentation of the complete image, with respect to a number of global unsupervised segmentation criteria. Finally, experimental results on a large-size Sentinel-2 time series validate the method’s feasibility. [ABSTRACT FROM AUTHOR]

Detection of small targets, more specifically cars, in aerial images of urban scenes, has various applications in several domains, such as surveillance, military, remote sensing, and others. This is a tremendously challenging problem, mainly because of the significant interclass similarity among objects in urban environments, e.g., cars and certain types of nontarget objects, such as buildings’ roofs and windows. These nontarget objects often possess very similar visual appearance to that of cars making it hard to separate the car and the noncar classes. Accordingly, most past works experienced low precision rates at high recall rates. In this paper, a novel framework is introduced that achieves a higher precision rate at a given recall than the state of the art. The proposed framework adopts a sliding-window approach and it consists of four stages, namely, window evaluation, extraction and encoding of features, classification, and postprocessing. This paper introduces a new way to derive descriptors that encode the local distributions of gradients, colors, and texture. Image descriptors characterize the aforementioned cues using adaptive cell distributions, wherein the distribution of cells within a detection window is a function of its dominant orientation, and hence, neither the rotation of the patch under examination nor the computation of descriptors at different orientations is required. The performance of the proposed framework has been evaluated on the challenging Vaihingen and Overhead Imagery Research data sets. Results demonstrate the superiority of the proposed framework to the state of the art. [ABSTRACT FROM PUBLISHER]

Processing large very high-resolution remote sensing images on resource-constrained devices is a challenging task because of the large size of these data sets. For applications such as environmental monitoring or natural resources management, complex algorithms have to be used to extract information from the images. The memory required to store the images and the data structures of such algorithms may be very high (hundreds of gigabytes) and therefore leads to unfeasibility on commonly available computers. Segmentation algorithms constitute an essential step for the extraction of objects of interest in a scene and will be the topic of the investigation in this paper. The objective of the present work is to adapt image segmentation algorithms for large amounts of data. To overcome the memory issue, large images are usually divided into smaller image tiles, which are processed independently. Region-merging algorithms do not cope well with image tiling since artifacts are present on the tile edges in the final result due to the incoherencies of the regions across the tiles. In this paper, we propose a scalable tile-based framework for region-merging algorithms to segment large images, while ensuring identical results, with respect to processing the whole image at once. We introduce the original concept of the stability margin for a tile. It allows ensuring identical results to those obtained if the whole image had been segmented without tiling. Finally, we discuss the benefits of this framework and demonstrate the scalability of this approach by applying it to real large images. [ABSTRACT FROM PUBLISHER]

The automatic detection of buried threats in ground penetrating radar (GPR) data is an active area of research due to GPR's ability to detect both metal and nonmetal subsurface objects. Recent work on algorithms designed to distinguish between threats and nonthreats in GPR data has utilized computer vision methods to advance the state-of-the-art detection and discrimination performance. Feature extractors, or descriptors, from the computer vision literature have exhibited excellent performance in representing 2-D GPR image patches and allow for robust classification of threats from nonthreats. This paper aims to perform a broad study of feature extraction methods in order to identify characteristics that lead to improved classification performance under controlled conditions. The results presented in this paper show that gradient-based features, such as the edge histogram descriptor and the scale invariant feature transform, provide the most robust performance across a large and varied data set. These results indicate that various techniques from the computer vision literature can be successfully applied to target detection in GPR data and that more advanced techniques from the computer vision literature may provide further performance improvements. [ABSTRACT FROM PUBLISHER]

In this paper, we contribute to answer the question: How accurately can we estimate heights of buildings with flat roofs given one high-resolution single-pass interferometric synthetic aperture radar (InSAR) image pair and one aerial orthophoto? What makes this problem challenging are the different sensor geometries and the sound stochastic combination of all available elevation cues. We revisit already existing methods and develop novel approaches to determine building heights. A rigorous stochastic approach based on least squares adjustment with functionally dependent parameters is introduced to combine all height measurements per building to one robust height estimate. Observation accuracies of the stochastic model are either taken from the literature or estimated empirically. A major benefit of adjustment is that it delivers a posterior standard deviation per height, which can be interpreted as a precision indicator and is of high relevance for practical applications. Estimated heights of an urban scene are compared to ground truth acquired with airborne laser scanning, allowing us to assess height accuracies that can be achieved under nearly optimal conditions. We conduct statistical tests that validate our model and show that a weighted combination of optical and synthetic aperture radar (SAR) data with least squares adjustment delivers reliable height estimates with meter accuracy for flat-roofed buildings. Additionally, we empirically estimate a confidence interval of the estimated heights that directly tells the user the security margin to be included, for example, in case of building evacuations for an anticipated flooding event, under the condition that the data and model have the same specifications as in this paper. [ABSTRACT FROM PUBLISHER]

The objective of synthetic aperture radar (SAR) edge detection is the identification of contours across the investigated scene, exploiting SAR complex data. Edge detectors available in the literature exploit singularly amplitude and interferometric phase information, looking for reflectivity or height difference between neighboring pixels, respectively. Recently, more performing detectors based on the joint processing of amplitude and interferometric phase data have been presented. In this paper, we propose a novel approach based on the exploitation of real and imaginary parts of single-look complex acquired data. The technique is developed in the framework of stochastic estimation theory, exploiting Markov random fields. Compared to available edge detectors, the technique proposed in this paper shows useful advantages in terms of model complexity, phase artifact robustness, and scenario applicability. Experimental results on both simulated and real TerraSAR-X and COSMO-SkyMed data show the interesting performances and the overall effectiveness of the proposed method. [ABSTRACT FROM PUBLISHER]

In this paper, we propose a task-oriented enhancing technique for extracting road networks from satellite images. By exploiting an approximate estimation of the potential road edges for guidance, we developed a joint enhancing filtering framework to generate a version of the input image that facilitates road network extraction. First, an adaptive smoothing scheme is designed to suppress the interference of noise or heavy textures, such as residential areas or terrain boundaries. By combining this scheme with the proposed novel anisotropic shock filter, the edges of the potential road regions can be kept sharp and clear. Through abundant experimental comparisons with state-of-the-art filtering techniques and quantitative evaluations using data from various satellite sensors, the performance of the proposed approach is comprehensively evaluated. The experimental results demonstrate that our system can address heavy high contrast textures and provide a meaningful improvement in the feature detection for road extraction. [ABSTRACT FROM AUTHOR]

