High-resolution remote sensing image change detection (CD) is one of the main methods to analyze land surface changes. How to effectively distinguish interesting changes and pseudochanges in high-resolution remote sensing images and form accurate and robust CD results is crucial. To deal with these problems, in this letter, we propose a deep-supervised dual discriminative metric network (SDMNet) trained end-to-end for CD in high-resolution bitemporal remote sensing images. A discriminative decoder network is designed in SDMNet to aggregate global and multiscale contextual information, utilizing high stage features to guide the selection of low stage features stage-by-stage to obtain more consistent and robust features. A discriminative implicit metric module is designed in SDMNet to measure the distance between features to detect changes and utilize batch-balanced contrastive loss (BCL) to enlarge the distance difference between unchanged pairs and changed pairs, while alleviate the problem of sample imbalance, and multiple change graph losses are introduced in the intermediate layer of the network for deep supervision. Quantitative evaluation of our method on the CD datasets SYSU, DSIFN, and CLCD demonstrates that the proposed method can provide superior performance than the other state-of-the-art methods.

Remote sensing change detection has always been one of the research hot issues in remote sensing. Current research focuses on studying deep learning change detection methods for remote sensing images with the same resolution. With the prevalence of multi-resolution remote sensing images, how to effectively utilize remote sensing images with different resolutions for change detection is a key issue. To solve this problem, this paper proposes an enhanced super-resolution-based dual-path metric change detection network (ESR-DMNet) to realize high-accuracy and high-efficiency end-to-end change detection of remote sensing images with different resolutions. ESR-DMNet provides a new enhanced super-resolution module for the change detection of remote sensing images with different resolutions, which can perceptively reconstruct low-resolution images into more realistic high-resolution images. ESR-DMNet proposes an effective and efficient dual-path metric change detection network, which processes shallow spatial details information and deep semantic information separately to achieve high-accuracy and high-efficiency change detection. Compared with nine state-of-the-art methods, our method shows good performance at three resolutions on three datasets, SYSU, CDD and CLCD, confirming its potential for change detection tasks in remote sensing images with different resolutions.

As the number of cross-sensor images increases continuously, the surface reflectance of these images is inconsistent at the same ground objects due to different revisit periods and swaths. The surface reflectance consistency between cross-sensor images determines the accuracy of change detection, classification, and land surface parameter inversion, which is the most widespread application. We proposed a relative radiometric normalization (RRN) method to improve the surface reflectance consistency based on the change detection and chi-square test. The main contribution was that a novel chi-square test automatically extracts the stably unchanged samples between the reference and subject images from the unchanged regions detected by the change-detection method. We used the cross-senor optical images of Gaofen-1 and Gaofen-2 to test this method and four metrics to quantitatively evaluate the RRN performance, including the Root Mean Square Error (RMSE), spectral angle cosine, structural similarity, and CIEDE2000 color difference. Four metrics demonstrate the effectiveness of our proposed RRN method, especially the reduced percentage of RMSE after normalization was more than 80%. Comparing the radiometric differences of five ground features, the surface reflectance curve of two Gaofen images showed more minor differences after normalization, and the RMSE was smaller than 50 with the reduced percentages of about 50–80%. Moreover, the unchanged feature regions are detected by the change-detection method from the bitemporal Sentinel-2 images, which can be used for RRN without detecting changes in subject images. In addition, extracting samples with the chi-square test can effectively improve the surface reflectance consistency.

Large-scale land subsidence has threatened the safety of the Hebei Plain in China. For tens of thousands of square kilometers of the Hebei Plain, large-scale subsidence monitoring is still one of the most difficult problems to be solved. In this paper, we employed the small baseline subset (SBAS) and NSBAS technique to monitor the land subsidence in the Hebei Plain (45,000 km²). The 166 Sentinel-1A data of adjacent-track 40 and 142 collected from May 2017 to May 2019 were used to generate the average deformation velocity and deformation time-series. A novel data fusion flow for the generation of land subsidence velocity of adjacent-track is presented and tested, named as the fusion of time-series interferometric synthetic aperture radar (TS-InSAR) results of adjacent-track using synthetic aperture radar amplitude images (FTASA). A cross-comparison analysis between the two tracks results and two TS-InSAR results was carried out. In addition, the deformation results were validated by leveling measurements and benchmarks on bedrock results, reaching a precision 9 mm/year. Twenty-six typical subsidence bowls were identified in Handan, Xingtai, Shijiazhuang, Hengshui, Cangzhou, and Baoding. An average annual subsidence velocity over −79 mm/year was observed in Gaoyang County of Baoding City. Through the cause analysis of the typical subsidence bowls, the results showed that the shallow and deep groundwater funnels, three different land use types over the building construction, industrial area, and dense residential area, and faults had high spatial correlation related to land subsidence bowls.