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With the rapid increase of remote sensing images in temporal, spectral, and spatial resolutions, it is urgent to develop effective techniques for joint interpretation of spatial-temporal images. Multitype change detection (CD) is a significant research topic in multitemporal remote sensing image analysis, and its core is to effectively measure the difference degree and represent the difference among the multitemporal images. In this paper, we propose a novel difference representation learning (DRL) network and present an unsupervised learning framework for multitype CD task. Deep neural networks work well in representation learning but rely too much on labeled data, while clustering is a widely used classification technique free from supervision. However, the distribution of real remote sensing data is often not very friendly for clustering. To better highlight the changes and distinguish different types of changes, we combine difference measurement, DRL, and unsupervised clustering into a unified model, which can be driven to learn Gaussian-distributed and discriminative difference representations for nonchange and different types of changes. Furthermore, the proposed model is extended into an iterative framework to imitate the bottom-up aggregative clustering procedure, in which similar change types are gradually merged into the same classes. At the same time, the training samples are updated and reused to ensure that it converges to a stable solution. The experimental studies on four pairs of multispectral data sets demonstrate the effectiveness and superiority of the proposed model on multitype CD. [ABSTRACT FROM AUTHOR]

Sustainable development in metropolitan regions is challenging in the light of continuous urbanization. Remote sensing provides timely and reliable information on urban areas and their changing patterns. This study's objectives are to evaluate the contribution of Sentinel-2A (S-2A) data to urban ecosystem service mapping and to investigate spatial ecosystem service characteristics with landscape metrics through a novel method. Service pattern changes between 2005 and 2015 are mapped for Beijing, China. Landscape metrics are used to qualitatively evaluate urban ecosystem service provision bundle changes. S-2A and Landsat TM data are segmented and classified with SVM, distinguishing three artificial and four natural classes based on ecosystem function. Spatial characteristics influencing ecosystem services are quantified with seven landscape metrics. Beijing's urban development is characterized by reduction in agricultural areas in the urban fringe in favor of built-up areas, urban green space, and golf courses. A transformation of old suburban agglomerations into urban green space can be observed. The planar increase in urban areas is accompanied by the creation of managed urban green space. Service bundles based on land cover classes and spatial characteristics decreased more than 30% for bundles that represent food supply, noise reduction, waste treatment, and global climate regulation. Temperature regulation/moderation of climate extremes, recreation/place values/social cohesion, and aesthetic benefits/cognitive development are least affected. This new approach of extending the ecosystem service concept through integration of spatial characteristics of ecosystem service provisional patches through landscape metrics is believed to give a more realistic appraisal of ecosystem services in urban areas. [ABSTRACT FROM PUBLISHER]

This paper proposes to integrate two different distances to measure the dissimilarity between neighboring pixels in PolSAR images, and introduces the entropy rate method into PolSAR image superpixel segmentation. Since the Gaussian model is commonly used for homogeneous scenes and less suitable for heterogeneous scenes, we adopt the spherically invariant random vector (SIRV) model to describe the back-scattering characteristics in heterogeneous areas. Moreover, a directional span-driven adaptive (DSDA) region is proposed such that it contains independent and identically distributed samples only, thus it can obtain accurate estimation of the distribution parameters. Using the DSDA region, the Wishart distance and SIRV distance are calculated, and then combined together through a homogeneity measurement. Therefore, the integrated distance takes advantage of the SIRV model and the Gaussian model, and suits both homogeneous and heterogeneous areas. Finally, based on the integrated distance, the superpixel segments are generated using the entropy rate framework. The experimental results on ESAR and PiSAR L-band datasets show that the proposed method can generate homogeneity-adaptive segments, resulting in smooth representation of the land covers in homogeneous areas, and better preserved details in heterogeneous areas. [ABSTRACT FROM PUBLISHER]

