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.

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%.

To meet the demands of regional mapping with synthetic aperture radar (SAR) satellites, this study proposes a multistrip stitching imaging mission planning (MSIMP) method that considers energy consumption. Accounting for both regional coverage rate and satellite energy consumption, an MSIMP model was constructed. The model maximizes the region coverage benefit (RCB), minimizes the total imaging time (TIT) as the optimization objectives, and sets the indices of orbit selection, side-swing angle coefficients, and determination coefficients of the start time and end times of each orbit as the decision variables to realize the overall optimal scheduling of available orbital resources (ORs), side-swing angles, and imaging time of satellites. To address the problem of the large-scale and diverse types of decision variables in the MSIMP model, an improved particle swarm optimization (PSO) algorithm with a hybrid local search and differential evolution (LSDE) strategy (LSDE-PSO) that implements differential operations and local searches on two types of particles at various stages of the evolution process to enhance the model’s solution efficiency was proposed. The proposed method was validated using three simulation scenarios with different regional sizes. The experimental results showed that the proposed method demonstrated consistent adaptability to different-scale tasks, achieving the synchronized optimization of coverage revenue and on-orbit energy consumption. Compared with existing algorithms, the proposed LSDE-PSO algorithm can obtain the optimal imaging scheme with a higher RCB with lower TIT at almost the same computational cost, which provides significant technical support for mission planning for regional SAR satellite imaging in practical applications.

Owing to changes in the space thermal environment, the optical axis angle between the camera and star tracker (Cam-ST) changes as well, resulting in regular low-frequency attitude errors and a decline in the geometric positioning accuracy of the images. In this study, a star-observation-based low-frequency attitude error correction method is proposed. First, the star was used as the control point to be observed by satellite from different latitudes in an orbit. The star information is extracted using a priori attitude to obtain the camera pointing in inertial coordinates. Second, combined with the optical axis pointing value of the star tracker, the angle change between the camera and two star trackers (Cam-2STs) was calculated. A polynomial model was used to fit the angle change, and the fitting model and camera optical axis pointing compensation algorithm were used to compensate for the camera optical axis. Third, the regular low-frequency attitude error of the camera was realized. The experiment used 20 groups of star observation data from two missions of the Jilin-1 07 video satellite. After compensation, the camera pointing maximum root mean square error was reduced from 30.08" to 7.70", theX-axis attitude error was reduced from 13.26" to 7.09", and theY-axis from 29.48" to 7.36". The geometric positioning accuracy of Jilin-1 ground images was 25.63 m after correction. The experimental results showed that the proposed method effectively improved the attitude measurement accuracy. This method can thus effectively improve the direct geometric positioning accuracy of a single image.

Noticeable striping artifacts in collected satellite imagery, caused by the inconsistency of pushbroom sensor detector responses, degrade the image quality. Relative radiometric calibration aims to calibrate inconsistencies in terms of detector responses, thereby eliminating detector-level striping artifacts. Yaw calibration has become the preferred imagery-based calibration method for satellites without on-board calibration equipment, owing to its high accuracy and convenience. However, it often relies on a large uniform field on the Earth’s surface and is a linear calibration method. This method does not consider acquisition-related geometric errors associated with the sensor, resulting in reduced calibration accuracy. In this study, we propose an improved method for yaw calibration, which accounts for the geometric distortion of sensor imaging and does not require a uniform field. A fast geometric-positioning algorithm was used to remove geometric distortion, followed by high-precision extraction of the calibration reference for each sensor’s detector. The dynamic range of the sensor calibration was extended, followed by the calibration of the sensor nonlinear response model. Our results based on Yaogan-25 images suggest the following: 1) the improved method effectively eliminates the “sawtooth” caused by the yaw calibration method without considering the geometric distortion and 2) it outperforms other imagery-based calibration methods with respect to visual destriping effects such that the root-mean-square deviations of the corrected imagery experienced a decrease of 0.65 percentage point, compared with that of the statistical method, and 0.17 percentage point for the yaw calibration. Such improvements will promote high-quality applications of remote-sensing images.

Aiming at the imaging problem of regional target in emergency surveying and mapping, a multi-objective optimization model(MOOM) is proposed, which takes the imaging lateral swing angles of satellite as decision variables and takes the maximum coverage rate of regional target and the minimum number of holes as objective functions. Aiming at the two key problems of evaluation function calculation and multi-objective model solving, Vatti algorithm and NSGAII algorithm are used to solve them respectively. Finally, STK simulation data are used to verify the feasibility of the optimization method.