Remote sensing hyperspectral images (HSIs) may contain incomplete or corrupted spatial or spectral information, how to reconstruct the missing regions to obtain a complete HSI is a challenging topic. Existing HSI inpainting methods are less generalizable across different remote sensing HSI and often contain artifacts. In order to address this problem, a HSI inpainting method based on predictive image filtering integrated generative restoration is proposed, which can adaptively handle different scenes with dynamically predicted filtering kernels according to different input. The generator of the proposed predictive filtering integrated generative HSI inpainting network (PFGIN) comprises two interconnected collaborative branches: the kernel prediction network (KPN) and the filtering guided generative network (FGN). FGN provides features to KPN, and the KPN dynamically predicts the kernels of designed spatial-spectral filtering according to its input. Extensive experiments on the public datasets have demonstrated the effectiveness of the proposed method and its superiority over the other comparison methods. The code for PFGIN is publicly available at https://github.com/yinhuwu/PFGIN.

Infrared small target detection plays a crucial role in infrared search and tracking systems. However, current detection methods are limited by the small target size and low signal-to-noise ratio of infrared imagery. Furthermore, motion features for target detection are difficult to extract using simple frame subtraction due to poor imaging conditions. Therefore, we focus on the holistic interframe interaction to enhance the temporal feature and propose a spatiotemporal local contrast method in this letter. First, the motion-enhanced density peak clustering is employed to determine the robust localization of candidate targets, in which the density feature maps are generated by the preprocessing of non-consecutive three frames difference after image registration. Second, to reliably exploit interframe interactions across both non-consecutive and successive frames, a temporal domain saliency map is computed based on local regions from successive frames. Moreover, a spatial domain saliency map is obtained using a novel tri-layer local contrast measure. By fusing results from both domains, the infrared small targets are detected through adaptive threshold segmentation. Experimental results on four real sequences demonstrate that the proposed method can achieve better detection performance by target enhancement and background suppression than other spatiotemporal algorithms.

In the field of remote sensing, the detection of tiny objects has always been an interesting and highly regarded issue. Although many researchers have dedicated their efforts to studying this problem, it still presents numerous challenges due to the complexity of the environment in which tiny objects are presented in remote sensing images. To this end, we propose a remote sensing image tiny objects detection method based on explicit semantic guidance, with a specific focus on regions containing tiny objects. Specifically, we incorporate supervision of the tiny object regions during the training process. This supervision allowed us to extract tiny object regions, thereby forming an explicit attention map. This explicit attention map is employed to semantically modulate the feature map for detecting tiny objects, thus enhancing the regions containing tiny objects while suppressing the background. Extensive experiments are conducted on the AI-TODv2 dataset and the proposed method can achieve an AP of 24.6%. The experimental results demonstrate the effectiveness of the proposed tiny object detection method based on explicit semantic guidance. The code will be released soon on the site of https://github.com/dyl96/ESG_TODNet.

Remote sensing hyperspectral images (HSIs) have been applied in a variety of fields. However, HSIs are susceptible to various types of noise which affect both their quality and subsequent analysis. Existing knowledge-driven methods are time-consuming and need handcrafted parameters, while data-driven methods require large amounts of training resources and lack interpretability. What’s more, most methods mainly focus on Gaussian noise rather than Poisson-Gaussian noise that matches better the real noise model. To address this issue, this paper proposes a multi-priors guided HSIs noise removal method that not only combines the benefits of traditional methods and deep learning methods but also considers the Poisson-Gaussian based mixed noise. Specifically, tensor subspace representation based on the guidance of the global spectral low-rank prior is employed to decompose the HSI into eigen-images and orthogonal spectral basis. Then a nonlocal-local aware network that incorporates the guidance of local and nonlocal self-similarity priors is constructed to remove the noise in the eigen-images effectively and efficiently. Extensive experiments demonstrate that our method achieves better quantitative and qualitative performance compared to the state-of-the-art methods.

