Xin Shen

Inconsistent radiometric response characteristics of nighttime sensor detectors can interfere with weak nighttime surface radiation, resulting in radiometric inconsistencies in captured images. This degrades image quality and compromises information extraction from such image products. Relative radiometric calibration involves calibrating response differences among imaging detectors to eliminate radiometric inconsistencies, which is particularly critical for nighttime sensors. However, existing on-orbit calibration methods require onboard calibration devices, reliable attitude control, and dedicated calibration imaging tasks. The limited hardware capabilities and attitude control capacity of micro nighttime satellites pose significant challenges for radiometric calibration. This study proposes a deep learning–based on-orbit relative radiometric calibration method for nighttime sensors to address these limitations, representing an effort to apply deep learning techniques to relative radiometric calibration of remote sensing satellites. A Multi-Gain Radiometric Response Transfer Network (MGRT-Net) was developed to characterize the radiometric response relationships of nighttime sensor across gain settings. A daytime low-gain radiometric calibration reference was established using a uniform calibration method. The calibration reference is transferred to nighttime images to calibrate the radiometric parameters of the nighttime sensor in high-gain mode. Using Luojia1-01 nighttime images, MGRT-Net accurately simulated radiometric responses across different gain settings and effectively transferred daytime low-gain radiometric calibration references to nighttime images. After MGRT-Net–based transfer correction, radiometric response errors among sensor detectors in nighttime high-gain images were effectively eliminated. Under low- and high- brightness, the streaking metrics exceeded 0.027% (76.5% improvement) and 0.028% (75.6% improvement), respectively, outperforming existing methods. Compared with analytical calibration methods, the proposed method requires no prior model or additional satellite requirements and demonstrates strong adaptability. This study provides a reference for micro-remote sensing satellites constrained by manufacturing costs and validates the applicability of deep learning methods for relative radiometric calibration.

Mosaicking multiple remote sensing images is an effective approach to rapidly expand the coverage of remote sensing imagery, and an indispensable process in large-area mapping using remote sensing imagery. Differences in sensor properties, imaging conditions, and processing algorithms cause radiometric variations, requiring consistency correction during mosaicking. However, most existing methods employ linear correction models based on statistical information between images, which are often inadequate for addressing nonlinear discrepancies caused by the above differences. This study proposes a nonlinear correction method for achieving radiometric consistency in multiple remote sensing images, based on global optimisation. A quadratic local constraint model, combined with a global mean-variance preservation constraint, ensures smooth transitions and prevents colour shifts. Validation with multi-temporal high-resolution images shows the method outperforms Wallis filtering, Histogram matching, linear correction, and TRR in handling nonlinear radiance differences. The mosaic products exhibit natural visual quality and smooth colour transitions, achieving colour balance and avoiding excessive radiometric correction. The method reduces the mean of absolute mean differences and the mean of absolute standard deviation differences by 30.5% and 44.5% versus uncorrected images, and by 17.5% and 6.1% versus linear correction. The color distance drops 81.2%, while the gradient loss remains low (0.64), preserving spectral fidelity and structural details. In terms of global radiometric stability, the method, maintaining the overall mean value above 93.4% of all the uncorrected images, an improvement of 10.3% compared to the linear method, achieving effective radiometric balance and consistency, and effectively enhancing the mapping quality of large-area remote sensing imagery.

Most existing multimodal fusion methods adopt a symmetric fusion strategy to optimize feature consistency between modalities. However, this strategy ignores modality specific discrepancies, and its equal weight distribution mechanism exacerbates the class imbalance problem, resulting in poor classification performance for small-sample categories such as “road”. To address these issues, this study proposes a Category Aware Asymmetric Dynamic Fusion Network (CADFNet), which realizes cross-modal differentiated feature interaction guided by class-level information through the synergy of a Multi-Level Dif ference Pyramid Fusion Module (MDPF) and a Dynamic Sparse Category Attention Module (DSCA). The MDPF module fully mines and enhances cross-modal complementary information by explicitly modeling multi-scale differential features to address the problem of insufficient utilization of modal difference features in existing methods; The DSCA module uses a dynamic sparsity mechanism in conjunction with semantic perception to adaptively assign modal weights to different categories, achieving refined alignment and fusion of cross-modal key areas, thereby effectively alleviating the class imbalance problem. Comprehensive experiments on the PIE-RGB-SAR and WHU-OPT-SAR datasets demonstrate that CADFNet achieves excellent performance. In particular, on the WHU-OPT-SAR dataset, its mean Intersection over Union (mIoU) shows improvements ranging from 1.99% to 15.32% over other state-of-the-art methods.