For random-field-based image segmentation, the conditional random field (CRF) model offers theoretic advantages over the generative Markov random field one, since it directly models the posterior distribution of label field conditioned on an observable image. In this paper, we propose an adaptive hybrid CRF (AHCRF) model for synthetic aperture radar (SAR) image segmentation. Based on the generation of superpixels and their boundary feature analysis, the proposed method adaptively divides SAR image into different parts, namely, homogeneous regions, heterogeneous regions, and edges. In homogeneous regions, the regional-level CRF is defined on superpixels, and the pixels within each superpixel force to have the same segmentation label. Oppositely, the pixel-level CRF is defined on pixels within heterogeneous regions or edges, and local autocovariance features are extracted for constructing the unary and pairwise potentials to incorporate effective local contextual information. The integration of regional-level and pixel-level CRFs gives the proposed AHCRF model, and it is validated by experiments on several real SAR images. The experimental analysis indicates that the AHCRF is robust to speckle noise and preserves detailed features well in segmentation. [ABSTRACT FROM PUBLISHER]

The current superresolution (SR) methods based on deep learning have shown remarkable comparative advantages but remain unsatisfactory in recovering the high-frequency edge details of the images in noise-contaminated imaging conditions, e.g., remote sensing satellite imaging. In this paper, we propose a generative adversarial network (GAN)-based edge-enhancement network (EEGAN) for robust satellite image SR reconstruction along with the adversarial learning strategy that is insensitive to noise. In particular, EEGAN consists of two main subnetworks: an ultradense subnetwork (UDSN) and an edge-enhancement subnetwork (EESN). In UDSN, a group of 2-D dense blocks is assembled for feature extraction and to obtain an intermediate high-resolution result that looks sharp but is eroded with artifacts and noises as previous GAN-based methods do. Then, EESN is constructed to extract and enhance the image contours by purifying the noise-contaminated components with mask processing. The recovered intermediate image and enhanced edges can be combined to generate the result that enjoys high credibility and clear contents. Extensive experiments on Kaggle Open Source Data set, Jilin-1 video satellite images, and Digitalglobe show superior reconstruction performance compared to the state-of-the-art SR approaches. [ABSTRACT FROM AUTHOR]

This paper introduces a novel heterogeneous domain adaptation (HDA) method for hyperspectral image (HSI) classification with a limited amount of labeled samples in both domains. The method is achieved in the way of cross-domain collaborative learning (CDCL), which is addressed via cluster canonical correlation analysis (C-CCA) and random walker (RW) algorithms. To be specific, the proposed CDCL method is an iterative process of three main components, i.e., RW-based pseudolabeling, cross-domain learning via C-CCA, and final classification based on extended RW (ERW) algorithm. First, given the initially labeled target samples as the training set (TS), the RW-based pseudolabeling is employed to update TS and extract target clusters (TCs) by fusing the segmentation results obtained by RW and ERW classifiers. Second, cross-domain learning via C-CCA is applied using labeled source samples and TCs. The unlabeled target samples are then classified with the estimated probability maps using the model trained in the projected correlation subspace. The newly estimated probability map and TS are used for updating TS again via RW-based pseudolabeling. Finally, when the iterative process converges, the result obtained by the ERW classifier using the final TS and estimated probability maps is regarded as the final classification map. Experimental results on four real HSIs demonstrate that the proposed method can achieve better performance compared with the state-of-the-art HDA and ERW methods. [ABSTRACT FROM AUTHOR]

Due to the speckle noise and complex geometric distortions within SAR images, it is still a challenge to develop a stable method that can produce superpixels with both high boundary adherence and visual compactness with low computational costs at the same time. In this paper, we propose an adaptive superpixel generation approach with linear feature clustering and edge constraint for synthetic aperture radar (SAR) images, which consists of three stages. First, the local gradient ratio pattern of each pixel in SAR imagery is extracted as features, which was previously proposed by us for SAR target recognition and has been proven to be insensitive to speckle noise. Second, we propose to use the feature-ratio-based edge detector with Gauss-shaped window instead of the traditional rectangle-shaped window to obtain the edge strength map and final edges for SAR images. Finally, a modified normalized cut (Ncut)-based superpixel generation strategy is adopted using a distance metric that simultaneously measures both the feature similarity and space proximity. In this strategy, we approximate the similarity measure through a positive semidefinite kernel function rather than directly using the traditional eigen-based algorithm. Therefore, the objective functions of weighted local K-means and Ncuts can achieve the same optimum point by appropriately weighting each point in this feature space, which greatly reduces the computation cost. During the linear feature clustering, the coefficient of variation is used to automatically determine the tradeoff factor between the feature similarity and space proximity, which helps change the superpixel shape and size adaptively according to the image homogeneity. Furthermore, the edge information is also introduced to constrain the clustering for the sake of high boundary adherence. By bridging the local K-means clustering and Ncuts, as well as the benefits of edge constraint, our method not only produces superpixels with good boundary adherence but also captures the global image structure information. Experimental results with simulated and real SAR images demonstrate the effectiveness of our proposed method, which performs better than other state-of-the-art algorithms. [ABSTRACT FROM AUTHOR]

This paper presents a novel Markov random field (MRF) method integrating adaptive interclass-pair penalty (aICP2) and spectral similarity information (SSI) for hyper-spectral image (HSI) classification. aICP2 structurally combines $K(K - 1)/2$ (K is the number of classes) classical “Potts model” with $K(K - 1)/2$ interaction coefficients. aICP2 tries a new way to solve the key problems, insufficient correction within homogeneous regions, and over-smoothness at class boundaries, in MRF-based HSI classification. It is assumed that different class pairs should be assigned with various degrees of penalties in MRF smoothness process, according to pairwise class separability and spatial class confusion in raw classification map. The Fisher ratio is modified to measure pairwise class separability with a training set. And, gray level co-occurrence matrix is used to measure spatial class confusion degree. Then, aICP2 is constructed by combining Fisher ratio and GCLM. aICP2 applies larger penalty on class pairs that confuse with each other seriously to provide sufficient smoothness, and vice versa. In addition, to protect class edges and details, SSI is introduced to make the penalty of related neighboring pixels small. aICP2ssi denotes the integration of aICP2 and SSI. The further improved method is both interclass-pair and interpixel adaptive in spatial term. A graph-cut-based $\alpha - \beta $ -swap method is introduced to optimize the proposed energy function. The experimental results on real HSI data indicate that the proposed method outperforms compared MRF-based and other spectral–spatial approaches in terms of classification accuracies and region uniformity. [ABSTRACT FROM AUTHOR]

