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Recent advances in remote sensing have shown great potential for different kind of applications like vegetation and crop supervision. Using hyperspectral images (HSI), this process can be performed over large areas of land, allowing for a fast and non-invasive analysis of variables such as water stress, diseases, type of crops, among many others. In this context, representative spatial-spectral features are crucial to develop the data classification process of hyperspectral remote sensing images. In this way, the use of deep learning networks has shown a remarkable classification performance in many applications as the processing of hyperspectral images. In this paper, a data classification model that use kernel PCA (K-PCA) and inception network architecture to generate deep spatial-spectral features is proposed. The features generated are used to label the different kind of classes into the HSI. The labeling process uses a logistic regression (LR) algorithm. This model was validated using six different datasets. The experiments performed shown that the proposed strategy allows improving the data classification performance regarding the results obtained by the traditional stacked layer model of convolutional neural networks (CNN). Quantitatively speaking, the overall accuracy over all experiments of the proposed model for all tested datasets is greater than 92%. [ABSTRACT FROM AUTHOR]

Snapshot compressive imaging (SCI) refers to compressive imaging systems where multiple frames are mapped into a single measurement, with video compressive imaging and hyperspectral compressive imaging as two representative applications. Though exciting results of high-speed videos and hyperspectral images have been demonstrated, the poor reconstruction quality precludes SCI from wide applications. This paper aims to boost the reconstruction quality of SCI via exploiting the high-dimensional structure in the desired signal. We build a joint model to integrate the nonlocal self-similarity of video/hyperspectral frames and the rank minimization approach with the SCI sensing process. Following this, an alternating minimization algorithm is developed to solve this non-convex problem. We further investigate the special structure of the sampling process in SCI to tackle the computational workload and memory issues in SCI reconstruction. Both simulation and real data (captured by four different SCI cameras) results demonstrate that our proposed algorithm leads to significant improvements compared with current state-of-the-art algorithms. We hope our results will encourage the researchers and engineers to pursue further in compressive imaging for real applications. [ABSTRACT FROM AUTHOR]

In this paper, we present a nonlinear version of the well-known anomaly detection method referred to as the RX-algorithm. Extending this algorithm to a feature space associated with the original input space via a certain nonlinear mapping function can provide a nonlinear version of the RX-algorithm. This non- linear RX-algorithm, referred to as the kernel RX-algorithm, is basically intractable mainly due to the high dimensionality of the feature space produced by the nonlinear mapping function. However, in this paper it is shown that the kernel RX-algorithm can easily be implemented by kernelizing the RX-algorithm in the feature space in terms of kernels that implicitly compute dot products in the feature space. Improved performance of the kernel RX-algorithm over the conventional RX-algorithm is shown by testing several hyperspectral imagery for military target and mine detection. [ABSTRACT FROM AUTHOR]

Exploring spatial-spectral data frequently involves classifying hyperspectral images using convolutional neural networks. Due to the high complexity of the data and the scarcity of available training samples, hyperspectral image classification presents significant difficulties. In the context of supervised classification, we find that traditional experimental designs are frequently misused in the spectral-spatial processing context, resulting in unfair or biased performance evaluation, particularly when training and testing samples are selected at random from the same dataset. Under these circumstances, the dependence caused by the overlap between training and testing samples may be artificially increased, in breach of the data independence assumption upheld by supervised learning theory. In order to prevent an unbiased classification result, we present in this paper a controlled strategy designed to minimize the overlap between the samples present in the training and the testing data sets. The proposed controlled sampling strategy ensures a more trustworthy generalization of the CNN model by minimizing the issues present in the random sampling approach, such as the inability to determine whether or not an increase in classification accuracy is due to the spatial information incorporated into a classifier or to an increase in the overlap between training and testing data sets. Experiments performed with a wavelet CNN on different HSIs, namely Indian Pines, Pavia University, and Salinas, ensure the generalization of the data under the assumption that the training and data sets are independent from one another, based on a controlled strategy. Considering the high dimension of the HSI image, as a preprocessing step, the evaluation of the proposed framework is done by PCA and FA methods. [ABSTRACT FROM AUTHOR]

Classification of hyperspectral images usually suffers from high dimensionality and few reference data, which limits the performance of the pixelwise classifiers. The spectral–spatial classifiers, which integrate the spectral data and the spatial information during the classification, perform impressively in terms of the high classification accuracy and the homogeneous appearance of the classification map. In this paper, we propose a new probabilistic framework for spectral–spatial classification (PFSSC), which integrates the spectral data and the spatial information from the probabilistic point of view. Both the spectral data and the spatial information are used to estimate the per-pixel probability, which gives the likelihood that one pixel belongs to one class, respectively. The classification map can then be directly derived from the joint probability. In the proposed framework, a pixelwise probabilistic classifier can be extended as a spectral–spatial one since it can integrate spatial information easily. Furthermore, these spectral–spatial classifiers in the proposed framework are realized in an iterative way to avoid the problem caused by the limited reference data to some extent. In each iterative step, some unassigned pixels are classified by considering the pixels assigned in previous iterative steps. In this iterative process, pixels are assigned to specific labels step by step gradually. In the proposed framework, the probabilistic support vector machine (SVM) and random forest (RF) are extended to be two spectral–spatial classifiers. In short, we denote them as SVM-PFSSC and RF-PFSSC, respectively. The experimental results show that SVM-PFSSC and RF-PFSSC outperform some pixelwise and spectral–spatial classifiers. [ABSTRACT FROM AUTHOR]

