Recently, unsupervised ship segmentation methods for Synthetic Aperture Radar (SAR) images have achieved promising results in offshore scenes. However, these methods generate a large number of false alarms in inshore scenes. To address this issue, we propose the pseudo-label driven framework for offshore to inshore SAR image ship segmentation (PLDF-S3), which leverages ship segmentation results from offshore scenes to assist inshore ship segmentation. In particular, to account for the anisotropy of ships, which are characterized by a dominant long-axis direction, we design a directional feature enhancement module (DFEM) in PLDF-S3to extract ship features with varying orientations. Additionally, due to the diverse size variations of ships in SAR images, we propose a hierarchical context enhancement module (HCEM) to capture ship features at different scales. Experimental results show that the proposed unsupervised PLDF-S3achieves comparable segmentation performance than several supervised methods under challenging inshore scenarios.

Optical remote sensing images are inevitably affected by cloud cover. To remove clouds from optical remote sensing images, a series of deep learning-based thin cloud removal methods have been developed. However, these methods have not explored the long-range modeling ability of state space models in optical remote sensing image thin cloud removal. In this paper, we propose a frequency-domain assisted Mamba for thin cloud removal, which is called CR-Famba. In CR-Famba, to better extract global and local features of images, we design a frequency-domain assisted state space layer (FDA-SSL). The FDA-SSL consists of two core components: residual state space block (RSSB) and frequency domain detail enhancement block (FDDEB). The RSSB utilizes the visual state space module (VSSM) to extract long-range dependencies of images from a spatial perspective while adding convolutional layers to overcome local pixel forgetting. Due to the rich detailed information of remote sensing images, we present FDDEB equipped with discrete wavelet transform (DWT) to supplement the extracted local information from the frequency domain perspective. We conduct experiments on different types of cloud-containing datasets, and the results show that our method can recover images with clearer texture details compared to other methods.

Image retrieval aims to find the most semantically similar images in the database. Existing deep hash-based retrieval algorithms utilize data augmentation strategies thus generating generalized hash codes. However, simple data augmentation only improves the accuracy of hash codes from the perspective of sample diversity, without fully utilizing the inherent characteristics of the images. In this letter, we explore the frequency domain information of images and propose a Frequency Domain Auxiliary Network (FDANet) for deep hash retrieval. To capture frequency domain information that can cope with image transformations, we develop the spectrum enhancement module (SEM) in FDANet. The SEM utilizes Fourier transform techniques to extract the amplitude component that can reflect the low-level statistics of the image. Then, leveraging the extracted amplitude components, the retrieval network enhances its perception of regions undergoing relative changes in the original spatial domain. Experiments on several image retrieval benchmarks demonstrate that our method outperforms other state-of-the-art hash algorithms in terms of performance on the test metrics.

Cloud removal is an inevitable task in optical remote sensing images, which aims at restoring high-quality images from cloud-contaminated images. In recent years, deep learning-based image cloud removal methods utilize convolution neural network to obtain clean images. However, due to the limitations of the convolution operator, these methods cannot effectively leverage the local and global information of the image. In this paper, we propose a Cascaded Memory Network (CMNet) for optical remote sensing imagery cloud removal. The CMNet recycles previously captured information to form a memory mechanism, which is composed of two cascaded components: local information memory module (LIMM) and global information auxiliary module (GIAM). The LIMM aims to obtain local spatial details information of the image via two sub-networks, and the GIAM tries to further restore the detail of the image from global perspective. In the LIMM, two sub-networks are constructed to capture the details from coarse to fine, each of which includes a continuous memory descriptor that describes local details of the image and a hierarchical memory correlation descriptor that adaptively integrates relevant features. In the GIAM, we design a swin cloud remove transformer layer and explore an adaptive normalization to cope with unevenly distributed thin clouds, and further provide theoretical proof for the existence of the required solution. Experimental results indicate that our method can remove clouds while maintaining the detailed information of the image. https://github.com/Lab-PANbin/.

Currently, image-denoising methods based on deep learning cannot adequately reconcile contextual semantic information and spatial details. To take these information optimizations into consideration, in this paper, we propose a Context-Space Progressive Collaborative Network (CS-PCN) for image denoising. CS-PCN is a multi-stage hierarchical architecture composed of a context mining siamese sub-network (CM2S) and a space synthesis sub-network (3S). CM2S aims at extracting rich multi-scale contextual information by sequentially connecting multi-layer feature processors (MLFP) for semantic information pre-processing, attention encoder-decoders (AED) for multi-scale information, and multi-conv attention controllers (MCAC) for supervised feature fusion. 3S parallels MLFP and a single-scale cascading block to learn image details, which not only maintains the contextual information but also emphasizes the complementary spatial ones. Experimental results show that CS-PCN achieves significant performance improvement in synthetic and real-world noise removal.