It is a classical task to automatically extract road networks from very high-resolution (VHR) images in remote sensing. This paper presents a novel method for extracting road networks from VHR remotely sensed images in complex urban scenes. Inspired by image segmentation, edge detection, and object skeleton extraction, we develop a multitask convolutional neural network (CNN), called RoadNet, to simultaneously predict road surfaces, edges, and centerlines, which is the first work in such field. The RoadNet solves seven important issues in this vision problem: 1) automatically learning multiscale and multilevel features [gained by the deeply supervised nets (DSN) providing integrated direct supervision] to cope with the roads in various scenes and scales; 2) holistically training the mentioned tasks in a cascaded end-to-end CNN model; 3) correlating the predictions of road surfaces, edges, and centerlines in a network model to improve the multitask prediction; 4) designing elaborate architecture and loss function, by which the well-trained model produces approximately single-pixel width road edges/centerlines without nonmaximum suppression postprocessing; 5) cropping and bilinear blending to deal with the large VHR images with finite-computing resources; 6) introducing rough and simple user interaction to obtain desired predictions in the challenging regions; and 7) establishing a benchmark data set which consists of a series of VHR remote sensing images with pixelwise annotation. Different from the previous works, we pay more attention to the challenging situations, in which there are lots of shadows and occlusions along the road regions. Experimental results on two benchmark data sets show the superiority of our proposed approaches. [ABSTRACT FROM AUTHOR]

Aiming at improving the classification accuracy with limited numbers of labeled pixels in polarimetric synthetic aperture radar (PolSAR) image classification task, this paper presents a graph-based semisupervised deep learning model for PolSAR image classification. It models the PolSAR image as an undirected graph, where the nodes correspond to the labeled and unlabeled pixels, and the weighted edges represent similarities between the pixels. Upon the graph, we design an energy function incorporating a semisupervision term, a convolutional neural network (CNN) term, and a pairwise smoothness term. The employed CNN extracts abstract and data-driven polarimetric features and outputs class label predictions to the graph model. The semisupervision term enforces the category label constraints on the human-labeled pixels. The pairwise smoothness term encourages class label smoothness and the alignment of class label boundaries with the image edges. Starting from an initialized class label map generated based on $K$ -Wishart distribution hypothesis or superpixel segmentation of PauliRGB images, we iteratively and alternately optimize the defined energy function until it converges. We conducted experiments on two real benchmark PolSAR images, and extensive experiments demonstrated that our approach achieved the state-of-the-art results for PolSAR image classification. [ABSTRACT FROM AUTHOR]

This paper provides a novel pointwise-adaptive speckle filter based on local homogeneous-region segmentation with pixel-relativity measurement. A ratio distance is proposed to measure the distance between two speckled-image patches. The theoretical proofs indicate that the ratio distance is valid for multiplicative speckle, while the traditional Euclidean distance failed in this case. The probability density function of the ratio distance is deduced to map the distance into a relativity value. This new relativity-measurement method is free of parameter setting and more functional compared with the Gaussian kernel-projection-based ones. The new measurement method is successfully applied to segment a local shape-adaptive homogeneous region for each pixel, and a simplified strategy for the segmentation implementation is given in this paper. After segmentation, the maximum likelihood rule is introduced to estimate the true signal within every homogeneous region. A novel evaluation metric of edge-preservation degree based on ratio of average is also provided for more precise quantitative assessment. The visual and numerical experimental results show that the proposed filter outperforms the existing state-of-the-art despeckling filters. [ABSTRACT FROM AUTHOR]

This paper presents a novel technique for automatic edge enhancement and detection in synthetic aperture radar (SAR) images. The characteristics of SAR images justify the importance of an edge enhancement step prior to edge detection. Therefore, this paper presents a robust and unsupervised edge enhancement algorithm based on a combination of wavelet coefficients at different scales. The performance of the method is first tested on simulated images. Then, in order to complete the automatic detection chain, among the different options for the decision stage, the use of geodesic active contour is proposed. The second part of this paper suggests the extraction of the coastline in SAR images as a particular case of edge detection. Hence, after highlighting its practical interest, the technique that is theoretically presented in the first part of this paper is applied to real scenarios. Finally, the chances of its operational capability are assessed. [ABSTRACT FROM AUTHOR]

In this paper, a novel method is introduced to detect and correct mislabeled training samples for hyperspectral image classification. First, domain transform recursive filtering-based feature extraction is used to improve the separability of the training samples. Then, constrained energy minimization-based object detection is performed on the training set with each training sample serving as the object spectrum. Finally, the label of each training sample is verified or corrected based on the averaged detection probabilities of different classes. Experiments performed on real hyperspectral data sets demonstrate the effectiveness of the proposed method in improving classification performance with respect to the classifier trained with the original training set that contains a number of mislabeled samples. [ABSTRACT FROM AUTHOR]

In this paper, we propose a self-learning approach for remote sensing image classification. The main work of this paper aims at providing a new framework of semisupervised learning technique for multiple-source synergetic classification, thereby improving the classification accuracy under the condition of small samples. Considering the high spectral resolution of a hyperspectral (HS) image and the high spatial resolution of a panchromatic (PAN) image, the proposed approach combines image segmentation with an active learning algorithm and adopts a standard active learning method for a self-learning strategy, in which the learning algorithm automatically selects informative unlabeled samples by itself according to their collaborative spatial–spectral features and the predicted information of a spectral-based classifier. This way, no extra cost of human expertise is required for labeling the selected pixels when compared with conventional active learning methods. Experiments on three data sets, including HS and PAN images, indicate that our proposed approach has a great enhancement on overall classification accuracy compared with classical supervised algorithms and turns out to be a promising strategy in synergetic classification of HS and PAN images. [ABSTRACT FROM PUBLISHER]

Remote sensing images exhibit significant contrast and intensity regions and edges, which makes them highly suitable for using different texture features to properly represent and classify the objects that they contain. In this paper, we present a new technique based on multiple morphological component analysis (MMCA) that exploits multiple textural features for decomposition of remote sensing images. The proposed MMCA framework separates a given image into multiple pairs of morphological components (MCs) based on different textural features, with the ultimate goal of improving the signal-to-noise level and the data separability. A distinguishing feature of our proposed approach is the possibility to retrieve detailed image texture information, rather than using a single spatial characteristic of the texture. In this paper, four textural features: content, coarseness, contrast, and directionality (including horizontal and vertical), are considered for generating the MCs. In order to evaluate the obtained MCs, we conduct classification by using both remotely sensed hyperspectral and polarimetric synthetic aperture radar (SAR) scenes, showing the capacity of the proposed method to deal with different kinds of remotely sensed images. The obtained results indicate that the proposed MMCA framework can lead to very good classification performances in different analysis scenarios with limited training samples. [ABSTRACT FROM PUBLISHER]

