The intricate structure of hyperspectral images comprising hundreds of successive spectral bands makes it challenging for conventional approaches to quickly and precisely classify this information. The classification performance of hyperspectral images has substantially improved in the past decade with the emergence of deep-learning-based techniques. Due to convolutional neural networks’(CNNs) excellent feature extraction and modeling, they have become a robust backbone network for hyperspectral image classification. However, CNNs fail to adequately capture the dependency and contextual information of the sequence of spectral properties due to the restrictions inherent in their fundamental network characteristics. We analyzed hyperspectral image classification from a frequency-domain angle to tackle this issue and proposed a split-frequency filter network. It is a simple and effective network architecture that improves the performance of hyperspectral image classification through three critical operations: a split-frequency filter network, a detail-enhancement layer, and a nonlinear unit. Firstly, a split-frequency filtering network captures the interactions between neighboring spectral bands in the frequency domain. The classification performance is then enhanced using a detail-improvement layer with a frequency-domain attention technique. Finally, a nonlinear unit is incorporated into the frequency-domain output layer to expedite training and boost performance. Experiments on various hyperspectral datasets demonstrate that the method outperforms other state-of-art approaches (an overall accuracy(OA) improvement of at least 2%), particularly when the training sample is insufficient.

The spatial resolution of an infrared focal plane polarization detection system is limited by the structure of the detector, resulting in lower resolution than the actual array size. To overcome this limitation and improve imaging resolution, we propose an infrared polarization super-resolution reconstruction model based on sparse representation, optimized using Stokes vector images. This model forms the basis for our method aimed at achieving super-resolution reconstruction of infrared polarization images. In this method, we utilize the proposed model to initially reconstruct low-resolution images in blocks. Subsequently, we perform a division by weight, followed by iterative back projection to enhance details and achieve high-resolution reconstruction results. As a supplement, we establish a near-real-time short-wave infrared time-sharing polarization system for data collection. The dataset was acquired to gather prior knowledge of the over-complete basis set and to generate a series of simulated focal plane images. Simulation experimental results demonstrate the superiority of our method over several advanced methods in objective evaluation indexes, exhibiting strong noise robustness in quantitative experiments. Finally, to validate the practical application of our method, we establish a split-focal plane polarization short-wave infrared system for scene testing. Experimental results confirm the effective processing of actual captured data by our method.

Hyperspectral Imagery (HSI) plays a crucial role in military and civilian target detection. However, HSI target detection remains highly challenging due to the interference caused by complex and diverse real-world scenarios. This paper proposes a Spectral Information Divergence-driven Diffusion Network model (SID-DN) for hyperspectral target detection, which significantly enhances detection robustness in complex scenes by decoupling background distribution modeling from target detection. The proposed method focuses on learning the background distribution in hyperspectral data and achieves target detection by accurately reconstructing background samples to identify differences between background and target samples. This method introduces an adaptive coarse detection module, which optimizes the coarse detection process in generative hyperspectral target detection, effectively reducing the background-target misclassification. Additionally, a SID-based Diffusion model is designed to optimize the loss of Diffusion, effectively reducing the interference of suspected target samples during the background learning process. Experiments on three real-world datasets demonstrate that the method is highly competitive, with detection results significantly outperforming current state-of-the-art methods.