While systematic radiometric errors can be mitigated through on-orbit calibration, correcting low-frequency, time-varying errors often induced by complex imaging dynamics remains a greater challenge. This study introduces a correction method tailored for optical remote sensing platforms to overcome such errors in JiTian-03 imagery, particularly those arising from high-agility curve imaging. The process begins with relative radiometric calibration to correct high-frequency inconsistencies among sensor detectors, thereby suppressing striping and banding noise in the imagery. Next, the image is decomposed into low-frequency background and high-frequency detail components. A column-wise moment-matching algorithm is then applied to the low-frequency background, aligning each column’s first- and second-order central moments with global statistics to derive correction coefficients. This approach effectively compensates for low-frequency radiometric errors while preserving high-frequency texture details in the image. Experimental results confirm that, following correction of both high- and low-frequency radiometric errors, JT-03 imagery exhibits a substantial reduction in striping, banding, and brightness nonuniformity. The correction yields a uniform radiometric response across the entire field of view while preserving ground texture fidelity and image detail. As a result, the radiometric uniformity of JT-03 image products is significantly enhanced. Quantitatively, post high-frequency calibration, the striping index across all JT-03 bands improved to within 0.1% and the standard deviation of column means to 4.7%. Subsequent low-frequency correction reduced the standard deviation of column means by an average of 73.89%, achieving a final value of better than 1.3%.

With the rapid development of hyperspectral image classification (HSIC) technology, its applications in geological exploration and environmental monitoring have become increasingly prominent. Recently, Mamba has garnered significant attention owing to its outstanding performance in long-range sequence modeling and linear computational complexity. However, Mamba still exhibits significant limitations in HSIC: first, it does not fully consider the hierarchical spatial-contextual representation and nonlinear spectral interactions in hyperspectral images; second, its sequential processing approach leads to the loss of spatial structural information and feature redundancy. In response, this study proposes a structure-enhanced spatial–spectral dynamic gating Mamba (SEDGM) that leverages the collaborative design of spatial and spectral gating Mamba mechanisms to extract and exploit key regional features of hyperspectral data. The spatial branch employs Hierarchical Gating Mamba to capture multi-directional pixel sequences and extract the hierarchical spatial features and their intrinsic relationships. In contrast, the spectral branch utilizes a Random Shuffled Gating Mamba to disrupt the fixed order of traditional spectral sequences and capture higher-order spectral couplings, effectively characterizing the cooperative variation patterns of spectral features. Both branches employ a dynamic gating mechanism that weights features based on sequence centrality, dynamically activating feature sequences. Additionally, shape-specific offset-aware attention is incorporated into each branch to enhance the structured features that were lacking in the Mamba sequences. Finally, a Spectral-Oriented Feature Review Module is incorporated to achieve dynamic feature fusion and optimized refinement. Experiments were conducted on four large-scale benchmark HSI datasets, with SEDGM achieving significant improvements in classification performance, validating the effectiveness of this approach in hyperspectral image classification tasks. The code is available at https://github.com/shuai2023-hash/SEDGM.

Accurate height estimation from satellite imagery is essential for digital surface model (DSM) generation in remote sensing applications. Although recent deep learning-based multi view stereo (MVS) methods have advanced height prediction, they often struggle with edge degradation, particularly around building outlines and terrain discontinuities. To address this issue, we propose SP-MVS, a structure-preserving MVS network that explicitly enhances edge-aware height estimation. The network integrates a triple-branch structural encoder equipped with a U-Net backbone and the Convolutional Block Attention Module (CBAM) to capture multi-scale spatial and texture features. A Mamba-based cost volume regularization module is introduced to model long-range cross-view dependencies with low computational overhead. Furthermore, we design a multistage Edge–texture consistency loss to guide the network in preserving sharp structural boundaries throughout the estimation process. SP-MVS directly produces high-quality height maps and serves as a critical component in precise DSM generation. Extensive experiments on the WHU-TLC and US3D datasets demonstrate that SP-MVS achieves superior accuracy and sharper boundary delineation compared to state-of-the-art methods.

In the process of mosaicking regional synthetic aperture radar (SAR) intensity images, multiple images with significant brightness anomalies can cause a considerable number of pixels to exceed the grayscale quantization range. Applying traditional color harmonization methods increases this issue, causing a loss of brightness information. We propose a multi-objective gray consistency correction method designed explicitly for mosaicking regional SAR intensity images with brightness anomalies to address this. We constructed a two-objective optimization model to ensure regional image gray consistency and mitigate brightness information loss. The truncation values of brightness anomaly images were selected as decision variables, maximizing the overall gray consistency of overlapping image pairs and minimizing the number of pixels with grayscale values that were out of bounds as the objective functions. To synchronously solve the truncation values of brightness anomaly images and linear stretch parameters of all images, a hybrid framework that combines the non-dominated sorting genetic algorithm II (NSGA-II) with the quadratic programming (QP) algorithm was proposed. Two large-area experimental results show that the proposed method achieves a balanced optimization between gray consistency and brightness information loss for regional SAR intensity image mosaicking. Compared with the traditional method, our method reduces brightness information loss by 99.552–99.647% and 99.973–99.969%, respectively, while maintaining better peak signal-to-noise ratio performance.