The Visible Infrared Imaging Radiometer Suite (VIIRS) instrument aboard the Suomi National Polar-orbiting Partnership satellite has been successfully operating since its launch on October 28, 2011. Unlike its predecessor MODerate resolution Imaging Spectroradiometer (MODIS), VIIRS has no independent onboard calibrator to perform the on-orbit characterization of its spatial parameters such as the band-to-band registration (BBR), detector-to-detector registration (DDR), and modulation transfer function (MTF). The surface properties of the Moon have been demonstrated to be spectrally, radiometrically, and geometrically stable in the long term, making the Moon a suitable target for the on-orbit calibration of satelliteborne remote sensing instruments. The methodologies of spatial characterization using the Moon have been developed for MODIS and validated, through the comparison of their results to those calibrated by the onboard spatial calibrator of MODIS. In this paper, the methodologies are extended and improved for VIIRS applications. The BBR, DDR in both along-scan and along-track directions and the MTF in the along-track direction of VIIRS are calculated from the lunar observations scheduled on a nearly monthly basis. The trending results confirm that these parameters have been stable over time: the BBR offset is less than 0.05 pixels in both directions, well within the performance specification requirement of 0.1 pixels. The along-track MTF is approximately 0.6, well above the specification of 0.3, and is consistent with the prelaunch measurement. The limitation of the current methodologies and the possible future improvements are also discussed in this paper. [ABSTRACT FROM PUBLISHER]

This paper develops several new strategies for remote sensing image classification postprocessing (CPP) and conducts a systematic study in this area. CPP is defined as a refinement of the labeling in a classified image in order to enhance its original classification accuracy. The current mainstream classification methods (preprocessing) extract additional spatial features in order to complement spectral information and enhance classification using spectral responses alone. On the other hand, however, the CPP methods, providing a new solution to improve classification accuracy by refining the initial result, have not received sufficient attention. They have potential for achieving comparable accuracy to the preprocessing methods but in a more direct and succinct way. In this paper, we consider four groups of CPP strategies: filtering; random field; object-based voting; and relearning. In addition to the state-of-the-art CPP algorithms, we also propose a series of new ones, e.g., anisotropic probability diffusion and primitive cooccurrence matrix. In experiments, a number of multisource remote sensing data sets are used for evaluation of the considered CPP algorithms. It is shown that all the CPP strategies are capable of providing more accurate results than the raw classification. Among them, the relearning approaches achieve the best results. In addition, our relearning algorithms are compared with the state-of-the-art spectral-spatial classification. The results obtained further verify the effectiveness of CPP in different remote sensing applications. [ABSTRACT FROM PUBLISHER]

Spectral unmixing (SU) methods incorporating the spatial regularizations have demonstrated increasing interest. Although spatial regularizers that promote smoothness of the abundance maps have been widely used, they may overly smooth these maps and, in particular, may not preserve edges present in the hyperspectral image. Existing unmixing methods usually ignore these edge structures or use edge information derived from the hyperspectral image itself. However, this information may be affected by the large amounts of noise or variations in illumination, leading to erroneous spatial information incorporated into the unmixing procedure. This paper proposes a simple yet powerful SU framework that incorporates external data [i.e. light detection and ranging (LiDAR) data]. The LiDAR measurements can be easily exploited to adjust the standard spatial regularizations applied to the unmixing process. The proposed framework is rigorously evaluated using two simulated data sets and a real hyperspectral image. It is compared with methods that rely on spatial information derived from a hyperspectral image. The results show that the proposed framework can provide better abundance estimates and, more specifically, can significantly improve the abundance estimates for the pixels affected by shadows. [ABSTRACT FROM AUTHOR]

In this paper, a novel SuperPixel-based Extended Random Walker (SPERW) classification method for hyperspectral images is proposed that consists of three main steps. First, a multiscale segmentation algorithm is adopted to generate many superpixels, each of which represents a homogeneous region of adaptive shape and size. Then, a new weighted graph is constructed based on the superpixels in which the nodes correspond to the superpixels and the edges correspond to the links connecting two adjacent superpixels. Each edge has a weight that defines the similarity between the two superpixels. Second, a widely used pixelwise classifier, i.e., the support vector machine, is adopted to obtain classification probability maps for a hyperspectral image, which are then used to approximate the prior probabilities of the superpixels. Finally, the obtained prior probability maps of the superpixels are optimized by using the Extended Random Walker (ERW) algorithm, which encodes the spatial information both among and within the superpixels of the hyperspectral image in a weighted graph. Compared with the spectrum of a single pixel, the spectrum of a superpixel is more stable and less affected by noise; therefore, superpixels are more appropriate for adoption as the basic elements in the hyperspectral image classification. Because the spectral correlation between pixels within the same superpixel and the spatial correlation among adjacent superpixels are both well considered in the ERW-based global optimization framework, the proposed method shows high classification accuracy on four widely used real hyperspectral data sets even when the number of training samples is relatively small. [ABSTRACT FROM AUTHOR]

Robust and effective detection of small target and false alarm (FA) suppression are the key techniques in infrared search and track systems. In this paper, the derivative entropy-based contrast measure (DECM) is proposed for small-target detection under various complex background clutters. Initially, different directional derivatives of an infrared image are calculated based on the facet model. Then, by analyzing the derivative properties of small target, the primitive entropy formula is improved by incorporating derivative information. With the improved entropy, the contrast measure is constructed to enhance small target and suppress background clutters in each derivative subband. Finally, the contrast measure maps derived from derivative subbands are fused together. The small target could be segmented easily from the fusion result. Experimental results demonstrate that DECM could effectively enhance dim small targets and suppress complex background clutters. Besides, DECM is also robust to infrared small-target images with noises of different levels. The detection results achieve higher detection ratio and lower FA compared with those of other methods under various infrared scenes. [ABSTRACT FROM AUTHOR]

Edge-preserving features (EPFs) obtained by the application of edge-preserving filters to hyperspectral images (HSIs) have been found very effective in characterizing significant spectral and spatial structures of objects in a scene. However, a direct use of the EPFs can be insufficient to provide a complete characterization of spatial information when objects of different scales are present in the considered images. Furthermore, the edge-preserving smoothing operation unavoidably decreases the spectral differences among objects of different classes, which may affect the following classification. To overcome these problems, in this paper, a novel principal component analysis (PCA)-based EPFs (PCA-EPFs) method for HSI classification is proposed, which consists of the following steps. First, the standard EPFs are constructed by applying edge-preserving filters with different parameter settings to the considered image, and the resulting EPFs are stacked together. Next, the spectral dimension of the stacked EPFs is reduced with the PCA, which not only can represent the EPFs in the mean square sense but also highlight the separability of pixels in the EPFs. Finally, the resulting PCA-EPFs are classified by a support vector machine (SVM) classifier. Experiments performed on several real hyperspectral data sets show the effectiveness of the proposed PCA-EPFs, which sharply improves the accuracy of the SVM classifier with respect to the standard edge-preserving filtering-based feature extraction method, and other widely used spectral-spatial classifiers. [ABSTRACT FROM PUBLISHER]

