Hyperspectral anomaly detection (HAD) plays a vital role in military and civilian applications. However, compared with target detection or classification tasks, HAD is more challenging due to insufficient anomaly information and the difficulty of extracting local and global discriminative features. In this letter, a Convolutional Transformer-inspired Autoencoder (CTA) is proposed for HAD. The CTA consists of a clustering-based module and an autoencoder-based module. First, note that the number of anomalies is small and distinct from their surroundings, a clustering-based module is proposed to detect the pseudo background and anomaly samples. Second, the autoencoder module is composed of an encoder and a decoder formed from several skip-connected convolutions and multi-head attention-based transformers. The CTA is trained not only to distinguish the anomalies from the background but also to reconstruct the input hyperspectral images. Benefiting from integrating the convolution and transformer, the CTA has local and global receptive fields. Moreover, both background and anomaly information explored by the clustering-based module can be adopted to improve the separability of anomalies. Experiments on two hyperspectral datasets demonstrate that the proposed CTA achieves superior detection performance to its counterparts. The code is available at https://github.com/hzhdhz/CTA.

Deep learning (DL)-based video satellite superresolution (SR) methods have recently yielded superior performance over traditional model-based methods by using an end-to-end manner. Existing DL-based methods usually assume that the blur kernels are known and, thus, do not model the blur kernels during restoration. However, this assumption is rarely held for real satellite videos and leads to oversmoothed results. In this article, we propose a Ghost module-based convolution network model for blind SR of satellite videos. The proposed Ghost module-based video SR (GVSR) method, which assumes that the blur kernel is unknown, consists of two main modules, i.e., the preliminary image generation module and the SR results’ reconstruction module. First, the motion information from adjacent video frames and the wrapped images are explored by an optical flow estimation network, the blur kernel is flexibly obtained by a blur kernel estimation network, and the preliminary high-resolution image is generated by feeding both blur kernel and wrapped images. Second, a reconstruction network consisting of three paths with attention-based Ghost (AG) bottlenecks is designed to remove artifacts in the preliminary image and obtain the final high-quality SR results. Experiments conducted on Jilin-1 and OVS-1 satellite videos demonstrate that the qualitative and quantitative performance of our proposed method is superior to current state-of-the-art methods.

Video satellite can generate video image sequences with rich dynamic information, thus providing a new way for monitoring moving objects. However, to maintain high temporal resolution, video satellite images usually sacrifice their spatial resolution. Therefore, super-resolution (SR) plays a vital role in improving the quality of video satellite images. In this article, we propose a multiframe video SR neural network (MVSRnet) for video satellite image SR reconstruction. The proposed MVSRnet consists of three main subnetworks: an optical flow estimation subnetwork (OFEnet), an upscaling subnetwork (Upnet) and an attention-based residual learning subnetwork (ARLnet). The OFEnet aims to estimate low-resolution (LR) optical flow of multiple image frames. Upnet is then constructed to enhance the resolution of both input frames and the estimated LR optical flows. Motion compensation is subsequently performed according to the high-resolution (HR) optical flows. Finally, the compensated HR cube is fed to the ARLnet to generate SR results. Different from existing video satellite image SR methods, the proposed MVSRnet is a multiframe-based method with an attention mechanism, which can merge the motion information among adjacent frames and highlight the importance of extracted features. Experiments conducted on Jilin-1 and OVS-1 video satellite images demonstrate that the proposed MVSRnet significantly outperforms some state-of-the-art SR methods.

Tree species classification is beneficial to multiple applications but is difficult because the categories should be discriminated by subtle differences, and the acquisition of class labels is both expensive and time-consuming. In this letter, a bilinear squeeze-and-excitation network (BiSENet) is proposed for fine-grained classification of tree species. First, objects of the remote sensing data are constructed based on superpixel segmentation. Second, a deep neural network (i.e., BiSENet) is constructed and trained to distinguish different tree species. The proposed BiSENet is inspired by the fine-grained image classification, which is more subtle than traditional classification since it classifies the images within a subordinate category. Moreover, the AdaBound optimization method is adopted to obtain the optimal parameters of BiSENet. Experiments on the Haizhu Lake data acquired by the Jilin-1 satellite demonstrate that the proposed method exhibits superior quantitative and qualitative performance than existing state-of-the-art methods.

Super-resolution (SR) has attracted increasing attention as it can improve the quality of video satellite images. Most previous studies only consider several integer magnification factors and focus on obtaining a specific SR model for each scale factor. However, in the real world, it is a common requirement to zoom the videos arbitrarily by rolling the mouse wheel. In this article, we propose a unified network for arbitrary scale SR (ASSR) of video satellite images. The proposed ASSR consists of two modules, i.e., feature learning module and arbitrary upscale module. The feature learning module accepts multiple low-resolution (LR) frames and extracts useful features of those frames by using many 3-D residual blocks. The arbitrary upscale module takes the extracted features as input and enhances the spatial resolution by subpixel convolution and bicubic-based adjustment. Different from existing video satellite image SR methods, ASSR can continuously zoom LR video satellite images with arbitrary integer and noninteger scale factors in a single model. Experiments have been conducted on real video satellite images acquired by Jilin-1 and OVS-1. Quantitative and qualitative results have demonstrated that ASSR has superior reconstruction performance compared with the state-of-the-art SR methods.