With the increase of resolution, effective characterization of synthetic aperture radar (SAR) image becomes one of the most critical problems in many earth observation applications. Inspired by deep learning and probability mixture models, a generalized Gamma deep belief network (g$\mathsf{\Gamma }$-DBN) is proposed for SAR image statistical modeling and land-cover classification in this work. Specifically, a generalized Gamma-Bernoulli restricted Boltzmann machine (g$\mathsf{\Gamma }$B-RBM) is proposed to capture high-order statistical characterizes from SAR images after introducing the generalized Gamma distribution. After stacking the g$\mathsf{\Gamma }$B-RBM and several standard binary RBMs in a hierarchical manner, a g$\mathsf{\Gamma }$-DBN is constructed to learn high-level representation of different SAR land-covers. Finally, a discriminative neural network is constructed by adding an additional predict layer for different land-covers over the constructed deep structure. Performance of the proposed approach is evaluated via several experiments on some high-resolution SAR image patch sets and two large-scale scenes which are captured by ALOS PALSAR-2 and COSMO-SkyMed satellites respectively.

With the increased number of synthetic aperture radar (SAR) systems that have been launched in recent years, many high-quality images have been produced for precise earth observation. The number of available images requires new feature extraction techniques to capture enough discriminant information from high-resolution SAR images. To effectively characterize different land covers, a multiscale local Fisher pattern (MLFP) is proposed to extract both statistical and spatial properties in this work. First, the Fisher vector (FV) of each pixel is calculated after the statistical distribution of high-resolution SAR intensities is modeled via a Gamma mixture model. Then, the generative and discriminant information implied in the FV is exploited after the extraction of the geometrical information between neighboring pixels within different scales. Two experiments are conducted to demonstrate the performance of the proposed MLFP, which are high-resolution SAR image land-cover classification and SAR scene classification. Compared with some existing approaches, the proposed MLFP shows superior performance after the statistical and spatial structure information from different land covers is exploited.

Speckle is one of the inevitable obstacles related to synthetic aperture radar (SAR) image change detection; it increases the overlap between changed and unchanged pixels in the histogram of a difference image. This makes the selection of a statistic model more difficult for describing opposite classes. To address this issue, this article developed an unsupervised change-detection approach for multitemporal SAR images that specifies a priori knowledge about the spatial characteristics of the classes through Dempster-Shafer evidence theory and embeds it into the Expectation-Maximization (EM) iteration process. It is based on the consideration that each pixel in the difference image is unique due to its neighbourhood, although some of them may have the same pixel value. Thus, under the hypothesis that local and global a priori knowledge are independent sources, a global-local a priori model is developed through Dempster-Shafer evidence theory. The EM algorithm allows one to estimate the statistical parameters of the opposite classes associated with this a priori model. As a consequence, the change-detection result can be obtained within the framework of Bayes. Visual and quantitative results obtained on real multitemporal SAR image data sets confirm the effectiveness of the proposed method compared with state-of-the-art ones for SAR image change detection.

Heterogeneous remote sensing images, acquired from different sensors, exhibit significant variations in data structure, resolution, and radiometric characteristics. These inherent heterogeneities present substantial challenges for change detection, a task that involves identifying changes in a target area by analyzing multi-temporal images. To address this issue, we propose the Multi-Head Graph Attention Mechanism (MHGAN), designed to achieve accurate detection of surface changes in heterogeneous remote sensing images. The MHGAN employs a bidirectional adversarial convolutional autoencoder network to reconstruct and perform style transformation of heterogeneous images. Unlike existing unidirectional translation frameworks (e.g., CycleGAN), our approach simultaneously aligns features in both domains through multi-head graph attention and dynamic kernel width estimation, effectively reducing false changes caused by sensor heterogeneity. The network training is constrained by four loss functions: reconstruction loss, code correlation loss, graph attention loss, and adversarial loss, which together guide the alignment of heterogeneous images into a unified data domain. The code correlation loss enforces consistency in feature representations at the encoding layer, while a density-based kernel width estimation method enhances the capture of both local and global changes. The graph attention loss models the relationships between features and images, improving the representation of consistent regions across bitemporal images. Additionally, adversarial loss promotes style consistency within the shared domain. Our bidirectional adversarial convolutional autoencoder simultaneously aligns features across both domains. This bilateral structure mitigates the information loss associated with one-way mappings, enabling more accurate style transformation and reducing false change detections caused by sensor heterogeneity, which represents a key advantage over existing unidirectional methods. Compared with state-of-the-art methods for heterogeneous change detection, the MHGAN demonstrates superior performance in both qualitative and quantitative evaluations across four benchmark heterogeneous remote sensing datasets.

Aiming at the lack of publicly available datasets of satellite Specific Emitter Identification (SEI) and the problem of insufficient data under multi-receiver conditions, this paper constructs a multi-receiver one-dimensional I/Q dataset for Iridium downlink signals using six ADALM-PLUTO software-defined radio receivers. The dataset contains a total of 367,658 receiver-level valid signal observations covering 66 Iridium satellites. Each sample contains metadata such as SNR, acquisition timestamp, location information, and environmental information. The dataset preserves the unmodulated tone, BPSK field, and QPSK data segment of each detected Iridium burst as one-dimensional I/Q sequences, while the multi-receiver acquisition configuration enables the investigation of receiver-dependent effects under common observation conditions. Representative classification experiments with multiple deep-learning models, different input lengths, different signal-to-noise ratios, and cross-receiver evaluations are provided as technical validation to demonstrate the usability of the released data for downstream satellite specific emitter identification tasks. The dataset provides a publicly available data resource for satellite radio frequency fingerprinting, cross-receiver identification, and low-SNR signal studies.

Aiming at the lack of public dataset of satellite Specific Emitter Identification (SEI) and the problem of insufficient data under the condition of multiple receivers, this paper constructs a multi-receiver one-dimensional I/Q dataset for Iridium downlink signal through six software radio receivers with the same parameter configuration. The dataset contains 367,658 valid samples from 66 Iridium satellites. Each sample contains metadata such as SNR, acquisition timestamp, location information, and environmental information. At the same time, the validity of the dataset was evaluated by three experiments under seven deep learning models, different input lengths and different signal-to-noise ratios. The experimental results show that the dataset can effectively support the training and testing of different deep learning models, and can reflect the influence of signal length and signal quality on the performance of satellite individual identification. It provides data basis for satellite radio-frequency fingerprinting identification, low signal-to-noise ratio identification and model performance evaluation under the condition of multiple receivers.