A novel method for anomaly detection in hyperspectral images is proposed. The method is based on two ideas. First, compared with the surrounding background, objects with anomalies usually appear with small areas and distinct spectral signatures. Second, for both the background and the objects with anomalies, pixels in the same class are usually highly correlated in the spatial domain. In this paper, the pixels with specific area property and distinct spectral signatures are first detected with attribute filtering and a Boolean map-based fusion approach in order to obtain an initial pixel-wise detection result. Then, the initial detection result is refined with edge-preserving filtering to make full use of the spatial correlations among adjacent pixels. Compared with other widely used anomaly detection methods, the experimental results obtained on real hyperspectral data sets including airport, beach, and urban scenes demonstrate that the performance of the proposed method is quite competitive in terms of computing time and detection accuracy. [ABSTRACT FROM PUBLISHER]

Techniques concerning postdisaster assessment from remotely sensed images have been studied by different research communities in the past decade. Such an assessment benefits a range of stakeholders, e.g., government organizations, insurance industry, local communities, and individual homeowners. This work explores detailed damage assessment on an individual building basis by utilizing supervised classification. In contrast with previous research efforts in the field, this work attempts at predicting the type of damages such as missing tiles, collapsed rooftop, and presence of holes, gaps, or cavities. Various existing and novel intensity-, edge-, and color-based features are evaluated. Additionally, preprocessing steps that automatically correct photometric and geometric differences are proposed. Furthermore, a study on the reliability of high-resolution aerial imagery in damage interpretation is conducted by comparing results with the assessment of expert volunteers. Results show that the proposed damage detection framework is very effective and performs at a level similar to that of the experts. This paper concludes that the type and extent of damage to individual rooftops can be identified with good accuracy from high-resolution aerial images. It is envisaged that the automated tools presented in this paper would play a significant role in rapid posthurricane damage estimation and in helping to better manage rescue and recovery missions. [ABSTRACT FROM PUBLISHER]

The high-contrast edge of the Moon has been used for the on-orbit measurement of the modulation transfer function (MTF) of remote sensing instruments with a lunar observation capability. With the lunar edge as a target, the classical edge method is applied to the National Aeronautics and Space Administration's MODerate resolution Imaging Spectroradiometer (MODIS) on board the Terra and Aqua satellites. One of the major difficulties encountered during the calculation is that the spatial resolution of MODIS is too coarse to capture the fine structure of the edge spread function (ESF), which is required to calculate the MTF. To produce the MODIS ESF in high resolution, lunar images of a selected edge acquired by multiple instrument scans need to be superposed by aligning the edge positions accurately. In this paper, an algorithm is developed to perform the alignment, based on the lunar position data generated by the MODIS geolocation algorithm and recorded scan by scan. The positions of the lunar edges at the focal plane are calculated scan by scan, allowing the construction of a high-quality ESF for MTF derivation. The algorithm is applied to all MODIS bands with 250-m, 500-m, and 1-km spatial resolutions in both along-scan and along-track directions. The along-track MTF results are particularly valuable because the onboard SpectroRadiometric Calibration Assembly can only monitor the along-scan MTF. The trending results show that the along-track MTF of MODIS has been stable throughout the MODIS lifetime and is well above the design specification for all bands. The limitation of the algorithm is analyzed. The algorithm developed in this paper can be applied to other instruments with similar design features. [ABSTRACT FROM PUBLISHER]

Joint spectral and spatial information should be fully exploited in order to achieve accurate classification results for hyperspectral images. In this paper, we propose an ensemble framework, which combines spectral and spatial information in different scales. The motivation of the proposed method derives from the basic idea: by integrating many individual learners, ensemble learning can achieve better generalization ability than a single learner. In the proposed work, the individual learners are obtained by joint spectral-spatial features generated from different scales. Specially, we develop two techniques to construct the ensemble model, namely, hierarchical guidance filtering (HGF) and matrix of spectral angle distance (mSAD). HGF and mSAD are combined via a weighted ensemble strategy. HGF is a hierarchical edge-preserving filtering operation, which could produce diverse sample sets. Meanwhile, in each hierarchy, a different spatial contextual information is extracted. With the increase of hierarchy, the pixels spectra tend smooth, while the spatial features are enhanced. Based on the outputs of HGF, a series of classifiers can be obtained. Subsequently, we define a low-rank matrix, mSAD, to measure the diversity among training samples in each hierarchy. Finally, an ensemble strategy is proposed using the obtained individual classifiers and mSAD. We term the proposed method as HiFi-We. Experiments are conducted on two popular data sets, Indian Pines and Pavia University, as well as a challenging hyperspectral data set used in 2014 Data Fusion Contest (GRSS_DFC_2014). An effectiveness analysis about the ensemble strategy is also displayed. [ABSTRACT FROM PUBLISHER]

This paper proposes a coastline detection method for polarimetric synthetic aperture radar (SAR) images based on region-based and edge-based active contour models. It can be used to detect coastline accurately and fast. In this method, the region-based and edge-based active contour models are effectively combined by an important property of the likelihood ratio edge detector in polarimetric SAR images, which is proved by theory. Using low-resolution image obtained by multilook processing, we detect accurate and continued coarse coastlines by a region-based level set method. The property of the likelihood ratio edge detector of polarimetric SAR images along the coastline region is then analyzed. The coarse detection result is finally refined using a fast snake active contour model based on the edge property. Polarimetric SAR data acquired by RADARSAT-2 over a Singapore region and TerraSAR-X over a Berkeley region are both used to test the proposed algorithm. The experimental results show that the coastline is fast and accurately detected in different initial scales. The proposed method greatly reduces the data processing time compared with the coastline detection method based on a single scale. [ABSTRACT FROM PUBLISHER]

The classification of pixels in remotely sensed imagery (RSI) into land cover classes typically requires knowing the labels of some image pixels for model training. However, accurate pixel-level label information is usually difficult and expensive to acquire, which restricts the applicability of supervised image classification methods. In contrast, the region labels information that specifies which classes are contained in a region of the image that is easier to acquire and less susceptible to identification errors. To utilize the region label information for remotely sensed image classification, this paper presents a weakly supervised image classification approach using label constraint and edge penalty (ILCEP), which has the following key characteristics. First, the predefined region labels are used as constraints in ILCEP to guide the inference of pixel labels in the image. Second, the edges between neighboring pixels are used as penalties to address the spatial contextual information in the image. Third, the label constraint and edge penalty are incorporated into the conditional random field framework, and simultaneous model learning and label inference are achieved by solving the maximum a posteriori problem through an enhanced simulated annealing algorithm. Experiments on both simulated and real RSIs demonstrate that the proposed approach can achieve high classification accuracy by knowing only the region-level label information. [ABSTRACT FROM AUTHOR]

