As one of the most popular topics in the field of Earth observation using remote sensing images, change detection provides great practical and valuable significance for many fields. Although the majority of supervised methods have made great progress by introducing deep learning in the change detection field, they are still limited by manually labeled data. In comparison, unsupervised methods do not require manually labeled data, but the accuracy of change detection is difficult to be improved due to the lack of constraints or guidance during training. To tackle these issues, we propose a novel domain knowledge-guided self-supervised learning approach (DK-SSCD) for unsupervised change detection by fusing the domain knowledge of remote sensing indices during training and inference. Furthermore, we calculate cosine similarity to select the high-similarity feature vectors outputted by the mean teacher and student networks to implement the hard negative sampling strategy, which effectively improves the change detection performance. Compared with other supervised and unsupervised change detection methods, our proposed approach achieves state-of-the-art performance with a Kap of 53.34% and an F1 of 55.69% on the OSCD dataset. Fusing domain knowledge to guide model training and inference obtains an improvement of 5.83% in Kap and 5.13% in F1, which further narrows the performance gap between unsupervised and supervised change detection.

It is necessary to timely and accurately estimate the surface deformations in mining areas, especially the three-dimensional (3D) deformations during surface movement. At present, nearly all mining-induced 3D deformations retrieved by interferometric synthetic aperture radar (InSAR) pertain to the SAR imaging interval. Research on progressive 3D deformations during surface movement is limited, and the existing approaches are unsatisfactory in practical engineering. Aiming at these challenges, we proposed a novel method for deriving mining-induced 3D surface deformations at any moment by integrating single InSAR interferogram (SII), the Gompertz time function, and the probability integral model (PIM), named the SII-GPIM method. We established an inversion approach for GPIM parameters and derived the mining-induced 3D surface deformations at any moment. Subsequently, we conducted experiments considering two ALOS PALSAR images in the Huaibei mining area. The accuracy of the proposed method was evaluated in subsidence, tilt, curvature, horizontal displacement, and horizontal strain. Compared with existing methods, the SII-GPIM method is state of the art. Additionally, we assessed the building damage, performance of parameter inversion, and method generality. The results demonstrated that the proposed method can accurately determine the mining-induced 3D surface deformations and deformation level at any moment under different geological mining conditions. Moreover, accurate GPIM parameters can be acquired with only two SAR images and traditional measurement is nearly not required. Consequently, the SII-GPIM method owns great value for improving economic efficiency, assessing building damage, and restoring the ecological environment in the mining area.

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.

The elaborate description of passenger travel profiles is of significant importance in urban planning, socioeconomic structural design, and individual travel preference analysis. Traditional models often lack consideration of personalized features and exhibit suboptimal performance in constructing spatiotemporal dependencies. To address these issues, this paper proposes a method that integrates spatiotemporal information with travel-related information and employs generative adversarial networks (GANs) for adversarial training. This method accurately fits the true distribution of user travel data, thereby providing detailed profiles of public transportation passengers’ travel behavior. Specifically, the proposed approach considers the complete travel chain of individuals, establishes a spatiotemporal constraint representation model, and utilizes GANs to simulate the distribution of passenger travel, obtaining more compact and high-level travel vector features. The empirical results demonstrate that the proposed method accurately captures passengers’ travel patterns in both the temporal and spatial dimensions, offering technical support for urban transportation planning.

Significant radiometric discrepancies commonly exist among multisource optical satellite images due to differences in sensor characteristics, acquisition geometry, atmospheric conditions, and imaging time, which severely limit their application accuracy in land-cover classification, change detection, and geophysical parameter retrieval. Therefore, relative radiometric normalization (RRN) has become an essential preprocessing step for the integrated application of multisource optical satellite imagery. However, existing RRN methods often suffer from low automation in radiometric reference sample selection and are highly sensitive to anomalous samples, resulting in limited normalization accuracy and robustness. To address these issues, this study proposes an automated cross-sensor RRN framework based on automatic construction and intelligent refinement of radiometric reference sample set (RRSS). An unsupervised deep-learning-based change detection approach is employed to automatically identify radiometrically stable samples and construct an initial RRSS. Subsequently, a dual-stage sample refinement strategy integrating statistical outlier detection and topographic prior knowledge is developed to progressively remove anomalous samples and improve sample reliability. Linear regression is then performed using the refined samples to establish the radiometric normalization model. Experiments conducted on multisource optical satellite imagery acquired under different terrain conditions demonstrate that the proposed framework consistently outperforms five mainstream RRN methods, achieving lower Root Mean Square Error (RMSE) and higher Spectral Angle Cosine (SAC) and Structural Similarity Index Measure (SSIM) values. The results indicate that automatic construction and intelligent refinement of RRSS effectively improve inter-image radiometric consistency, particularly in topographically complex regions. Root Mean Square Error (RMSE), Spectral Angle Cosine (SAC), and Structural Similarity Index Measure (SSIM).