Many deep learning models, such as convolutional neural network (CNN) and recurrent neural network (RNN), have been successfully applied to extracting deep features for hyperspectral tasks. Hyperspectral image classification allows distinguishing the characterization of land covers by utilizing their abundant information. Motivated by the attention mechanism of the human visual system, in this study, we propose a spectral-spatial attention network for hyperspectral image classification. In our method, RNN with attention can learn inner spectral correlations within a continuous spectrum, while CNN with attention is designed to focus on saliency features and spatial relevance between neighboring pixels in the spatial dimension. Experimental results demonstrate that our method can fully utilize the spectral and spatial information to obtain competitive performance.

This paper investigates the problem of detecting a multichannel signal embedded in subspace interference and Gaussian noise. The interference lies in a known subspace but with unknown coordinates, while the noise, in the general sense, consisting of thermal noise and clutter, has an unknown covariance matrix. To estimate the covariance matrix, it is assumed that there are sufficient signal-free training data. Two cases are considered, namely, the homogeneous environment (HE) and partially homogenous environment (PHE). The test and training data in the HE have the same noise covariance matrix, while the test and training data in the PHE share the same noise covariance matrix up to an unknown scaling factor. We derive the corresponding Wald tests both in the HE and PHE. Remarkably, these two detectors reject the clutter by using the sample covariance matrix, and then reject interference and integrate signal simultaneously through the oblique projection. Numerical examples show that the two proposed detectors can provide better detection performance than existing detectors in some situations.

In infrared and visible image fusion, existing methods typically have a prerequisite that the source images share the same resolution. However, due to limitations of hardware devices and application environments, infrared images constantly suffer from markedly lower resolution compared with the corresponding visible images. In this case, current fusion methods inevitably cause texture information loss in visible images or blur thermal radiation information in infrared images. Moreover, the principle of existing fusion rules typically focuses on preserving texture details in source images, which may be inappropriate for fusing infrared thermal radiation information because it is characterized by pixel intensities, possibly neglecting the prominence of targets in fused images. Faced with such difficulties and challenges, we propose a novel method to fuse infrared and visible images of different resolutions and generate high-resolution resulting images to obtain clear and accurate fused images. Specifically, the fusion problem is formulated as a total variation (TV) minimization problem. The data fidelity term constrains the pixel intensity similarity of the downsampled fused image with respect to the infrared image, and the regularization term compels the gradient similarity of the fused image with respect to the visible image. The fast iterative shrinkage-thresholding algorithm (FISTA) framework is applied to improve the convergence rate. Our resulting fused images are similar to super-resolved infrared images, which are sharpened by the texture information from visible images. Advantages and innovations of our method are demonstrated by the qualitative and quantitative comparisons with six state-of-the-art methods on publicly available datasets.

The generalized direction detection (GDD) problem involves determining the presence of a signal of interest within matrix-valued data, where the row and column spaces of the signal (if present) are known, but the specific coordinates are unknown. Many detectors have been proposed for GDD, yet there is a lack of analytical results regarding their statistical detection performance. This paper presents a theoretical analysis of two adaptive detectors for GDD in scenarios with known spatial steering vectors. Specifically, we establish their statistical distributions and develop closed-form expressions for both detection probability (PD) and false alarm probability (PFA). Simulation experiments are carried out to validate the theoretical results, demonstrating good agreement between theoretical and simulated results.

This paper investigates the problem of distributed target detection with signal mismatch in homogeneous environment. Precisely, the actual signal steering vector is not aligned with the nominal one which is assumed by radar system. Within this framework, we propose a tunable detector which can adjust its directivity (robustness or selectivity) flexibly and effectively by changing a tunable parameter. Moreover, this tunable detector encompasses the generalized Kelly's generalized likelihood ratio test (GKGLRT) and the generalized adaptive matched filter (GAMF) as its two special cases. Finally, to test the effectiveness of this detector, the real data received by the IPIX radar are used for experiments. The results illustrate the superiority of the proposed detector both in simulation environment and realistic environment.

This study deals with the problem of covariance matrix estimation for radar sensor signal detection applications with insufficient secondary data in non-Gaussian clutter. According to the Euclidean mean, the authors combined an available prior covariance matrix with the persymmetric structure covariance estimator, symmetric structure covariance estimator, and Toeplitz structure covariance estimator, respectively, to derive three knowledge-aided structured covariance estimators. At the analysis stage, the authors assess the performance of the proposed estimators in estimation accuracy and detection probability. The analysis is conducted both on the simulated data and real sea clutter data collected by the IPIX radar sensor system. The results show that the knowledge-aided Toeplitz structure covariance estimator (KA-T) has the best performance both in estimation and detection, and the knowledge-aided persymmetric structure covariance estimator (KA-P) has similar performance with the knowledge-aided symmetric structure covariance estimator (KA-S). Moreover, compared with existing knowledge-aided estimator, the proposed estimators can obtain better performance when secondary data are insufficient.