Terrestrial laser scanning has demonstrated increasing potential for rapid comprehensive measurement of forest structure, especially when multiple scans are spatially registered in order to reduce the limitations of occlusion. Although marker-based registration techniques (based on retro-reflective spherical targets) are commonly used in practice, a blind marker-free approach is preferable, insofar as it supports rapid operational data acquisition. To support these efforts, we extend the pairwise registration approach of our earlier work, and develop a graph-theoretical framework to perform blind marker-free global registration of multiple point cloud data sets. Pairwise pose estimates are weighted based on their estimated error, in order to overcome pose conflict while exploiting redundant information and improving precision. The proposed approach was tested for eight diverse New England forest sites, with 25 scans collected at each site. Quantitative assessment was provided via a novel embedded confidence metric, with a mean estimated root-mean-square error of 7.2 cm and 89% of scans connected to the reference node. This paper assesses the validity of the embedded multiview registration confidence metric and evaluates the performance of the proposed registration algorithm. [ABSTRACT FROM PUBLISHER]

In this paper, a framework for multiscale and multifeature normalized cut (MMNCut) segmentation is proposed for high spatial resolution (HSR) remote sensing images. Normalized cuts (NCuts), as a widely used segmentation method for natural images, can obtain a globally optimized segmentation result corresponding to the optimized partitions of a graph. However, it is difficult to apply the traditional NCuts directly to HSR images because of the huge computational complexity and the diversity of the characteristics of the land covers. In order to solve these problems, the proposed MMNCuts builds a multiscale graph based on superpixels, which can provide powerful grouping cues to guide the segmentation. Generated by different algorithms with varying parameters, superpixels can capture diverse and multiscale visual patterns of HSR images. In addition, the newly constructed graph integrates the multiscale information by considering various connection relationships. Meanwhile, the successful integration of the multifeature cues, including the spectral information, texture information, and structure information, from a large number of superpixels, helps to enhance the expression ability of the graph. Computationally, this leads to a much more efficient algorithm than the traditional NCuts, and in effect, the proposed method achieves a significantly better performance than the traditional approaches. The experimental results with three HSR image data sets demonstrate that the proposed MMNCut algorithm shows a competitive performance in both qualitative and quantitative evaluations when compared with the other state-of-the-art segmentation algorithms for HSR images. [ABSTRACT FROM AUTHOR]

High-resolution (HR) images are always preferred for many remote sensing applications, which can be obtained from their low-resolution (LR) counterparts via a technique referred to as super-resolution (SR). Among SR approaches, single image SR (SISR) methods aim at reconstructing the HR image from only one LR image. In this paper, a residual dense backprojection network (RDBPN)-based SISR method is proposed to promote the resolution of RGB remote sensing images with median- and large-scale factors. The proposed network consists of several residual dense backprojection blocks that contain two kinds of modules, named the upprojection module and the downprojection module, and these modules are densely connected in one block. Different from the chain-connected backprojection structure, the proposed method applies a residual backprojection block structure, which can utilize residual learning in both global and local manners. We further simplify the network by replacing the downprojection unit with the downscaling unit to accelerate the speed of reconstruction, and this implementation is called fast RDBPN (FRDBPN). Several experiments under the UC Merced data set are conducted to validate the effectiveness of the proposed method, and the results indicate that: 1) the proposed residual block structure is superior to the chain-connected structure; 2) FRDBPN achieves a speedup of about 1.3 times with similar and even better-reconstructed performance in comparison with RDBPN; and 3) RDBPN and FRDBPN outperform several state-of-the-art methods in terms of both quantitative evaluation and visual quality. [ABSTRACT FROM AUTHOR]

This paper investigates the problems of outliers and/or noise in surface segmentation and proposes a statistically robust segmentation algorithm for laser scanning 3-D point cloud data. Principal component analysis (PCA)-based local saliency features, e.g., normal and curvature, have been frequently used in many ways for point cloud segmentation. However, PCA is sensitive to outliers; saliency features from PCA are nonrobust and inaccurate in the presence of outliers; consequently, segmentation results can be erroneous and unreliable. As a remedy, robust techniques, e.g., RANdom SAmple Consensus (RANSAC), and/or robust versions of PCA (RPCA) have been proposed. However, RANSAC is influenced by the well-known swamping effect, and RPCA methods are computationally intensive for point cloud processing. We propose a region growing based robust segmentation algorithm that uses a recently introduced maximum consistency with minimum distance based robust diagnostic PCA (RDPCA) approach to get robust saliency features. Experiments using synthetic and laser scanning data sets show that the RDPCA-based method has an intrinsic ability to deal with outlier- and/or noise-contaminated data. Results for a synthetic data set show that RDPCA is 105 times faster than RPCA and gives more accurate and robust results when compared with other segmentation methods. Compared with RANSAC and RPCA based methods, RDPCA takes almost the same time as RANSAC, but RANSAC results are markedly worse than RPCA and RDPCA results. Coupled with a segment merging algorithm, the proposed method is efficient for huge volumes of point cloud data consisting of complex objects surfaces from mobile, terrestrial, and aerial laser scanning systems. [ABSTRACT FROM PUBLISHER]

To obtain accurate classification results of hyperspectral images, both spectral and spatial information should be fully exploited in the classification process. In this paper, we propose a novel method using independent component analysis (ICA) and edge-preserving filtering (EPF) via an ensemble strategy for the classification of hyperspectral data. First, several subsets are randomly selected from the original feature space. Second, ICA is used to extract spectrally independent components followed by an effective EPF method, to produce spatial features. Two strategies (i.e., parallel and concatenated) are presented to include the spatial features in the analysis. The spectral–spatial features are then classified with a random forest or a rotation forest classifier. Experimental results on two real hyperspectral data sets demonstrate the effectiveness of the proposed methods. A sensitivity analysis of the new classifiers is also performed. [ABSTRACT FROM PUBLISHER]

