This work presents a robust graph mapping approach for the unsupervised heterogeneous change detection problem in remote sensing imagery. To address the challenge that heterogeneous images cannot be directly compared due to different imaging mechanisms, we take advantage of the fact that the heterogeneous images share the same structure information for the same ground object, which is imaging modality-invariant. The proposed method first constructs a robust K-nearest neighbor graph to represent the structure of each image, and then compares the graphs within the same image domain by means of graph mapping to calculate the forward and backward difference images, which can avoid the confusion of heterogeneous data. Finally, it detects the changes through a Markovian co-segmentation model that can fuse the forward and backward difference images in the segmentation process, which can be solved by the co-graph cut. Once the changed areas are detected by the Markovian co-segmentation, they will be propagated back into the graph construction process to reduce the influence of changed neighbors. This iterative framework makes the graph more robust and thus improves the final detection performance. Experimental results on different data sets confirm the effectiveness of the proposed method. Source code of the proposed method is made available at https://github.com/yulisun/IRG-McS.

Change detection of heterogeneous multitemporal satellite images is an important and challenging topic in remote sensing. Since the imaging mechanisms of heterogeneous sensors are different, it is not possible to directly compare heterogeneous images to detect changes as in the homogeneous images. To address this challenge, we propose an unsupervised image regression-based change detection method based on the structure consistency. The proposed method first adaptively constructs a similarity graph to represent the structure of a pre-event image, then uses the graph to translate the pre-event image to the domain of the post-event image, and then computes the difference image. Finally, a superpixel-based Markovian segmentation model is designed to segment the difference image into changed and unchanged classes. The proposed adaptive structure consistency-based image regression model can not only alleviate the impact of noise and changed pixels on the regression process by using the structure-based transformation, but also easily distinguish between changed and unchanged classes in the difference image by using the prior sparse knowledge of changes. Experimental results on six different datasets demonstrate the effectiveness of the proposed method by comparing with some state-of-the-art methods.

This paper provides a new strategy for the heterogeneous change detection (HCD) problem: solving HCD from the perspective of graph signal processing (GSP). We construct a graph to represent the structure of each image, and treat each image as a graph signal defined on the graph. In this way, we convert the HCD into a GSP problem: a comparison of the responses of signals on systems defined on the graphs, which attempts to find structural differences and signal differences due to the changes between heterogeneous images. Firstly, we analyze the GSP for HCD from the vertex domain. We show that once a region has changed, the local structure of image changes, i.e . the connectivity of the vertex containing this region changes. Therefore, we can compare the output signals of the same input graph signal passing through filters defined on the two graphs to detect changes. We analyze the negative effects of changing regions on the change detection results from the viewpoint of signal propagation, and we also design different filters from the vertex domain to explore the high-order neighborhood information hidden in original graphs. Secondly, we analyze the GSP for HCD from the spectral domain. We explore the spectral properties of different images on the same graph, and show that their spectra exhibit commonalities and dissimilarities. Specifically, it is the change that leads to the dissimilarities of their spectra. With the help of graph spectral analysis, we propose a regression model for the HCD, which decomposes the source signal into the regressed signal and changed signal, and constrains the spectral property of the regressed signal. Experiments conducted on seven real data sets show the effectiveness of the vertex domain filtering based and spectral domain analysis based HCD methods. Source code will be made available at https://github.com/yulisun/HCD-GSP.

The issue of marine oil spills has been receiving increasing attention. Polarimetric Synthetic Aperture Radar (PolSAR) is effective in detecting oil spills by distinguishing backscatter differences between seawater and oil slicks. However, traditional polarimetric SAR features struggle to accurately distinguish between oil slicks and oil slick analogues generated by natural phenomena. In addition, using polarimetric features for oil spill detection faces challenges of poor performance in detecting elongated and small-scale oil spills. To address the interference from look-alikes, we introduce a composite polarimetric feature, termed composite polarimetric scattering entropy, which effectively differentiates between oil spills and look-alikes. To refine oil-water boundary segmentation, we propose an oil spill detection framework based on a Multi-Scale Hybrid Feature Fusion Network (MSHFFNet). The proposed method combines composite polarimetric scattering entropy with polarimetric intensity information and employs both square and strip pooling in the hybrid pooling module to capture a larger receptive field. This approach effectively captures information that helps distinguish elongated oil slicks. Additionally, in the feature fusion module, the method efficiently utilizes shallow features that contain detailed information, such as edges and textures, as well as deep features that contain semantic information. This significantly enhances the detection accuracy of small-scale oil slicks. Experiments conducted on Radarsat-2 data demonstrate that our method exhibits outstanding performance in reducing similar interferences and improving boundary accuracy, surpassing four state-of-the-art methods in terms of detection accuracy.

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.