The detection of camouflaged targets in the complex background is a hot topic of current research. Existing hyperspectral target detection algorithms do not take advantage of spatial information and rarely use temporal information. It is difficult to obtain the required targets, and the detection performance in hyperspectral sequences with complex background will be low. Therefore, a hyperspectral time-series target detection method based on spectral perception and spatial-temporal tensor decomposition (SPSTT) is proposed. Firstly, a sparse target perception strategy based on spectral matching is proposed. To initially acquire the sparse targets, the matching results are adjusted by using the correlation mean of the prior spectrum, the pixel to be measured and the four-neighborhood pixel spectra. The separation of target and background is enhanced by making full use of local spatial structure information through local topology graph representation of the pixel to be measured. Secondly, in order to obtain a more accurate rank and make full use of temporal continuity and spatial correlation, a spatial-temporal tensor model based on the Gamma norm and L _2,1 norm is constructed. Furthermore, an excellent alternating direction method of multipliers is proposed to solve this model. Finally, spectral matching is fused with spatial-temporal tensor decomposition in order to reduce false alarms and retain more right targets. A 176-band hyperspectral image sequence (BIT-HSIS-I) dataset is collected for the hyperspectral target detection task. It is found by testing on the collected dataset that the proposed SPSTT has superior performance over the state-of-the-art algorithms.

Band selection is an effective method to reduce redundancy in a hyperspectral image (HSI) without compromising the original contents. Popular band selection methods usually use strong assumptions, such as linear or nonlinear assumptions with simple predefined Kernel functions, to model the correlations between bands. However, this kind of strong assumption may not valid in the real environment due to the complex interactions between bands. In this letter, we treat hyperspectral band selection as a spectral reconstruction task. By assuming that an HSI can be sparsely reconstructed from a few informative bands, we propose an attention-based autoencoder to model the underlying nonlinear interdependencies between bands. The proposed model consists of two parts: an attention module and an autoencoder. The attention module is used to produce the attention mask which selects the most informative bands for every pixel. The autoencoder uses these informative bands to reconstruct the raw HSI. The final band selection is conducted via clustering column vectors of the attention mask and exploring the most representative band for each cluster. Different from most of the existing band selection methods, the proposed method directly learns global nonlinear correlations between bands without strong assumptions. The proposed model is easy to implement and all the parameters can be jointly optimized using the stochastic gradient descend algorithm. Experiments on three open public data sets show that the proposed method offers the promising results.

Object detection in remote sensing is a challenging task due to the arbitrary orientations of objects and the vast variation in the number of objects within a single image. For instance, one image may contain hundreds of small vehicles, while another may only have a single football field. Recently, DEtection TRansformer (DETR) and its variants have achieved great success in object detection by setting a fixed number of object queries and using bipartite graph matching for one-to-one label assignment. However, we have observed that bipartite graph matching can result in relative redundancy of object queries when the number of objects changes dramatically in an image. This relative redundancy can cause two problems: slower convergence during training and redundant bounding boxes during inference. To analyze the aforementioned problems, we proposed a metric, Redundancy of Object Query (ROQ), to quantitatively analyze the redundancy. Through experiments, we discovered that the reason for the two issues is the difficulty in distinguishing between high-quality negative samples and positive samples. In this paper, we proposed Efficient-Matching Oriented Object Detection with Transformers(EMO2-DETR) consisting of three dedicated components to address the aforementioned issues. Specifically, Reassign Bipartite Graph Matching (RBGM) is proposed to extract high-quality negative samples from the negative samples. And Ignored Sample Predicted Head (ISPH) is proposed to predict high-quality negative samples. Then, Reassigned Hungarian loss is used to better involve high-quality negative samples in the update of model parameters. Extensive experiments on DOTAv1 and DOTAv1.5 datasets demonstrated that our proposed method achieves competitive results.

Hyperspectral target detection technology is becoming more and more important in remote sensing. Most of the traditional methods of target sensing using spectral information treat hyperspectral image as 2-D matrix regardless of the structure information of hyperspectral image, resulting in insufficient separation of target and background and limited detection accuracy. In order to better utilize the hyperspectral intrinsic structural information, this article proposes a hyperspectral target sensing approach based on tensor adaptive reconstruction cascaded with global and local feature fusion (TRGLF). First, the Tucker decomposition and reconstruction method is utilized to alleviate the influence of noise and other elements in the complex background hyperspectral, which can maintain the intrinsic structural features of the data and improve the background and target separation. The principal components of the factor matrix are determined by a logarithmic singular value summation strategy, and then the energy difference between the spectra to be measured and the previous spectra is calculated to fine-tune the principal components and obtain more appropriate principal component values. Second, on the basis of reconstructed data, a global and local spatial spectral fusion method is adopted to obtain the target of interest. This includes using globally constrained energy minimization to obtain the target of interest and using local differential sensing to further obtain the target boundary. The detection performed on four realistically acquired hyperspectral water surface datasets demonstrate the excellent detection performance of the proposed target detection method.

Despite the promising performance on benchmark datasets that deep convolutional neural networks have exhibited in single image super-resolution (SISR), there are two underlying limitations to existing methods. First, current supervised learning-based SISR methods for remote sensing satellite imagery do not use paired real sensor data, instead operating on simulated high-resolution (HR) and low-resolution (LR) image-pairs (typically HR images with their bicubic-degraded LR counterparts), which often yield poor performance on real-world LR images. Second, SISR is an ill-posed problem, and the super-resolved image from discriminatively trained networks with l p norm loss is an average of the infinite possible HR images, thus, always has low perceptual quality. Though this issue can be mitigated by generative adversarial network (GAN), it is still hard to search in the whole solution-space and find the best solution. In this paper, we focus on real-world application and introduce a new multisensor dataset for real-world remote sensing satellite imagery super-resolution. In addition, we propose a novel conditional GAN scheme for SISR task which can further reduce the solution-space. Therefore, the super-resolved images have not only high fidelity, but high perceptual quality as well. Extensive experiments demonstrate that networks trained on the introduced dataset can obtain better performances than those trained on simulated data. Additionally, the proposed conditional GAN scheme can achieve better perceptual quality while obtaining comparable fidelity over the state-of-the-art methods.