In this paper, we propose a novel multiple kernel learning (MKL) framework to incorporate both spectral and spatial features for hyperspectral image classification, which is called multiple-structure-element nonlinear MKL (MultiSE-NMKL). In the proposed framework, multiple structure elements (MultiSEs) are employed to generate extended morphological profiles (EMPs) to present spatial–spectral information. In order to better mine interscale and interstructure similarity among EMPs, a nonlinear MKL (NMKL) is introduced to learn an optimal combined kernel from the predefined linear base kernels. We integrate this NMKL with support vector machines (SVMs) and reduce the min–max problem to a simple minimization problem. The optimal weight for each kernel matrix is then solved by a projection-based gradient descent algorithm. The advantages of using nonlinear combination of base kernels and multiSE-based EMP are that similarity information generated from the nonlinear interaction of different kernels is fully exploited, and the discriminability of the classes of interest is deeply enhanced. Experiments are conducted on three real hyperspectral data sets. The experimental results show that the proposed method achieves better performance for hyperspectral image classification, compared with several state-of-the-art algorithms. The MultiSE EMPs can provide much higher classification accuracy than using a single-SE EMP. [ABSTRACT FROM AUTHOR]

Morphological attribute profiles are among the most prominent spectral–spatial pixel description methods. They are efficient, effective, and highly customizable multiscale tools based on hierarchical representations of a scalar input image. Their application to multivariate images in general and hyperspectral images in particular has been so far conducted using the marginal strategy, i.e., by processing each image band (eventually obtained through a dimension reduction technique) independently. In this paper, we investigate the alternative vector strategy, which consists in processing the available image bands simultaneously. The vector strategy is based on a vector-ordering relation that leads to the computation of a single max and min tree per hyperspectral data set, from which attribute profiles can then be computed as usual. We explore known vector-ordering relations for constructing such max trees and, subsequently, vector attribute profiles and introduce a combination of marginal and vector strategies. We provide an experimental comparison of these approaches in the context of hyperspectral classification with common data sets, where the proposed approach outperforms the widely used marginal strategy. [ABSTRACT FROM AUTHOR]

The high dimensionality of hyperspectral images are usually coupled with limited data available, which degenerates the performances of clustering techniques based only on pixel spectral. To improve the performances of clustering, incorporation of spectral and spatial is needed. As an attempt in this direction, in this paper, we propose an unsupervised co-clustering framework to address both the pixel spectral and spatial constraints, in which the relationship among pixels is formulated using an undirected bipartite graph. The optimal partitions are obtained by spectral clustering on the bipartite graph. Experiments on four hyperspectral data sets are performed to evaluate the effectiveness of the proposed framework. Results also show our method achieves similar or better performance when compared to the other clustering methods. [ABSTRACT FROM AUTHOR]

This paper presents a kernel-based feature selection method for the classification of hyperspectral images. The proposed method aims at selecting a subset of the original features that are both 1) relevant (discriminant) for the considered classification problem, i.e., preserve the functional relationship between input and output variables, and 2) invariant (stable) across different domains, i.e., minimize the data-set shift between the source and the target domains. Domains can be associated with hyperspectral images collected either on different geographical areas or on the same area at different times. We propose a novel measure of data-set shift for evaluating the domain stability, which computes the distance of the conditional distributions between the source and target domains in a reproducing kernel Hilbert space. Such a measure is defined on the basis of the kernel embeddings of the conditional distributions resulting in a nonparametric approach that does not require estimating the distribution of the classes. The adopted search strategy is based on a multiobjective optimization algorithm, which optimizes the two terms of the criterion function for the estimation of the Pareto-optimal solutions. This results in an effective approach of performing feature selection in a transfer learning setting. The experimental results obtained on two hyperspectral images show the effectiveness of the proposed method in selecting features with high generalization capabilities. [ABSTRACT FROM AUTHOR]

Classification of remotely sensed hyperspectral images via supervised approaches is typically affected by high dimensionality of the spectral data and a limited number of labeled samples. Dimensionality reduction via feature extraction and active learning (AL) are two approaches that researchers have investigated independently to deal with these two problems. In this paper, we propose a new method in which the feature extraction and AL steps are combined into a unique framework. The idea is to learn and update a reduced feature space in a supervised way at each iteration of the AL process, thus taking advantage of the increasing labeled information provided by the user. In particular, the computation of the reduced feature space is based on the large-margin nearest neighbor (LMNN) metric learning principle. This strategy is applied in conjunction with $k$-nearest neighbor ( $k$-NN) classification, for which a new sample selection strategy is proposed. The methodology is validated experimentally on four benchmark hyperspectral data sets. Good improvements in terms of classification accuracy and computational time are achieved with respect to the state-of-the-art strategies that do not combine feature extraction and AL. [ABSTRACT FROM PUBLISHER]