Simple linear iterative clustering (SLIC) algorithm was proposed for superpixel generation on optical images and showed promising performance. Several studies have been proposed to modify SLIC to make it applicable for polarimetric synthetic aperture radar (PolSAR) images, where the Wishart distance is adopted as the similarity measure. However, the superpixel segmentation results of these methods were not satisfactory in heterogeneous urban areas. Further, it is difficult to determine the tradeoff factor which controls the relative weight between polarimetric similarity and spatial proximity. In this research, an adaptive polarimetric SLIC (Pol-ASLIC) superpixel generation method is proposed to overcome these limitations. First, the spherically invariant random vector (SIRV) product model is adopted to estimate the normalized covariance matrix and texture for each pixel. A new edge detector is then utilized to extract PolSAR image edges for the initialization of central seeds. In the local iterative clustering, multiple cues including polarimetric, texture, and spatial information are considered to define the similarity measure. Moreover, a polarimetric homogeneity measurement is used to automatically determine the tradeoff factor, which can vary from homogeneous areas to heterogeneous areas. Finally, the SLIC superpixel generation scheme is applied to the airborne Experimental SAR and PiSAR L-band PolSAR data to demonstrate the effectiveness of this proposed superpixel generation approach. This proposed algorithm produces compact superpixels which can well adhere to image boundaries in both natural and urban areas. The detail information in heterogeneous areas can be well preserved. [ABSTRACT FROM PUBLISHER]

This study aims at providing a new method to efficiently analyze detailed urban ecological conditions at the example of Shanghai, one of the world's most densely populated megacities. The main objective is to develop a method to effectively analyze high-resolution optical satellite data for mapping of ecologically important urban space and to evaluate ecological changes through the emerging ecosystem service supply and demand concept. Two IKONOS and GeoEye-1 scenes were used to determine land use/land cover change in Shanghai's urban core from 2000 to 2009. After preprocessing, the images were segmented and classified into seven distinct urban land use/land cover classes through SVM. The classes were then transformed into ecosystem service supply and demand budgets for regulating, provisioning and cultural services, and ecological integrity based on ecosystem functions. Decreases in continuous urban fabric and industrial areas in the favor of urban green sites and high-rise areas with commercial/residential function could be observed resulting in an increase of at least 20% in service supply budgets. Main contributor to the change is the decrease in continuous urban fabric and industrial areas. The overall results and outcome of the study strengthen the suggested application of the proposed method for urban ecosystem service budget mapping with hitherto for that purpose unutilized high-resolution data. The insights and results from this study might further contribute to sustainable urban planning, prove common grounds for interurban comparisons, or aid in enhancing ecological intraurban functionality by analyzing the distribution of urban ecospace and lead to improved accessibility and proximity to ecosystem services in urban areas. [ABSTRACT FROM PUBLISHER]

The classic constant false alarm rate edge detector with a rectangle-shaped filter has been proven to be effective and widely used in polarimetric synthetic aperture radar (PolSAR) images. However, in practical use, the assumption of complex Wishart distribution is often not respected, particularly in heterogeneous urban areas. In addition, as a simple smoothing filter, the rectangle-shaped window is often shown to be easy to incur false edge pixels near true edges. Therefore, its performance is limited. To overcome this restriction, we propose a new edge detector for PolSAR images, which utilizes the spherically invariant random vector product model to estimate the normalized covariance matrix for each pixel, and then replace the rectangle-shaped filter with a Gauss-shaped filter. The performance of our proposed methodology is presented and analyzed on two real PolSAR data sets, and the results show that the new edge detector attains better performance than the classic one, particularly for urban areas. [ABSTRACT FROM PUBLISHER]

