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. Additionally, many existing matching networks typically perform only a forward pass, resulting in suboptimal model performance. This paper proposes a cyclic iterative registration SAR mechanism network (termed as CIRSM-Net) for the registration of SAR and optical images. Firstly, 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.

Multispectral and synthetic aperture radar (SAR) image fusion is one of the key technologies to improve image quality. The fusion method of multiscale decomposition includes two aspects: the decomposition of the image and the design of the 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 (LS) optimization rolling guidance filtering (RGF) for multiscale decomposition is proposed. All gradients of RGF are optimized by LS 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 the image is extended to multiscale space to achieve scale separation of space-overlapping features, which is convenient for multilevel fusion of image features. In the end, based on the scale of decomposition, the results fall into three layers, and coupled neural P (CNP) system and other rules are designed for different layers of information fusion. The results indicate that this method has a good visual effect and outperforms all the other comparison methods on nine evaluation indexes.

Spectral information and backscatter information are both exclusively important bases for land cover classification, and these two kinds of information are found in multispectral images and SAR images, respectively. Therefore, the fusion of complementary information of multispectral and SAR images can effectively improve land cover classification accuracy. However, the existing fusion methods of multispectral and SAR images generally have some problems, such as insensitivity to edge information, serious interference by speckle noise, and unreasonable settings of fusion rules, which lead to unsatisfactory results of land cover classification. To solve this issue, a fusion method based on adaptive multiscale Gaussian co-occurrence filtering decomposition is proposed. Firstly, parameters controlling Gaussian distribution scale in co-occurrence filter are adaptive according to pixel intensity skewness. Gaussian filtering and adaptive co-occurrence filtering are applied to the original image to smooth out speckle noise and interference edges within the textures while preserving edge information between textures. Secondly, difference calculation among the original image, Gaussian filtered image and adaptive co-occurrence filtered image is carried out to realize the decomposition of detail information, edge information and basic information. Compared with the decomposition method that only decomposes detail information and basic information, it can effectively preserve boundary structure between the features. The image decomposition is extended to multi-scale space for multi-layer fusion of image features at the same time. Finally, according to the decomposition scale, the decomposition results are divided into three layers, and corresponding rules of fusion are formulated based on their feature information, so as to generate fusion image with low noise interference, clear boundary and homogeneous pixel convergence. Experimental results show that the proposed method generally performs the best in eight evaluation indexes compared with ten other methods. The overall accuracy, average accuracy and Kappa coefficient of land cover classification are increased by 7.674%, 6.776% and 0.098 respectively compared with those of the original multispectral image in Area 1, and by 6.904%, 7.649% and 0.089 respectively compared with those of the original multispectral image in Area 2.

Weighted total least-squares (WTLS) is becoming popular for parameter estimations in geodesy and surveying. However, it does not take into consideration the possible gross errors in observations, which may lead to a reduction in the robustness and reliability of parameter estimations. In order to solve this problem, in this study, Lagrange multipliers (LM) are employed to make WTLS solution rigorous and the IGG (Institute of Geodesy and Geophysics) weight function is employed to make WTLS solution more robust and reliable, resulting in a new robust WTLS solution (RWTLS-LM-IGG). A comparison with existing WTLS and robust WTLS solutions is conducted for linear regression and coordinate transformation, through experimental evaluation with simulation data sets (with different numbers and magnitudes of gross errors) and two sets of real-life data. The results of simulation experiments show that the variance component and the mean square error of estimated parameter vector obtained by using the existing methods increase almost linearly with an increase in the numbers and magnitudes of gross errors, but these values obtained by using the proposed method are almost stable, which means an effective reduction of the influence of the gross errors by the proposed method as compared with the existing methods. It is also found that the larger the numbers and magnitudes of gross errors, the more obvious such a reduction. Furthermore, the experiment results with two sets of real-life data are consistent with the results of simulation experiments.

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.

Remote sensing image fusion can effectively solve the inherent contradiction between spatial resolution and spectral resolution of imaging systems. At present, the fusion methods of remote sensing images based on multi-scale transform usually set fusion rules according to local feature information and pulse-coupled neural network (PCNN), but there are problems such as single local feature, as fusion rule cannot effectively extract feature information, PCNN parameter setting is complex, and spatial correlation is poor. To this end, a fusion method of remote sensing images that combines low-level visual features and a parameter-adaptive dual-channel pulse-coupled neural network (PADCPCNN) in a non-subsampled shearlet transform (NSST) domain is proposed in this paper. In the low-frequency sub-band fusion process, a low-level visual feature fusion rule is constructed by combining three local features, local phase congruency, local abrupt measure, and local energy information to enhance the extraction ability of feature information. In the process of high-frequency sub-band fusion, the structure and parameters of the dual-channel pulse-coupled neural network (DCPCNN) are optimized, including: (1) the multi-scale morphological gradient is used as an external stimulus to enhance the spatial correlation of DCPCNN; and (2) implement parameter-adaptive representation according to the difference box-counting, the Otsu threshold, and the image intensity to solve the complexity of parameter setting. Five sets of remote sensing image data of different satellite platforms and ground objects are selected for experiments. The proposed method is compared with 16 other methods and evaluated from qualitative and quantitative aspects. The experimental results show that, compared with the average value of the sub-optimal method in the five sets of data, the proposed method is optimized by 0.006, 0.009, 0.009, 0.035, 0.037, 0.042, and 0.020, respectively, in the seven evaluation indexes of information entropy, mutual information, average gradient, spatial frequency, spectral distortion, ERGAS, and visual information fidelity, indicating that the proposed method has the best fusion effect.