An efficient compression of hyperspectral images on-board satellites is mandatory in current and future space missions in order to save bandwidth and storage space. Reducing the data volume in space is a challenge that has been faced with a twofold approach: to propose new highly efficient compression algorithms; and to present technologies and strategies to execute the compression in the hardware available on-board. The Consultative Committee for Space Data Systems (CCSDS), a consortium of the major space agencies in the world, has recently issued the CCSDS 123 standard for multispectral and hyperspectral image (MHI) compression, with the aim of facilitating the inclusion of on-board compression on satellites by the space industry. In this paper, we present a low-complexity feld programmable gate arrays (FPGAs) implementation of this recent CCSDS 123 standard, which demonstrates its main features in terms of compression efficiency and suitability for an implementation on the available on-board technologies. A hardware architecture is conceived and designed with the aim of achieving low hardware occupancy and high performance on a space-qualified FPGA from the Microsemi RTAX family. The resulting FPGA implementation is therefore suitable for on-board compression. The effect of the several CCSDS-123 configuration parameters on the compression efficiency and hardware complexity is taken into consideration to provide flexibility in such a way that the implementation can be adapted to different application scenarios. Synthesis results show a very low occupancy of 34% and a maximum frequency of 43 MHz on a space-qualified RTAX1000S. The benefits of the proposed implementation are further evidenced by a demonstrator, which is implemented on a commercial prototyping board from Xilinx. Finally, a comparison with other FPGA implementations of on-board data compression algorithms is provided. [ABSTRACT FROM PUBLISHER]

With increasing amounts of hyperspectral images (HSI) and the limitations of the memory requirements, compressive techniques for hyperspectral images have attracted extensive research efforts in recent years. The main difficulty of applying compressed sampling (CS) theory to compression and reconstruction of hyperspectral images lies in using the spatial correlation and spectral correlation of hyperspectral images. In this paper, a reconstruction algorithm of hyperspectral images taking advantage of twodimensional compressed sampling (2DCS) and two-dimensional total variation (2DTV) incorporating spectral prediction (SP) is investigated. In the sampling process, the hyperspectral images are divided into reference bands and common bands, and all bands are sampled using 2DCS independently. In the reconstruction process, the reference bands are reconstructed by 2DTV first. In order to improve the reconstruction quality of common bands, spectral prediction utilizing the spectral correlation between reference bands and common bands is conducted. Then the spectral compensation is computed by using a combination of the prediction value and the initial approximation for the common bands. The residual between the compensation value and the original value is obtained to revise the approximation for the common bands. The algorithm is implemented in an iterative manner to enhance the performance. Experimental results on popular hyperspectral datasets reveal that the proposed algorithm exploiting spectral prediction outperforms the algorithm 2DCS-2DTV, which does not use spectral correlation, as well as the state-of-the-art algorithms in terms of peak signal-to-noise ratio (PSNR). In particular, when the sampling rate of the reference bands is higher than that of the common bands, the proposed algorithm would improve the reconstruction quality dramatically. [ABSTRACT FROM AUTHOR]

Hyperspectral images (HSI) contain a great number of bands, which enable better characterization of features. However, the huge dimension and information volume brought by the abundant bands may give rise to a negative influence on the efficiency of subsequent processing on hyperspectral images. Band selection (BS) is a commonly adopted to reduce the dimension of HSIs. Different from the previous work, in this paper, hyperspectral band selection problem is formulated as a multi-objective optimization problem, where the band distribution uniformity among the selected bands and inter-class separation distance from a few labeled samples are optimized simultaneously. To fully exploit the relation between the band subsets with different sizes, we construct a multi-objective evolutionary multi-tasking algorithm for hyperspectral band selection (namely MEMT-HBS) to achieve the selected band subsets for all the selected band sizes in one run. To implement MEMT-HBS, the intra-task pairwise learning based solution generation strategy is suggested to evolve the population for each task to achieve high-quality offspring whose selected band size is restricted to a fixed scope. The inter-task band coverage based knowledge transferring strategy is utilized to choose useful individuals from adjacent tasks to further enhance the performance of current task. Compared with the state-of-the-art semi-supervised and unsupervised BS algorithms, empirical results on different standard hyperspectral datasets show that our proposed MEMT-HBS can determine the superior band subset which has a higher image classification accuracy over the comparison algorithms. [ABSTRACT FROM AUTHOR]

Lactic acid content is a crucial indicator for evaluating maize silage quality, and its accurate detection is essential for ensuring product quality. In this study, a quantitative prediction model for the change of lactic acid content during the secondary fermentation of maize silage was constructed based on a colorimetric sensor array (CSA) combined with hyperspectral imaging. Volatile odor information from maize silage samples with different days of aerobic exposure was obtained using CSA and recorded by a hyperspectral imaging (HSI) system. Subsequently, the acquired spectral data were subjected to preprocessing through five distinct methods before being modeled using partial least squares regression (PLSR). The coronavirus herd immunity optimizer (CHIO) algorithm was introduced to screen three color-sensitive dyes that are more sensitive to changes in lactic acid content of maize silage. To minimize model redundancy, three algorithms, such as competitive adaptive reweighted sampling (CARS), were used to extract the characteristic wavelengths of the three dyes, and the combination of the characteristic wavelengths obtained by each algorithm was used as an input variable to build an analytical model for quantitative prediction of the lactic acid content by support vector regression (SVR). Moreover, two optimization algorithms, namely grid search (GS) and crested porcupine optimizer (CPO), were compared to determine their effectiveness in optimizing the parameters of the SVR model. The results showed that the prediction accuracy of the model can be significantly improved by choosing appropriate pretreatment methods for different color-sensitive dyes. The CARS-CPO-SVR model had better prediction, with a prediction set determination coefficient ( R P 2 ), root mean square error of prediction (RMSEP), and a ratio of performance to deviation (RPD) of 0.9617, 2.0057, and 5.1997, respectively. These comprehensive findings confirm the viability of integrating CSA with hyperspectral imaging to accurately quantify the lactic acid content in silage, providing a scientific and novel method for maize silage quality testing. [ABSTRACT FROM AUTHOR]