Recently, 3D reconstruction methods based on the neural radiance fields have demonstrated remarkable generative performance. However, these methods frequently tend to be resource hungry and are challenging to regulate large low-textured regions in typical indoor scenes. In this work, we analyze and integrate inherent semantic geometry cues for self-supervised 3D reconstruction training via a unified framework of volume rendering and signed distance implicit representations. In contrast to previous neural implicit methods, we simultaneously incorporate the pixel-aligned features and image patches for multi-view consistency, thereby enabling us to depict a large indoor scene from challenging scenarios with rich visual details and large smooth backgrounds. Extensive experiments and comparisons demonstrate that our proposed method has achieved state-of-the-art results by a large margin in various tasks (e.g. actual surface reconstruction, novel view synthesis, and learning a universal scheme in occlusion or distorted regions).

Image restoration is an ill-posed problem due to the infinite feasible solutions for degraded images. Although CNN-based and Transformer-based approaches have been proven effective in image restoration, there are still two challenges in restoring complex degraded images: 1)local-global information extraction and fusion, and 2)computational cost overhead. To address these challenges, in this paper, we propose a lightweight image restoration network (CLG-INet) based on CNN-Transformer interaction, which can efficiently couple the local and global information. Specifically, our model is hierarchically built with a "sandwich-like" structure of coupling blocks, where each block contains three layers in sequence (CNN-Transformer-CNN). The Transformer layer is designed with two core modules: Dynamic Bi-Projected Attention (DBPA), which performs dual projection with large convolutions across windows to capture long-range dependencies, and Gated Non-linear Feed-Forward Network (GNFF), which reconstructs mixed feature information. In addition, we introduce interactive learning, which fuses local features and global representations in different resolutions to the maximum extent. Extensive experiments demonstrate that CLG-INet significantly boosts performance on various image restoration tasks, such as deraining, deblurring, and denoising.

Remote sensing image scene classification aims to automatically assign semantic labels for remote sensing images. Recently, to overcome the distribution discrepancy of training data and test data, domain adaptation has been applied to remote sensing image scene classification. Most domain adaptation approaches usually explore transferability under the assumption that the source domain and target domain have common classes. However, in real applications, new categories may appear in the target domain. Besides, only considering the transferability will degrade the classification performance due to the strong interclass similarity of remote sensing images. In this article, we present an open set domain adaptation algorithm via exploring transferability and discriminability (OSDA-ETD) for remote sensing image scene classification. To be specific, we propose the transferability technology, which aims at the high interdomain variations and high intraclass diversity of remote sensing images. The purpose of transferability is to reduce the global distribution difference of domains and the local distribution discrepancy of the same classes in different domains. For high interclass similarity in remote sensing images, we adopt the discriminability strategy. The discriminability intends to enlarge the distribution discrepancy of different classes in different domains. To further promote the effectiveness of scene classification, we integrate the transferability and the discriminability into a framework. Moreover, we prove that the algorithm has a unique optimizer.

Remote sensing image scene classification refers to assigning specific semantic labels for remote sensing images. Due to the lack of labeled remote sensing images, domain adaptation is applied to remote sensing image scene classification. However, recent proposed methods mainly focus on the closed set scenario. In this paper, we explore the open set scenario and introduce an open set domain adaptation network (OSDANet) for remote sensing image scene classification. Inspired by the idea of Generative Adversarial Network (GAN), we design a feature generator as well as a classifier which are learnt in an adversarial way. The purpose of the classifier is to find a boundary between the source and the target samples, while the feature generator attempts to force target samples away from the boundary. Especially, for the target samples, the feature generator will determine whether to align them with source samples or reject them as unknown target samples. The experimental results have indicated the effectiveness of the proposed method.

Remote sensing image scene classification refers to assigning semantic labels according to the content of the remote sensing scenes. Most machine learning-based scene classification methods assume that training and testing data share the same distributions. However, in real application scenarios, this assumption is difficult to guarantee. Domain adaptation (DA) is a promising approach to address this problem by aligning the feature distribution of training and testing data. Inspired by the idea DA, in this article, we propose a correlation subspace dynamic distribution alignment (CS-DDA) method for remote sensing image scene classification. Aiming at the characteristics of remote sensing scenes, we introduce two strategies to balance the effects of source and target domains: subspace correlation maximization (SCM) and dynamic statistical distribution alignment (DSDA). On the one hand, SCM tries to avoid mapping source domain data into irrelevant subspace to preserve the representation information of the source domain. On the other hand, DSDA is proposed to reduce the data distribution discrepancy between aligned source and target domains. Specifically, DSDA is a dynamic adjustment process where an adaptive factor is learned to balance the interclass and intraclass distribution between domains. Moreover, we integrate SCM and DSDA into a uniform optimization framework, and the optimal solution can be converted to the generalized eigendecomposition problem by derivation. The experimental results indicate that the proposed method can generate better results when compared with other feature distribution alignment methods.