Detection of man-made targets in urban areas using polarimetric synthetic aperture radar (PolSAR) data has become a promising research area since it has a close relationship with urban planning, rescue service, etc. This paper presents an improved man-made target detection method for PolSAR data based on nonstationarity and asymmetry. Nonstationarity in azimuth direction is already utilized to separate man-made and natural targets in urban areas. However, there are still some drawbacks. Some small man-made targets and roads cannot be effectively detected. In addition, nonstationarity can also occur in some other natural surfaces, such as cropland with Bragg resonance. Therefore, to resolve these problems, we incorporate reflection asymmetry into the azimuth nonstationarity extraction method to improve the man-made target detection accuracy, i.e., removing the natural areas and detecting the small targets. Airborne ESAR data and spaceborne PALSAR data are used to validate the performance of the proposed method. The result obtained by our proposed method shows a 20% higher accuracy than the result based on original nonstationarity extraction method. Natural areas with Bragg resonance are removed. Moreover, most of the buildings and some metallic fences along the road can also be accurately detected. [ABSTRACT FROM PUBLISHER]

Cross-polarized scattering (HV) is not only caused by vegetation but also by rotated dihedrals. In this letter, we use rotated dihedral corner reflectors to form a cross scattering matrix and propose an extended model-based decomposition method for polarimetric synthetic aperture radar (PolSAR) data over urban areas. Unlike other urban decomposition techniques which need to discriminate between urban and natural areas before decomposition, this proposed method is applied directly on the PolSAR image. The building orientation angle is considered in this scattering matrix, making it flexible and adaptive in the decomposition process. This enables the separation of the cross scattering of urban areas from the overall HV component. The cross and helix scattering components are also compared in this study. RADARSAT-2 quad-pol C band and AIRSAR L band data are used to validate the performance of the proposed method. The cross scattering power of oriented buildings is generated, leading to a better decomposition result for urban areas with respect to other urban decomposition techniques. [ABSTRACT FROM PUBLISHER]

This paper presents a reflectance-like coefficient, normalized reflective factor (NRF) to characterize the radiometric attributes of point cloud generated from small footprint light detection and ranging (LiDAR) waveform data. The NRF is defined as a normalized ratio between the energy of emitted laser beam and the peak in return waveform in conjunction with the atmospheric attenuation and observation geometry. Based on the Gaussian parameters of the emitted and return waveforms, NRF is calculated with an empirical atmospheric model and user-defined standard observation geometry. To correct the radiometric measurement of point cloud in multipeak waveform, a semi-physical-based method is adopted to enhance the NRF of point cloud generated from multipeak waveform. Experiments are conducted with small footprint LiDAR waveform data acquired by RIEGL LMS-Q560. A curve-fitting-based approach is applied to decompose LiDAR waveform into three-dimensional (3-D) coordinates of point cloud, and the NRF are calculated using the Gaussian parameters of both emitted and return waveforms. The visualization of the radiometric attributes of point cloud data is carried out over the overlapping areas between different flight strips, it suggests that the NRF over overlapping area is much smooth than the normalized intensity. Quantitative comparison with Hyperion data indicates that the NRF has much higher correlation with surface reflectance than the normalized intensity data. Standard deviations of NRF and the normalized intensity of different land cover patches are analyzed to assess the homogeneity of the radiometric data. It is observed that NRF has less variability than the normalized intensity within the same land cover patches. Point cloud of two sample trees is also selected to assess the performance of the “sub-footprint” effect correction. It is observed that the proposed approach reduced the variability of radiometric attributes over tree canopies with increasing NRF values; which means the “sub-footprint” effect is mitigated. In summary, the proposed NRF can serve as a promising indicator to characterize radiometric attribute of LiDAR point cloud. [ABSTRACT FROM PUBLISHER]

In remote sensing change detection, Markov random field (MRF) has been used successfully to model the prior probability using class-labels dependencies. MRF has played an important role in the detection of complex urban changes using optical images. However, the preservation of details in urban change analysis turns out to be a highly complex task if multitemporal SAR images with their speckle are to be used. Here, the ability of MRF to preserve geometric details and to combat speckle effect at the same time becomes questionable. Blob-region phenomenon and fine structures removal are common consequences of the application of traditional MRF-based change detection algorithm. To overcome these limitations, the iterated conditional modes (ICM) framework for the optimization of the maximum a posteriori (MAP-MRF) criterion function is extended to include a nonlocal probability maximization step. This probability model, which characterizes the relationship between pixels’ class-labels in a nonlocal scale, has the potential to preserve spatial details and to reduce speckle effects. Two multitemporal SAR datasets were used to assess the proposed algorithm. Experimental results using three density functions [i.e., the log normal (LN), generalized Gaussian (GG), and normal distributions (ND)] have demonstrated the efficiency of the proposed approach in terms of detail preservation and noise suppression. Compared with the traditional MRF algorithm, the proposed approach proved to be less-sensitive to the value of the contextual parameter and the chosen density function. The proposed approach has also shown less sensitivity to the quality of the initial change map when compared with the ICM algorithm. [ABSTRACT FROM PUBLISHER]