Accurate spatial distribution of grassland degradation indicator species is of great significance for grassland degradation monitoring. In order to realize the intelligent remote sensing grassland degradation monitoring task, this paper collects remote sensing data of three degradation indicator species of desert grassland, namely, constructive species, dominant species, and companion species, through the UAV hyperspectral remote sensing platform, and proposes a multi-feature fusion (MFF) classification model. In addition, vertical convolution, horizontal convolution, and group convolution mechanisms are introduced to further reduce the number of model parameters and effectively improve the computational efficiency of the model. The results show that the overall accuracy and kappa coefficient of the model can reach 91.81% and 0.8473, respectively, and it also has better classification performance and computational efficiency compared to different deep learning classification models. This study provides a new method for high-precision and efficient fine classification study of degradation indicator species in grasslands. [ABSTRACT FROM AUTHOR]

With the rapid development of hyperspectral remote sensing technology, the spatial resolution and spectral resolution of hyperspectral images are continually increasing, resulting in a continual increase in the scale of hyperspectral data. At present, hyperspectral lossless compression technology has reached a bottleneck. Simultaneously, the rise of deep learning has provided us with new ideas. Therefore, this paper examines the use of deep learning for the lossless compression of hyperspectral images. In view of the differential pulse code modulation (DPCM) method being insufficient for predicting spectral band information, the proposed method, called C-DPCM-RNN, uses a deep recurrent neural network (RNN) to improve the traditional DPCM method and improve the generalization ability and prediction accuracy of the model. The final experimental result shows that C-DPCM-RNN achieves better compression on a set of calibrated AVIRIS test images provided by the Multispectral and Hyperspectral Data Compression Working Group of the Consultative Committee for Space Data Systems in 2006. C-DPCM-RNN overcomes the limits of traditional methods in its performance on uncalibrated AVIRIS test images. [ABSTRACT FROM AUTHOR]

Edge detection plays an important role in the field of image processing. Various edge detection techniques are obtained like Sobel, PSO Preweitt, Laplacian and Laplacian of Gaussian. These techniques consume some restrictions such as fixed edge thickness and some parameter like threshold is problematic to implement. Although many different edge-detection methods have been proposed for gray-scale, color, and multispectral images, they still face difficulties when extracting edge features from hyperspectral images (HSIs) that contain a large number of bands with very narrow gap in the spectral domain. High performance computing (HPC) is a technique used to assemble process and analyze more quantity of remote sensing (satellite) data which needs less processing time. HPC method is used in distributed & cluster Computing, parallel computing and following satellite data analyzing & processing approaches like geo-referencing, image edge detection, image classification, image mosaicking, Morphological/neural approach and image fusion for hyperspectral satellite data. Stimulated by means of the specific clustering of theory called gravitational theory, an original edge detection process. The conservative methods include the usage of linear time invariant filters. Recognition of edge is done by these filters as a These filters recognize an edge as a sudden conversion of grey scale pixel intensity. These techniques are well recognized and computationally effective. Canny, Sobel, Robert, Kirsch, Prewitt and LOG, on idea of spatial differential filters by using local grade. Within the less time these filters process the data and are inclined to noise. But in fuzzy technique, it does not include like this restriction, by altering the rules and output parameters, it is possible to change the thickness of the edge. In this paper, a new technique is created on fuzzy logic, which is suggested for detection of edge in digital images deprived of finding the threshold value. This method starts through segmenting the images into sections by means of fluctuating 3 × 3 binary matrixes. The pixels of edge are planned to a range of standards separated from each other. The results of this proposed technique is compared with the results obtained from PSO and neural network methods. This suggested technique provides a permanent effect in the lines smoothness and straightness for the straight lines and good roundness for the curved lines. And sharpness in the curves. [ABSTRACT FROM AUTHOR]

Hyperspectral remote image sensing is a rapidly developing integrated technology used widely in numerous areas. The rich spectral information from hyperspectral images aids in recognition and classification of many types of objects, but the high dimensionality of these images leads to information redundancy. In this paper, we used sensitivity analysis for dimension reduction. However, another challenge is that hyperspectral images identify objects as either a "different body with the same spectrum" or "same body with a different spectrum." Therefore, it is difficult to maintain the correct correspondence between ground objects and samples, which hinders classification of the images. This issue can be addressed using multi-instance learning for classification. In our proposed method, we combined neural network sensitivity analysis with a multiinstance learning algorithm based on a support vector machine to achieve dimension reduction and accurate classification for hyperspectral images. Experimental results demonstrated that our method provided strong overall classification effectiveness when compared with prior methods. [ABSTRACT FROM AUTHOR]

