Remote sensing image reconstruction from undersampled data is very much required by the onboard imaging system to cut down data volume and maintain image quality. Nonlocal low-rank regularization deriving from group sparsity, low rank, singular-value thresholding, and nonconvex surrogate functions have recently emerged for image recovery. To use nonlocal low-rank compressed sensing for remote sensing image reconstruction, spectral and temporal redundancy are considered in this letter by utilizing the similarity of correlated bands or historical records. Prior structural knowledge helps to group nonlocal similar blocks more accurately. Oversmoothness of low-rank regularization is improved by injecting referenced structures selectively. The proposed compressed sensing method is tested on satellite images from MODIS, LandSat-7, LandSat-8, IKONOS, and Google Earth to make clear that it outweighs state-of-the-art methods in maintaining fidelity and high visual details.

Fusion of remote sensing images with different spatial and temporal resolutions is highly needed by diverse earth observation applications. A small number of spatiotemporal fusion methods using sparse representation appear to be more promising than traditional linear mixture methods in reflecting abruptly changing terrestrial content. However, one of the main difficulties is that the results of sparse representation have reduced expressional accuracy; this is due in part to insufficient prior knowledge. For remote sensing images, the cluster and joint structural sparsity of the sparse coefficients could be employed as a priori knowledge. In this paper, a new optimization model is constructed with the semi-coupled dictionary learning and structural sparsity to predict the unknown high-resolution image from known images. Specifically, the intra-block correlation and cluster-structured sparsity are considered for single-channel reconstruction, and the inter-band similarity of joint-structured sparsity is considered for multichannel reconstruction, and both are implemented with block sparse Bayesian learning. The detailed optimization steps are given iteratively. In the experimental procedure, the red, green, and near-infrared bands of Landsat-7 and Moderate Resolution Imaging Spectrometer (MODIS) satellites are put to fusion with root mean square errors to check the prediction accuracy. It can be concluded from the experiment that the proposed methods can produce higher quality than state-of-the-art methods.

Existing point cloud registration models suffer from large performance loss in low-overlap scenarios, while the generalization ability of most models are weak. In this article, we design a new model for point cloud registration pursing better low-overlap performance and generalization ability. On the one hand, to solve the registration problem in low-overlap scenes, we propose a novel full convolutional network searching for super points located in the overlapping region and generating feature descriptors at the super points simultaneously. The new network aims at extracting points beyond nonoverlapping or smooth regions. On the other hand, we introduce a rotation-invariant convolution strategy for the fully convolutional model so that the extracted feature descriptors have rotation invariance, which improves the generalization performance of the features. Our method is tested on 3DMatch, 3DLoMatch, KITTI, and ETH, and compared with the state-of-the-art methods. The experimental results demonstrate that our method can achieve the best performance in low-overlap registration tasks, and it performs well across unseen scenarios with different sensor modalities.

Because of the contradiction between the spatial and temporal resolution of remote sensing images (RSI) and quality loss in the process of acquisition, it is of great significance to reconstruct RSI in remote sensing applications. Recent studies have demonstrated that reference image-based reconstruction methods have great potential for higher reconstruction performance, while lacking accuracy and quality of reconstruction. For this application, a new compressed sensing objective function incorporating a reference image as prior information is developed. We resort to the reference prior information inherent in interior and exterior data simultaneously to build a new generalized nonconvex low-rank approximation framework for RSI reconstruction. Specifically, the innovation of this paper consists of the following three respects: (1) we propose a nonconvex low-rank approximation for reconstructing RSI; (2) we inject reference prior information to overcome over smoothed edges and texture detail losses; (3) on this basis, we combine conjugate gradient algorithms and a single-value threshold (SVT) simultaneously to solve the proposed algorithm. The performance of the algorithm is evaluated both qualitatively and quantitatively. Experimental results demonstrate that the proposed algorithm improves several dBs in terms of peak signal to noise ratio (PSNR) and preserves image details significantly compared to most of the current approaches without reference images as priors. In addition, the generalized nonconvex low-rank approximation of our approach is naturally robust to noise, and therefore, the proposed algorithm can handle low resolution with noisy inputs in a more unified framework.

Satellite images of synthetic aperture radar (SAR) sensors are contaminated by speckles from the coherent imaging mechanism. Although removing or mitigating speckle has been a critical issue for SAR applications, effective reduction continues to be a significant challenge for existing methods when preserving the intricate structures within SAR images. To address this issue, this work proposes a novel conditional diffusion model for SAR despeckling (DiffusionSAR). The new method explicitly learns data distributions by forward diffusion towards multiplicative gamma noise. The logarithmic and Yeo-Johnson (log-Yeo-Johnson) transformation are harnessed in preprocessing for fine-tuning or hybrid training. A prolonging steps technique is suggested in fine-tuning to match the preprocessing. A new synthetic dataset is designed for satellite SAR despeckling. The proposed method is compared with eight state-of-the-art methods using both synthetic and real-world SAR satellite images. The qualitative and quantitative evaluations confirm the effectiveness of the proposed method in structural preservation as well as noise reduction. A fine-tuning experiment using stacked multitemporal data shows the necessity of tine-tuning training in bridging the domain gap when trained with synthetic data and tested with real-world SAR data.