This paper investigates the problem of change detection in multitemporal synthetic aperture radar (SAR) images. Our motivation is to avoid using a large-size dense neighborhood around each pixel to measure its change level, which is usually considered by classical methods in order to perform their accurate detectors. Therefore, we propose to develop a pointwise approach to detect land-cover changes between two SAR images employing the principle of signal processing on graphs. First, a set of characteristic points is extracted from one of the two images to capture the image's significant contextual information. A weighted graph is then constructed to encode the interaction among these keypoints and hence capture the local geometric structure of this first image. With regard to this graph, the coherence of the information carried by the two images is considered for measuring changes between them. In other words, the change level will depend on how much the second image still conforms to the graph structure constructed from the first image. Additionally, due to the presence of speckle noise in SAR imaging, the log-ratio operator will be exploited to perform the image comparison measure. Experimental results performed on real SAR images show the effectiveness of the proposed algorithm, in terms of detection performance and computational complexity, compared to classical methods. [ABSTRACT FROM PUBLISHER]

In this paper, we propose an automated detection algorithm for well- and ill-shaped ground-penetrating radar reflection hyperbolas for complex media, calibrated with human recognition principles. The algorithm detects the apex of the hyperbolas by fitting an analytical function of a hyperbola to the profile edge dots detected with a Canny filter. The existence of a hyperbola is determined using a set of carefully chosen criteria calibrated in order to fit the ambiguity zone for the human brain. The inherent misshapedness of field hyperbolas is further considered by defining a buffer zone around the theoretical hyperbola. First, the method was tested in the laboratory over tree roots and PVC pipes and on field images over tree root systems. Both time- and frequency-domain radars were used on-ground. After around 1–3 min of computation time for 10 000 edge dots in a MATLAB environment (single 1.96-GHz processor), the results showed rates of false alarm and nondetection of maximum 20% and 28%, respectively. In comparison with the semiautomated hyperbola detection provided by a commercial software, these rates were lower. Second, we conducted a sensitivity analysis to estimate the validity of the fitting of a hyperbola equation neglecting the object radius. The fitting was close, but the derivation of the relative permittivity from the analytical equation neglecting the radius led to high errors. In conclusion, owing to the low computational time and its good performances, the proposed algorithm is suitable for complex environments. [ABSTRACT FROM AUTHOR]

This paper describes a linear-image-transform-based algorithm for reducing stripe noise, track line artifacts, and motion-induced errors in remote sensing data. Developed for multibeam bathymetry (MB), the method has also been used for removing scalloping in synthetic aperture radar images. The proposed image transform is the composition of an invertible edge detection operator and a fast discrete Radon transform (DRT) due to Götz, Druckmüller, and Brady. The inverse DRT is computed by using an iterative method and exploiting an approximate inverse algorithm due to Press. The edge operator is implemented by circular convolution with a Laplacian point spread function modified to render the operator invertible. In the transformed image, linear discontinuities appear as high-intensity spots, which may be reset to zero. In MB data, a second noise signature is linked to motion-induced errors. A Chebyshev approximation of the original image is subtracted before applying the transform, and added back to the denoised image; this is necessary to avoid boundary effects. It is possible to process data faster and suppress motion-induced noise further by filtering images in nonoverlapping blocks using a matrix representation for the inverse DRT. Processed test images from several MB data sets had less noise and distortion compared with those obtained with standard low-pass filters. Denoising also improved the accuracy in statistical classification of geomorphological type by 10–28% for two sets of invariant terrain features. [ABSTRACT FROM PUBLISHER]

In this paper, we propose an integrated framework of the recently proposed Genetic Sequential Image Segmentation (GeneSIS) algorithm. GeneSIS segments the image in an iterative manner, whereby at each iteration, a single object is extracted via a genetic algorithm-based object extraction method. This module evaluates the fuzzy content of candidate regions, and through an effective fitness function design provides objects with optimal balance between fuzzy coverage, consistency and smoothness. GeneSIS exhibits a number of interesting properties, such as reduced over-/undersegmentation, adaptive search scale, and region-based search. To enhance the capabilities of GeneSIS, we incorporate here several improvements of our initial proposal. On one hand, two modifications are introduced pertaining to the object extraction algorithm. Specifically, we consider a more flexible representation of the structural elements used for the object's extraction. Furthermore, in view of its importance, the consistency criterion is redefined, thus providing a better handling of the ambiguous areas of the image. On the other hand we incorporate three tools properly devised, according to the fuzzy principles characterizing GeneSIS. First, we develop a marker selection strategy that creates reliable markers, particularly when dealing with ambiguous components of the image. Furthermore, using GeneSIS as the essential part, we consider a generalized experimental setup embracing two different classification schemes for remote sensing images: the spectral-spatial classification and the supervised segmentation methods. Finally, exploiting the inherent property of GeneSIS to produce multiple segmentations, we propose a segmentation fusion scheme. The effectiveness of the proposed methodology is validated after thorough experimentation on four data sets. [ABSTRACT FROM PUBLISHER]

This paper proposes a polarimetric homogeneity measurement and applies it to the speckle filtering of polarimetric synthetic aperture radar (PolSAR) data. First, a line-and-edge (LAE) detector that can detect both the lines and edges in one scan is developed based on the traditional edge detector. A polarimetric homogeneity measurement is then derived by combining the equivalent number of looks and the LAE maps and is used to distinguish the homogeneous and heterogeneous regions. Finally, a new adaptive-window PolSAR filtering algorithm based on the LAE detector and the polarimetric homogeneity measurement is proposed. The proposed speckle filter adjusts the filtering windows in both shape and size, based on the homogeneity and gradient information. Consequently, it uses small and nonsquare windows in heterogeneous regions to preserve the detail information and uses large and square windows in homogeneous regions to maximize the suppression of speckle noise. EMISAR and ESAR L-band PolSAR data were used to demonstrate the effectiveness of the proposed filter in speckle suppression, detail preservation, and polarimetric information preservation. [ABSTRACT FROM PUBLISHER]

Efficient detection of targets immersed in a complex background with a low signal-to-clutter ratio (SCR) is very important in infrared search and tracking (IRST) applications. In this paper, we address the target detection problem in terms of local image segmentation and propose a novel small target detection algorithm derived from facet kernel and random walker (RW) algorithm which includes four main stages. First, since the RW algorithm is suitable for images with less noises, local order-statistic and mean filtering are applied to remove the pixel-sized noises with high brightness (PNHB) and smooth the infrared images. Second, the infrared image is filtered by the facet kernel to enhance the target pixels and candidate target pixels are extracted by an adaptive threshold operation. Third, inspired by the properties of infrared targets, a novel local contrast descriptor (NLCD) based on the RW algorithm is proposed to achieve clutter suppression and target enhancement. Then, the candidate target pixels are selected as central pixels to construct the local regions and the NLCD map of all local regions is computed. The obtained NLCD map is weighted by the filtered map of facet kernel to further enhance target. Finally, the target is detected by a thresholding operation on the weighted map. Experimental results on three data sets show that the proposed method outperforms conventional baseline methods in terms of target detection accuracy. [ABSTRACT FROM AUTHOR]