The superpixel segmentation has been widely applied in many computer vision and image process applications. In recent years, amount of superpixel segmentation algorithms have been proposed. However, most of the current algorithms are designed for natural images with little noise corrupted. In order to apply the superpixel algorithms to hyperspectral images which are always seriously polluted by noise, we propose a noiseresistant superpixel segmentation (NRSS) algorithm in this paper. In the proposed NRSS, the spectral signatures are first transformed into frequency domain to enhance the noise robustness; then the two widely spectral similarity measures-spectral angle mapper (SAM) and spectral information divergence (SID) are combined to enhance the discriminability of the spectral similarity; finally, the superpixels are generated with the proposed frequency-based spectral similarity. Both qualitative and quantitative experimental results demonstrate the effectiveness of the proposed superpixel segmentation algorithm when dealing with hyperspectral images with various noise levels. Moreover, the proposed NRSS is compared with the most widely used superpixel segmentation algorithm-simple linear iterative clustering (SLIC), where the comparison results prove the superiority of the proposed superpixel segmentation algorithm. [ABSTRACT FROM AUTHOR]

Hyperspectral images provide abundant spectral information of the land surface materials, which make it possible to distinguish those materials with subtle difference. Researches about improvement in classification accuracy of hyperspectral images have been conducted from two aspects. The first one is trying to integrating other features such as texture features, geometry features and geographical information. The second one focuses on the employment of advance classifiers such as random forest classifier, support vector machine classifier, decision tree classifier. This paper demonstrated a recent study about the ensemble classification of hyperspectral image by integrating spectral features and texture features. Morphology texture features were extracted from the principal components of the hyperspectral bands. Multi-size of structure elements was used to get the morphology texture images by implementing the closing and opening operation. Texture features extracted from the gray-level co-occurrence matrix were also utilized to classify the hyperspectral images. Four classifiers were trained using spectral features, texture features, combined spectral and texture features, respectively. It was found that a single feature induced relatively poor classification accuracy in all of the four classifiers. Integrated spectral-texture features generated improved classification results. On the other hand, ensemble classification could produce much better classification effects compared with a single classifier. Further works will focus on the classification performance of other features such as wavelet texture feature and context information. [ABSTRACT FROM AUTHOR]

The availability of hyperspectral images with improved spectral and spatial resolutions provides the opportunity to obtain accurate land-cover classification. In this paper, a novel methodology that combines spectral and spatial information for supervised hyperspectral image classification is proposed. A feature reduction strategy based on independent component analysis is the main core of the spectral analysis, where the exploitation of prior information coupled to the evaluation of the reconstruction error assures the identification of the best class-informative subset of independent components. Reduced attribute profiles (APs), which are designed to address well-known issues related to information redundancy that affect the common morphological APs, are then employed for the modeling and fusion of the contextual information. Four real hyperspectral data sets, which are characterized by different spectral and spatial resolutions with a variety of scene typologies (urban, agriculture areas), have been used for assessing the accuracy and generalization capabilities of the proposed methodology. The obtained results demonstrate the classification effectiveness of the proposed approach in all different scene typologies, with respect to other state-of-the-art techniques. [ABSTRACT FROM PUBLISHER]

In several remote sensing applications, detecting exceptional/irregular regions (i.e, pixels) with respect to the whole dataset homogeneity is regarded as a very interested issue. Currently, this is limited to the pre-processing step aiming to eliminate the cloud or noisy pixels. In this paper, we propose to extend the coverage area and to tackle this issue by regarding the irregular/exceptional pixels as outliers. The main purpose is the adaptation of the class outlier mining concept in order to find abnormal and irregular pixels in hyperspectral images. This should be done taking into account the class labels and the relative uncertainty of collected data. To reach this goal, the Class Outliers: DistanceBased (CODB) algorithm is enhanced to take into account the multivariate high-dimensional data and the concomitant partially available knowledge of our data. This is mainly done by using belief theory and a learnable task-specific similarity measure. To validate our approach, we apply it for vegetation inspection and normality monitoring. For experimental purposes, the Airborne Prism Experiment (APEX) data, set acquired during an APEX flight campaign in June 2011, was used. Moreover, a collection of simulated hyperspectral images and spectral indices, providing a quantitative indicator of vegetation health, were generated for this purpose. The encouraging obtained results can be used to monitor areas where vegetation may be stressed, as a proxy to detect potential drought. [ABSTRACT FROM AUTHOR]

In this paper, a new sparse representation-based binary hypothesis (SRBBH) model for hyperspectral target detection is proposed. The proposed approach relies on the binary hypothesis model of an unknown sample induced by sparse representation. The sample can be sparsely represented by the training samples from the background-only dictionary under the null hypothesis and the training samples from the target and background dictionary under the alternative hypothesis. The sparse vectors in the model can be recovered by a greedy algorithm, and the same sparsity levels are employed for both hypotheses. Thus, the recovery process leads to a competition between the background-only subspace and the target and background subspace, which are directly represented by the different hypotheses. The detection decision can be made by comparing the reconstruction residuals under the different hypotheses. Extensive experiments were carried out on hyperspectral images, which reveal that the SRBBH model shows an outstanding detection performance. [ABSTRACT FROM PUBLISHER]