This letter presents a pixel-based contextual classification algorithm by integrating a multiscale modified Pappas adaptive clustering (mMPAC) and an adaptive Markov random field (AMRF) into the stochastic expectation–maximization process for urban land cover mapping using multitemporal polarimetric synthetic aperture radar (PolSAR) data. This algorithm can effectively explore spatiotemporal contextual information to improve classification accuracy. Using the mMPAC, the problem caused by the class feature variation could be mitigated. Using the AMRF, shape details could be preserved from overaveraging that often occurs in many nonadaptive contextual approaches. Six-date RADARSAT-2 PolSAR data over the Greater Toronto Area were used for evaluation. The results show that this algorithm outperformed the support vector machine in producing homogeneous and detailed land cover classification in a complex urban environment with high accuracy. [ABSTRACT FROM PUBLISHER]

Multitemporal synthetic aperture radar (SAR) images have been increasingly used in change detection studies. However, the presence of speckle is the main disadvantage of this type of data. To reduce speckle, many local adaptive filters have been developed. Although these filters are effective in reducing speckle in homogeneous areas, their use is often accompanied with the degradation of spatial details and fine structures. In this paper, we investigate a nonlocal means (NLM) denoising algorithm that combines local structures with a global averaging scheme in the context of change detection using multitemporal SAR images. First, the ratio image is logarithmically scaled to convert the multiplicative noise model to an additive model. A multidimensional change image is then constructed using image neighborhood feature vectors. Principle component analysis is then used to reduce the dimensionality of the neighborhood feature vectors. Recursive linear regression combined with fitting-accuracy assessment strategy is developed to determine the number of significant PC components to be retained for similarity weight computation. An intuitive method to estimate the unknown noise variance (necessary to run the NLM algorithm) based on the discarded PC components is also proposed. The efficiency of the method has been assessed using two different bitemporal SAR datasets acquired in Beijing and Shanghai, respectively. For comparison purposes, the algorithm is also tested against some of the most commonly used local adaptive filters. Qualitative and quantitative analyses of the algorithm have demonstrated the efficiency of the algorithm in recovering the noise-free change image while preserving the complex structures in urban areas. [ABSTRACT FROM PUBLISHER]

The objectives of this research are to develop robust methods for segmentation of multitemporal synthetic aperture radar (SAR) and optical data and to investigate the fusion of multitemporal ENVISAT advanced synthetic aperture radar (ASAR) and Chinese HJ-1B multispectral data for detailed urban land-cover mapping. Eight-date multiangle ENVISAT ASAR images and one-date HJ-1B charge-coupled device image acquired over Beijing in 2009 are selected for this research. The edge-aware region growing and merging (EARGM) algorithm is developed for segmentation of SAR and optical data. Edge detection using a Sobel filter is applied on SAR and optical data individually, and a majority voting approach is used to integrate all edge images. The edges are then used in a segmentation process to ensure that segments do not grow over edges. The segmentation is influenced by minimum and maximum segment sizes as well as the two homogeneity criteria, namely, a measure of color and a measure of texture. The classification is performed using support vector machines. The results show that our EARGM algorithm produces better segmentation than eCognition, particularly for built-up classes and linear features. The best classification result (80%) is achieved using the fusion of eight-date ENVISAT ASAR and HJ-1B data. This represents 5%, 11%, and 14% improvements over eCognition, HJ-1B, and ASAR classifications, respectively. The second best classification is achieved using fusion of four-date ENVISAT ASAR and HJ-1B data (78%). The result indicates that fewer multitemporal SAR images can achieve similar classification accuracy if multitemporal multiangle dual-look-direction SAR data are carefully selected. [ABSTRACT FROM PUBLISHER]