Tiny object detection in the field of remote sensing has always been a challenging and interesting topic. Despite many researchers have been working on this problem, it has not been well solved due to its complexity. In this paper, we analyze the reasons for the poor performance of deep learning-based object detection methods for tiny objects in remote sensing images. Moreover, we propose a new remote sensing image tiny object detection network based on object reconstruction and multiple receptive field adaptive feature enhancement module (MRFAFEM), called ORFENet. Detailedly, object reconstruction aims to reduce the information loss of tiny objects within deep neural networks, which is only used in the training phase and can be discarded in the inference phase. MRFAFEM is designed to enhance the features for detecting tiny objects by dynamically adjusting the multiple receptive field features. We have conducted several experiments on the AI-TODv2 and LEVIR-Ship datasets, both of which are proposed for tiny object detection in remote sensing images. The experimental results indicate the effectiveness of the proposed method. Specifically, the proposed ORFENet can achieve the AP of 24.8% on the AI-TODv2 dataset and 83.3% AP50 on the LEVIR-Ship dataset. The code will be released at https://github.com/dyl96/ORFENet.

Lightweight object detection and recognition models are extremely crucial for in-orbit applications, which is the most critical factor for whether deep learning-based object detection and recognition algorithms can be applied to remote sensing satellites for real-time or near real-time processing. Global information is extremely important for object detection and recognition of remote sensing images. However, due to the high computational cost, the existing CNN-based lightweight models over-emphasize on the extraction of local information, while ignoring the global information. For this reason, we propose a lightweight object detection and recognition model (Lightweight Global-Local Detection, LGLDet) based on the especially light global modeling structure. In LGLDet, light global-local module (LGLM) is proposed to extract the global and local information. The LGLM consists of Point2Patch Non-Local (P2PNL), local branch and skip connection. Specifically, P2PNL is proposed to reduce the computation of global long-range dependency modeling. In addition, the feature fusion part and detection head are also designed in a lightweight way. In the experiments, the proposed method can achieve optimal performance with fewer parameters and lower computational complexity than existing CNN-based lightweight models and transformer-based lightweight models with similar parameters or computational complexity. The code will be released on the site of https://github.com/dyl96/LGLDet.

Hyperspectral images (HSIs) are inevitably influenced by various noise, including Gaussian noise, sparse noise and so on, which could degrade the HSIs and limit their applications greatly. Deep neural network (DNNs) based HSIs denoising methods have been widely used in recent years. However, the existing methods based on deep learning are mainly for Gaussian noise removal, and few are for mixed noise. Accordingly, we propose a two-stage cascade refined network consisting of two subnetworks for hyperspectral mixed Gaussian and sparse noise reduction. In the first stage, the spatial-spectral features are extracted by a feature extraction block based on attentions mechanism firstly. Then the multi-band noise is obtained by feeding the extracted features into the multi-band noise estimation subnetwork with encoder-decoder structure. Finally, the single-band denoising subnetwork in the second stage further refines the output of the previous subnetwork to accomplish single-band noise reduction. The experiments on HSI show that the superiority of the proposed method compared with four typical methods for mixed noise removal.

Automated shadow detection is an important research problem in the field of remote sensing image processing. The shadow regions seriously affect the interpretation of the remote sensing images. However, the existing methods have poor detection effect for small shadow regions, and the ability to distinguish between weakly illuminated regions and shadow regions is insufficient. For this reason, we propose a shadow detection network based on multiscale spatial attention mechanism for aerial remote sensing images, called MSASDNet. First, the backbone based on residual block is employed to extract the preliminary features of the input image. Then, we design a multiscale feature extraction module based on the spatial attention mechanism to extract multiscale features with spatial attention information, which can suppress the influence of complex nonshadow regions on the detection and improve the detection ability of small shadow regions. Finally, the decoder structure based on deconvolution is used to predict the shadow mask from the combined feature. Experiments performed on the Aerial Imagery dataset for Shadow Detection (AISD) dataset demonstrate the superiority of MSASDNet in terms of quantitative and qualitative comparison with several state-of-the-art methods. The code will release soon inhttps://github.com/HITLDY/MSASDNet.