This paper presents a thorough study on the performances of different independent component analysis (ICA) algorithms for the extraction of class-discriminant information in remote sensing hyperspectral image classification. The study considers the three implementations of ICA that are most widely used in signal processing, namely Infomax, FastICA, and JADE. The analysis aims to address a number of important issues regarding the use of ICA in the RS domain. Three scenarios are considered and the performances of the ICA algorithms are evaluated and compared against each other, in order to reach the final goal of identifying the most suitable approach to the analysis of hyperspectral images in supervised classification. Different feature extraction and selection techniques are used for dimensionality reduction with ICA and are then compared to the commonly used strategy, which is based on preprocessing data with principal components analysis (PCA) prior to classification. Experimental results obtained on three real hyperspectral data sets from each of the considered algorithms are presented and analyzed in terms of both classification accuracies and computational time. [ABSTRACT FROM PUBLISHER]

Linear spectral unmixing is a widely used technique in hyperspectral remote sensing to quantify materials present in an image pixel. In order to produce accurate estimates of abundances, nonnegativity constraint and sum-to-one constraint must be imposed on the abundances of materials. Under these two constraints, linear spectral unmixing is often formulated as a convex optimization problem that requires more advanced optimization technology, leading to excessive computational complexity. In this paper, a novel geometric method is presented for solving the fully constrained linear spectral unmixing problem. Specifically, abundances are first expressed as the ratios of signed volumes of simplexes. Then, Laplace expansion is applied in the process of determinant calculation, which derives a new low-complexity abundance estimation method. Furthermore, the mixed pixel outside the simplex is iteratively projected onto the facet planes through the endmember vertices for making the abundances satisfy the nonnegativity constraint. This process is continued until one finds a projected point lying inside the simplex. The proposed method is in line with the least squares criterion. Experimental results based on simulated and the AVIRIS Cuprite data sets demonstrate the superiority of the proposed algorithm with respect to other state-of-the-art approaches. [ABSTRACT FROM PUBLISHER]

Remote-sensing developments such as UAVs heighten the need for hyperspectral image stitching techniques that can obtain information on a large area through various parts of the same scene. State-of-the-art approaches often suffer from accumulation errors and high computational costs for large-scale hyperspectral remote-sensing images. In this study, we aim to generate high-precision hyperspectral panoramas with less spatial and spectral distortion. We introduce a new stitching strategy and apply it to hyperspectral images. The stitching framework was built as follows: First, a single band obtained by signal-to-noise ratio estimation was chosen as the reference band. Then, a feature-matching method combining the SuperPoint and LAF algorithms was adopted to strengthen the reliability of feature correspondences. Adaptive bundle adjustment was also designed to eliminate misaligned artifact areas and occasional accumulation errors. Lastly, a spectral correction method using covariance correspondences is proposed to ensure spectral consistency. Extensive feature-matching and image-stitching experiments on several hyperspectral datasets demonstrate the superiority of our approach over the state of the art. [ABSTRACT FROM AUTHOR]

Haze contamination is a common issue in optical remote sensing images, including hyperspectral images (HSIs), which can distort the spectral features of land cover objects. Over the last decades, although many haze removal solutions have been developed, very few studies have focused on haze removal of HSIs. Moreover, most of these methods cannot fully explore the abundant spectral information of HSIs in haze removal. To cope with the issues, a data driven method is proposed for haze removal of HSIs in this paper. Specifically, we design a spectral grouping network (SG-Net) to fully utilize the useful information in each spectral band during the reconstruction. To facilitate the relationship construction between hazy image and the corresponding haze-clear image, the proposed SG-Net first groups each HSI into several spectral subsets based on the intra-spectral correlations. Then, these subsets are convoluted in parallel with multiple branches for feature extraction. Furthermore, a novel attention block is designed to connect the adjacent branches for feature transmission, which can distill the useful information (e.g., uncontaminated information in longer wavelength bands) of each subset and assist the reconstruction of HSIs. Comprehensive experiments on both simulated and real haze HSIs showed that SG-Net is more accurate than seven state-of-the-art haze removal methods and is also more robust to different haze levels and shapes. [ABSTRACT FROM AUTHOR]

Hyperspectral images are multidimensional massive sets of information that have shown a great potential for different kind of applications as urban mapping, environmental management, vegetation and crops supervision and mineral detection. However, due to its high dimensional nature and the high variability of the spectral information, the dimensionality reduction process is one of the main challenges in processing hyperspectral images. The aim of dimensionality reduction is to eliminate redundant information and simplify the subsequent processes of classification and the search of information. In this context, several dimensionality reduction methods have been proposed, but most of them are not flexible enough to deal with the particular features of the hyperspectral images. In this way, the use of intelligent methods as neural networks and specially an unsupervised approach as self-organized maps, may improve the dimensionality reduction stage and the final classification process. This paper proposes an unsupervised method for the dimensionality reduction of hyperspectral images based on Kohonen self-organized maps, which, compared with other traditional methods such as principal component analysis (PCA) and wavelet decomposition, provides better classification results. The results provided in this paper use an RBF (radial basis function) classifier. On average, the proposed method provides a 64% dimensionality reduction and an 88.5% classification accuracy. These results suggest that the dimensionality reduction algorithm based on self-organized maps is an efficient approach compared with other popular algorithms. This is due to the ability of self-organized maps to automatically detect (self-organizing) relationships within the set of input patterns, which provides flexibility to deal with the special features of the hyperspectral images.