Illumination variation is one of the major problems in multitemporal earth observation (EO) image matching. Despite much research has been focused on illumination invariant image matching, subpixel image matching under large illumination variation without prior knowledge is still a challenge. This paper proposes a phase correlation decomposition (PCD) theory model in order to analyze the joint effects of zenith and azimuth angles of the lighting source. A novel stepwise least-squares fitting-based PC (SLSF-PC) is proposed to accurately calculate the subpixel image shift by a stepwise function in the frequency domain. Our mathematical investigation is validated by image alignment and stereo dense matching experiments using simulated terrain shading images representing four different landscapes and a multi-illumination remotely sensed image data set containing eight different scenes under seasonal and daily illumination variation. Image matching experiments demonstrate the superior performance of the proposed SLSF-PC compared to the state-of-the-art image matching algorithms, such as speeded up robust features (SURF), mutual information (MI), and normalized cross correlation (NCC). Even under great illumination angle change, the proposed SLSF-PC is able to achieve 0.1 subpixel matching accuracy on average while other methods fail to find the correspondence. [ABSTRACT FROM AUTHOR]

Satellite image classification is an important technique used in remote sensing for the computerized analysis and pattern recognition of satellite data, which facilitates the automated interpretation of a large amount of information. Today, there exist many types of classification algorithms, such as parallelepiped and minimum distance classifiers, but it is still necessary to improve their performance in terms of accuracy rate. On the other hand, over the last few decades, cellular automata have been used in remote sensing to implement processes related to simulations. Although there is little previous research of cellular automata related to satellite image classification, they offer many advantages that can improve the results of classical classification algorithms. This paper discusses the development of a new classification algorithm based on cellular automata which not only improves the classification accuracy rate in satellite images by using contextual techniques but also offers a hierarchical classification of pixels divided into levels of membership degree to each class and includes a spatial edge detection method of classes in the satellite image. [ABSTRACT FROM PUBLISHER]

The scale-invariant feature transform (SIFT) algorithm and its many variants are widely used in computer vision and in remote sensing to match features between images or to localize and recognize objects. However, mostly because of speckle noise, it does not perform well on synthetic aperture radar (SAR) images. In this paper, we introduce a SIFT-like algorithm specifically dedicated to SAR imaging, which is named SAR-SIFT. The algorithm includes both the detection of keypoints and the computation of local descriptors. A new gradient definition, yielding an orientation and a magnitude that are robust to speckle noise, is first introduced. It is then used to adapt several steps of the SIFT algorithm to SAR images. We study the improvement brought by this new algorithm, as compared with existing approaches. We present an application of SAR-SIFT to the registration of SAR images in different configurations, particularly with different incidence angles. [ABSTRACT FROM PUBLISHER]

This paper investigates the application of a level set method for the automated multiphase segmentation of multiband and polarimetric synthetic aperture radar (SAR) images. The level set formulation is used to form an energy functional that includes the image statistical information defined on active contours. In addition to the classical Wishart/Gaussian distribution for locating region boundaries, edge information is incorporated into the energy functional to improve the performance of polarimetric data segmentation. An active contour model with an edge indicator is proposed by assuming that the image boundary term follows a Gibbs prior. An empirical parameter setting criterion is developed to ensure that the components of the energy functional are in proper proportion. We then investigate the multiphase extension for energy minimization, and we use a piecewise constant model to embed the proposed active contour model. Synthetic and real multiband polarimetric SAR data are used for verification. The experiments show that our method is superior to another level set method based on the Wishart/Gaussian distribution, in which SAR edge information is not included, particularly for discriminating among low-contrast regions. Furthermore, results also show that segmentation is improved when multiband data are used in the level set framework. [ABSTRACT FROM PUBLISHER]

This paper proposes a novel two-stage method for remote sensing image segmentation. First, initial small segments, also called subobject primitives (sub-OPs), are obtained using edge-constrained watershed segmentation and edge allocation. These segments are gradually merged into a larger segment until the edge-controlled limits are reached, thereby creating the initial OPs. In this stage, a concept of hard-boundary ratio is proposed to control the merge effectively. Second, nonconstrained merging is conducted on the OPs, which results in final segmentation. In addition, a repeatable pairwise segment-merging scheme is utilized. This scheme improves method efficiency and accuracy. Comprehensive experiments comparing this new method with the multiresolution segmentation method of eCognition were conducted. Results show that this new method has the following advantages: higher segmentation accuracy and OP boundary precision and less dependence on the scale parameter. [ABSTRACT FROM PUBLISHER]

With the flourish of the nonlocal mean method, the neighborwise similarity metric is widely applied in speckle reduction for its robust performance on the search of similar samples. In this metric, an isotropic kernel function is usually chosen to aggregate the corresponding pixels' distance between two neighborhoods. It means that the kernel function is considered as the explanation of the local spatial relationship at each pixel. However, for anisotropic features (such as edges and lines), a strong relationship exists along their directions rather than across them, so the isotropic kernel is not suitable to explain the spatial relationship around these features. Meanwhile, due to the inherent speckle in synthetic aperture radar (SAR) images, the discrimination and exploration of the geometrical properties of anisotropic features are important for the construction of adaptive kernel function. In this paper, the sketch map which is a representation of the sketch information of SAR images is extracted as the criterion for designing the kernel function. Meanwhile, due to the properties of symmetric and maximal self-similarity, a modified ratio distance is proposed and used jointly with the constructed kernel function as a similarity metric. Then, under the local stationary assumption, the local maximal homogeneous region of each pixel is searched by using the region growing method with the proposed metric. Moreover, maximal likelihood rule is used within the region for the estimation of true value. From the experiments on the synthetic and real SAR images, a promising performance in terms of speckle reduction and preservation of the details is achieved by our proposed method. [ABSTRACT FROM PUBLISHER]

This paper presents a new method for estimating nearshore wave height from a digital video sequence. The method identifies main wave breaking zones in the video records and estimates the height of breaking waves inside the detected breaking zones. A geometric rectification is applied to the resulting estimation to convert the height measurement from image pixels to meters. The validation of the algorithm was undertaken over three months at Surfers Paradise, Australia. The performance of the algorithm was demonstrated to be comparable with that of buoy-measured wave height, as well as manual estimates of the onshore wave height by a surf reporter. The results indicate that the method can be used as a cost-effective tool for long-term monitoring of nearshore wave conditions. [ABSTRACT FROM PUBLISHER]