The limitations of remote sensing sensor technology make it difficult to simultaneously capture earth observation information presented in different forms within a single remote sensing image. Acquiring more plentiful target information through fusion technology has therefore remained a research hotspot. The fusion of multispectral (MS) and synthetic aperture radar (SAR) imagery integrates spectral and backscatter information, thereby improving land cover (LC) classification effects. However, current pixel-level fusion methods often fail to adequately account for the model differences between SAR and MS images, leading to spectral-spatial inconsistencies and severe degradation from speckle noise. To address this problem, A fusion method is proposed based on Bidirectional Aggregation of Saliency in the Human Visual System (BASHVS). First, the SAR and MS images are decomposed into base and detail layers using a synchronized anisotropic diffusion algorithm. Subsequently, the detail layer is fused using a New Sum of Modified Anisotropic Laplacian (NSMAL) algorithm. Finally, for base layer fusion, pixel saliency and structural saliency are extracted bidirectionally. The BASHVS is compared with 16 existing fusion methods using 10 evaluation metrics. The results demonstrate that BASHVS achieves the best comprehensive performance and significantly improves the visual quality of the fused images. LC classification using BASHVS fused images shows an average increase of 1.050% in overall accuracy and 0.014 in Kappa coefficient compared to the original MS images, confirming its advantage for LC classification. The source code of BASHVS is shared at https://github.com/CHUANGL8346/BASHVS.

To address the limitations of existing deep learning-based hyperspectral image compression methods in accurately modeling the rate-distortion problem, we propose a coupled multi-scale attention spatial-spectral high-fidelity compression network (MSAHiFiC). MSAHiFiC employs a super-prior network to estimate bitrate and guide rate-distortion optimization, enhancing performance under constrained bitrate conditions. A multi-scale spectral attention module is introduced to capture spectral dependencies across varying inter-band distances and preserve key spectral features during downscaling. A spectral fidelity term is further incorporated into the loss function to improve reconstruction accuracy. Experiments on three benchmark hyperspectral datasets—HySpecNet-11k, XiongAn, and WHU-Hi—demonstrate that MSAHiFiC outperforms state-of-the-art methods by achieving 5% higher spectral fidelity and 6% improvement in reconstruction accuracy under a low bitrate of 0.5 bpp.

The registration of synthetic aperture radar (SAR) and optical images is critical in multimodal remote sensing image fusion. In recent years, deep learning-based registration networks have been continuously introduced. However, owing to the significant disparities in viewing angles and radiometric properties between SAR and optical images, current deep learning methods struggle to fully exploit the physical properties of radar imaging. In addition, many existing matching networks typically perform only a forward pass, resulting in suboptimal model performance. This article proposes a cyclic iterative registration SAR mechanism network (termed as CIRSM-Net) for the registration of SAR and optical images. First, we design a learning module that integrates the radar equation with a microwave scattering model to capture deep features from SAR images, and design a corresponding scattering feature loss to aid in better generalization across various radar images. Then, to explore optimization methods for matching networks, this study proposes a strategy of multiple iterative optimizations within the matching network. Specifically, it integrates speeding-up radiation-variation insensitive feature transform (RIFT2) supervision in the backend matching network and iteratively optimizes the final output. Finally, during the iteration process, we propose an innovative matching loss function that combines the rotation invariance supervision of RIFT2 with iterative optimization techniques to enhance feature matching accuracy. Experimental results on both public and our own datasets additionally confirm the effectiveness and superiority of the proposed approach, demonstrating its significant potential for practical applications.

The regularization of point cloud contour points is crucial for the accurate recognition of object boundary information, to the realization of 3-D representation, object recognition, and segmentation. To express the outline of buildings of different curve structures, a regularization method of multiple-curve growth is proposed in this article. First, we construct multicurve fitting parametric equations for lines, 3-D conics, and circular curves based on global least squares, geometric features, and 3-D–2-D transformations. Second, we propose a multicurve growth algorithm of contour points based on multicurve parameter fusion and segment the multicurve points. Finally, the distance between points and multicurve is used to determine the curve type, and the regularization based on different types of curve points is realized by combining the interpolation principle. The experimental results show that the proposed method is more robust than the least squares, changeable degree (CD) spline curve, and improved B-spline curve, and the precision, recall, and F1-score of the proposed method are higher than 80%. The contour points of linear and curvilinear of buildings can be effectively regularized by the obtained multicurve fitting model. The proposed method can provide urban planners with more comprehensive information on urban architectural forms and spatial layouts. The source code will be available athttps://github.com/xijiangyue04/Regularization-of-multi-curve-parameter-fusion.

Multispectral and SAR image fusion is one of the key technologies to improve image quality. The fusion method of multi-scale decomposition includes two aspects: the decomposition of image and the design of fusion rule. There are some problems in the traditional decomposition methods, such as gradient reversals, halos and other artifacts, and limited scale separation of space overlapping features. In addition, the quality of fusion images is greatly affected by the fusion rule design. Therefore, a novel method based on least squares optimization rolling guidance filtering for multi-scale decomposition is proposed. All gradients of rolling guidance filtering are optimized by least squares to suppress artifacts such as gradient inversion, and then combined with Gaussian filtering for image decomposition to eliminate interference texture and speckle noise while preserving edge details. At the same time, the decomposition of image is extended to multi-scale space to achieve scale separation of space overlapping features, which is convenient for multi-level fusion of image features. In the end, based on the scale of decomposition, the results fall into three layers, and coupled neural P system and other rules are designed for different layers of information fusion. The results indicate that this method has good visual effect and outperforms all the other comparison methods on nine evaluation indexes.

Hyperspectral remote sensing band selection picks out characteristic feature combination to weaken the strong correlation caused by spectral continuity. However, it is difficult for traditional methods with fixed strategies to search the entire space and make adjustments for the optimization process. Thus, the solutions obtained can be mostly local optima. In this paper, a novel adaptive multi-strategy particle swarm optimization for hyperspectral image remote sensing band selection (AMSPSO_BS) is introduced to obtain a subset solution suitable for classification. The problem is modeled as an effective fitness function, and the quotient of the linear discriminant value and the mean mutual information (LD/MMI) is used to remove the redundancy between bands. The randomly generated solutions are then encoded to form a population, which rely on various particle update strategies (PUS) with different reference positions for updating. During the particle motion, the effect of each strategy on population evolution is considered comprehensively and reflected in the change of selection probability. And the motion parameters are dynamically adjusted to balance the global and local capabilities. Four hyperspectral remote sensing image datasets were utilized to conduct band selection experiments, to confirm the effectiveness of AMSPSO_BS.