Due to the high spectral resolution, anomaly detection from hyperspectral images provides a new way to locate potential targets in a scene, especially those targets that are spectrally different from the majority of the data set. Conventional Mahalanobis-distance-based anomaly detection methods depend on the background statistics to construct the anomaly detection metric. One of the main problems with these methods is that the Gaussian distribution assumption of the background may not be reasonable. Furthermore, these methods are also susceptible to contamination of the conventional background covariance matrix by anomaly pixels. This paper proposes a new anomaly detection method by effectively exploiting a robust anomaly degree metric for increasing the separability between anomaly pixels and other background pixels, using discriminative information. First, the manifold feature is used so as to divide the pixels into the potential anomaly part and the potential background part. This procedure is called discriminative information learning. A metric learning method is then performed to obtain the robust anomaly degree measurements. Experiments with three hyperspectral data sets reveal that the proposed method outperforms other current anomaly detection methods. The sensitivity of the method to several important parameters is also investigated. [ABSTRACT FROM PUBLISHER]

This paper proposes a lossless to lossy compression scheme for hyperspectral images based on dual-tree Binary Embedded Zerotree Wavelet (BEZW) algorithm. The algorithm adapts Karhunen-Loève Transform and Discrete Wavelet Transform to achieve 3-D integer reversible hybrid transform and decorrelate spectral and spatial data. Since statistics of the hyperspectral image are not symmetrical, the asymmetrical dual-tree structure is introduced. The 3-D BEZW algorithm compresses hyperspectral images by implementing progressive bitplane coding. The lossless and lossy compression performance is compared with other state-of-the-art predictive coding and transform-based coding algorithms on Airborne Visible/Infrared Imaging Spectrometer images. Results show that the 3-D-BEZW lossless compression performance is comparable with the best predictive algorithms, while its computational cost is comparable with those of transform-based algorithms. [ABSTRACT FROM PUBLISHER]

Band selection is an important preprocessing step for hyperspectral data processing. It involves two crucial problems, i.e., suitable measure criterion and effective search strategy. Mutual information (MI) has been widely used as the measure criterion for its nonlinear and nonparametric characteristics. For efficient calculation, traditional MI-based criteria commonly use bivariate MI (BMI) to approximate the ideal MI-based criterion. However, these BMI-based criteria may miss the bands having discriminative information and do not give the condition of the approximation. In this paper, a novel criterion based on trivariate MI (TMI) is proposed to measure the redundancy for classification. From the multivariate MI perspective, the proposed TMI-based and traditional BMI-based criteria are proved as the low-order approximations of the ideal criterion under some assumptions. Compared with the BMI-based criteria, a more relaxed assumption condition is required for the TMI-based criterion. To alleviate the problem of few labeled samples existing in hyperspectral images, the TMI-based criterion is extended to the semisupervised TMI-based (STMI) method by adding a graph regulation term. Additionally, to search an appropriate band subset by the TMI- and STMI-based criteria, a new clonal selection algorithm (CSA) is proposed. In CSA, integer encoding and adaptive operators are devised to reduce space and time cost. Experimental results demonstrate the effectiveness of the proposed algorithms for hyperspectral band selection. [ABSTRACT FROM PUBLISHER]

Feature extraction is known to be an effective way in both reducing computational complexity and increasing accuracy of hyperspectral image classification. In this paper, a simple yet quite powerful feature extraction method based on image fusion and recursive filtering (IFRF) is proposed. First, the hyperspectral image is partitioned into multiple subsets of adjacent hyperspectral bands. Then, the bands in each subset are fused together by averaging, which is one of the simplest image fusion methods. Finally, the fused bands are processed with transform domain recursive filtering to get the resulting features for classification. Experiments are performed on different hyperspectral images, with the support vector machines (SVMs) serving as the classifier. By using the proposed method, the accuracy of the SVM classifier can be improved significantly. Furthermore, compared with other hyperspectral classification methods, the proposed IFRF method shows outstanding performance in terms of classification accuracy and computational efficiency. [ABSTRACT FROM PUBLISHER]

Hyperspectral remote sensing technology allows one to acquire a sequence of possibly hundreds of contiguous spectral images from ultraviolet to infrared. Conventional spectral classifiers treat hyperspectral images as a list of spectral measurements and do not consider spatial dependences, which leads to a dramatic decrease in classification accuracies. In this paper, a new automatic framework for the classification of hyperspectral images is proposed. The new method is based on combining hidden Markov random field segmentation with support vector machine (SVM) classifier. In order to preserve edges in the final classification map, a gradient step is taken into account. Experiments confirm that the new spectral and spatial classification approach is able to improve results significantly in terms of classification accuracies compared to the standard SVM method and also outperforms other studied methods. [ABSTRACT FROM AUTHOR]

Integrating spatial information into hyperspectral unmixing procedures has been shown to have a positive effect on the estimation of fractional abundances due to the inherent spatial-spectral duality in hyperspectral scenes. However, current research works that take spatial information into account are mainly focused on the linear mixing model. In this paper, we investigate how to incorporate spatial correlation into a nonlinear abundance estimation process. A nonlinear unmixing algorithm operating in reproducing kernel Hilbert spaces, coupled with a l1-type spatial regularization, is derived. Experiment results, with both synthetic and real hyperspectral images, illustrate the effectiveness of the proposed scheme. [ABSTRACT FROM AUTHOR]

