In recent years, convolutional neural networks (CNNs) have been widely used in hyperspectral image (HSI) classification due to their exceptional performance in local feature extraction. However, due to the local join and weight sharing properties of the convolution kernel, CNNs have limitations in long-distance modeling, and deeper networks tend to increase computational costs. To address these issues, this paper proposes a vision Transformer (VIT) based on the light self-Gaussian-attention (LSGA) mechanism, which extracts global deep semantic features. Firstly, the hybrid spatial-spectral Tokenizer module extracts shallow spatial-spectral features and expands image patches to generate Tokens. Next, the light self-attention uses Q (Query), X (Origin input), and X instead of Q, K (Key), and V (Value) to reduce the computation and parameters. Furthermore, to avoid the lack of location information resulting in the aliasing of central and neighborhood features, we devise Gaussian absolute position bias to simulate HSI data distribution and make the attention weight closer to the central query block. Several experiments verify the effectiveness of the proposed method, which outperforms state-of-the-art methods on four datasets. Specifically, we observed a 0.62% accuracy improvement over A2S2K and a 0.11% improvement over SSFTT. In conclusion, the proposed LSGA-VIT method demonstrates promising results in HSI classification and shows potential in addressing the issues of location-aware long-distance modeling and computational cost. Our codes are available at https://github.com/machao132/LSGA-VIT.

Infrared small target detection plays a crucial role in maritime security. However, detecting small targets within heavy sea clutter environments remains challenging. Existing methods often fail to deliver satisfactory performance in the presence of substantial clutter interference. This paper analyzes the spatial–temporal appearance characteristics of small targets and sea clutter. Based on this analysis, we propose a novel detection method based on the appearance stable isotropy measure (ASIM). First, the original images are processed using the Top-Hat transformation to obtain the salient regions. Next, a preliminary threshold operation is employed to extract the candidate targets from these salient regions, forming a candidate target array image. Third, to distinguish between small targets and sea clutter, we introduce two characteristics: the gradient histogram equalization measure (GHEM) and the local optical flow consistency measure (LOFCM). GHEM evaluates the isotropy of the candidate targets by examining their gradient histogram equalization, while LOFCM assesses their appearance stability based on local optical flow consistency. To effectively combine the complementary information provided by GHEM and LOFCM, we propose ASIM as a fusion characteristic, which can effectively enhance the real target. Finally, a threshold operation is applied to determine the final targets. Experimental results demonstrate that our proposed method exhibits superior comprehensive performance compared to baseline methods.

Infrared target tracking plays an important role in both civil and military fields. The main challenges in designing a robust and high-precision tracker for infrared sequences include overlap, occlusion and appearance change. To this end, this paper proposes an infrared target tracker based on proximal robust principal component analysis method. Firstly, the observation matrix is decomposed into a sparse occlusion matrix and a low-rank target matrix, and the constraint optimization is carried out with an approaching proximal norm which is better than L1-norm. To solve this convex optimization problem, Alternating Direction Method of Multipliers (ADMM) is employed to estimate the variables alternately. Finally, the framework of particle filter with model update strategy is exploited to locate the target. Through a series of experiments on real infrared target sequences, the effectiveness and robustness of our algorithm are proved.