The objective of this research is to examine effective methods for urban change detection using multitemporal spaceborne SAR data in two rapid expanding cities in China. One scene of ERS-2 SAR C-VV image was acquired in Beijing in 1998 and in shanghai in 1999 respectively and one scene of ENVISAT ASAR C-VV image was acquired in near-anniversary dates in 2008 in Beijing and Shanghai. To compare the SAR images from different dates, a modified ratio operator that takes into account both positive and negative changes was developed to derive a change image. A generalized version of Kittler-Illingworth minimum-error thresholding algorithm was then tested to automatically classify the change image into two classes, change and no change. Various probability density functions such as Log normal, Generalized Gaussian, Nakagami ratio, and Weibull ratio were investigated to model the distribution of the change and no change classes. The results showed that Kittler-Illingworth algorithm applied to the modified ratio image is very effective in detecting temporal changes in urban areas using SAR images. Log normal and Nakagami density models achieved the best results. The Kappa coefficients of these methods were of 0.82 and 0.71 for Beijing and Shanghai respectively while the false alarm rates were 2.7% and 4.75%. The findings indicated that the change accuracies obtained using Kittler-Illingworth algorithm vary depending on how the assumed conditional class density function fits the histograms of change and no change classes. [ABSTRACT FROM PUBLISHER]

This paper presents a semi-supervised Stochastic Expectation-Maximization (SEM) algorithm for detailed urban land cover mapping using multitemporal high-resolution polarimetric SAR (PolSAR) data. By applying an adaptive Markov Random Field (MRF) with the spatially variant Finite Mixture Model (SVFMM), spatial-temporal contextual information could be effectively explored to improve the mapping accuracy with homogenous results and preserved shape details. Further, a learning control scheme was proposed to ensure a robust semi-supervised mapping process thus avoiding the undesired class merges. Four-date RADARSAT-2 polarimetric SAR data over the Greater Toronto Area were used to evaluate the proposed method. Common PolSAR distribution models such as Wishart, G0p, Kp and KummerU were compared through this contextual SEM algorithm for detailed urban land cover mapping. Comparisons with Support Vector Machine (SVM) were also conducted to assess the potential of our parametric approach. The results show that the Kp, G0p and KummerU models could generate better urban land cover mapping results than the Wishart model and SVM. [ABSTRACT FROM PUBLISHER]

This letter introduces a new approach for light detection and ranging (LiDAR) waveform decomposition. First, inflection points are identified by the Ramer–Douglas–Peucker curve-fitting algorithm, and each inflection point has a corresponding baseline during curve fitting. Second, according to the spatial relation between the baseline and the inflection point, peaks are selected from the inflection points. The distance between each peak and its baseline and the maximum number of peaks are employed as a criterion to select a “significant” peak. Initial parameters such as width and boundaries of peaks provide restraints for the decomposition; right and left boundaries are estimated via a conditional search. Each peak is fitted by a Gaussian function separately, and other parts of the waveform are fitted as line segments. Experiments are implemented on waveforms acquired by both small-footprint LiDAR system LMS-Q560 and large-footprint LiDAR system Laser Vegetation Imaging Sensor. The results indicate that the algorithm could provide an optimal solution for LiDAR waveform decomposition. [ABSTRACT FROM PUBLISHER]

The papers in this special issue were presented at the 8th InternationalWorkshop on the Analysis of Multitemporal Remote Sensing Images (Multi-Temp 2015) and 2nd EARSeL InternationalWorkshop on Temporal Analysis of Satellite Images (Temporal Analysis 2015). [ABSTRACT FROM PUBLISHER]