Although hyperspectral images acquired by on-board satellites provide information from a wide range of wavelengths in the spectrum, the obtained information is usually highly correlated. This paper proposes a novel framework to reduce the computation cost for large amounts of data based on the efficiency of the optimum-path forest (OPF) classifier and the power of metaheuristic algorithms to solve combinatorial optimizations. Simulations on two public data sets have shown that the proposed framework can indeed improve the effectiveness of the OPF and considerably reduce data storage costs. [ABSTRACT FROM PUBLISHER]

Hyperspectral data analysis has been given a growing attention due to the scientific challenges it raises and the wide set of applications that can benefit from it. Classification of hyperspectral images has been identified as one of the hottest topics in this context, and has been mainly addressed by discriminative methods such as SVM. In this paper, we argue that generative methods, and especially those based on manifold representation of classes in the hyperspectral space, are relevant alternatives to SVM. To illustrate our point, we focus on the recently published PerTurbo algorithm and benchmark against SVM this generative manifold learning algorithm in the context of hyperspectral image classification. This choice is motivated by the fact that PerTurbo is fitted with numerous interesting properties, such as 1) low sensitivity to dimensionality curse, 2) high accuracy in weakly labelled images classification context (few training samples), 3) straightforward extension to on-line setting, and 4) interpretability for the practitioner. The promising results call for an up-to-date interest toward generative algorithms for hyperspectral image classification. [ABSTRACT FROM PUBLISHER]

Computational intelligence is a very active and fruitful research of artificial intelligence with a broad spectrum of applications. Remote sensing data has been a salient field of application of computational intelligence algorithms, both for the exploitation of the data and for the research/ development of new data analysis tools. In this editorial paper we provide the setting of the special issue "Computational Intelligence in Remote Sensing" and an overview of the published papers. The 11 accepted and published papers cover a wide spectrum of applications and computational tools that we try to summarize and put in perspective in this editorial paper. [ABSTRACT FROM AUTHOR]

Analysis of hyperspectral images is of great interest in plant studies. Nowadays, this analysis is used more and more widely, so the development of hyperspectral image processing methods is an urgent task. This paper presents a hyperspectral image processing pipeline that includes: preprocessing, basic statistical analysis, visualization of a multichannel hyperspectral image, and solving classification and clustering problems using machine learning methods. The current version of the package implements the following methods: construction of a confidence interval of an arbitrary level for the difference of sample averages; verification of the similarity of intensity distributions of spectral lines for two sets of hyperspectral images on the basis of the Mann–Whitney U-criterion and Pearson’s criterion of agreement; visualization in two-dimensional space using dimensionality reduction methods PCA, ISOMAP and UMAP; classification using linear or ridge regression, random forest and catboost; clustering of samples using the EM-algorithm. The software pipeline is implemented in Python using the Pandas, NumPy, OpenCV, SciPy, Sklearn, Umap, CatBoost and Plotly libraries. The source code is available at: https://github.com/igor2704/Hyperspectral_images. The pipeline was applied to identify melanin pigment in the shell of barley grains based on hyperspectral data. Visualization based on PCA, UMAP and ISOMAP methods, as well as the use of clustering algorithms, showed that a linear separation of grain samples with and without pigmentation could be performed with high accuracy based on hyperspectral data. The analysis revealed statistically significant differences in the distribution of median intensities for samples of images of grains with and without pigmentation. Thus, it was demonstrated that hyperspectral images can be used to determine the presence or absence of melanin in barley grains with great accuracy. The flexible and convenient tool created in this work will significantly increase the efficiency of hyperspectral image analysis.

Various methods of spectral-spatial classification of hyperspectral data are reviewed. Papers devoted to the most popular ways of using spatial information for increasing the accuracy of classification maps are considered. It is shown that the best results are obtained by using preprocessing of "raw" data before the procedures of pixel-wise spectral classification. Disadvantages, limits, and possible directions for developing existing methods are investigated and analyzed. [ABSTRACT FROM AUTHOR]

Hyperspectral and multispectral imagery allows remote-sensing applications such as the land-cover mapping, which is a significant baseline to understand and to monitor the Earth. Furthermore, it is a relevant process for socio-economic activities. For that reason, high land-classification accuracies are imperative, and minor image processing time is essential. In addition, the process of gathering classes’ documented samples is complicated. This implies that the classification system is required to perform with a limited number of training observations. Another point worth mentioning is that there are hardly any methods that can be used analogously for hyperspectral or multispectral images. This paper aims to propose a novel classification system that can be used for both types of images. The designed classification system is composed of a novel parallel feature extraction algorithm, which utilises a cluster of two graphics processing units in combination with a multicore central processing unit (CPU), and an artificial neural network (ANN) particularly devised for the classification of the features ensued by the implemented feature extraction method. To prove the performance of the proposed classification system, it is compared with non-parallel and CPU-only-parallel implementations employing multispectral and hyperspectral databases. Moreover, experiments with different number of samples for training the classifier are performed. Finally, the proposed ANN is compared with a state-of-the-art support vector machine in classification and processing time results. [ABSTRACT FROM AUTHOR]