The predecessor of China Optics was China Optics and Applied Optics Digest, which was founded in 1985. At that time, it was the only retrieval journal in the field of optics in China. At the end of 2008, China Optics and Applied Optics Digest was renamed 《中国光学》的前身是1985年创办的《中国光学与应用光学文摘》，当时是国内光学领域唯一的检索刊物。2008年底《中国光学与应用光学文摘》转型更名为《中国光学与应用光学》期刊，刊载内容突破了文摘的限制，正式成为学术刊物。

Deep learning-based hyperspectral image (HSI) classification has significant applications in remote sensing scene understanding. Whole-image propagation classification methods can achieve high-precision classification at the full-pixel level with a significant efficiency advantage. However, such methods generally face challenges such as the smoothness of high-frequency details and difficulty in efficiently modeling deep features. To address these issues, this article proposes a fast multikernel graph convolutional network (FMGCN) for fast whole-image classification. The model designs a multikernel graph convolution (MKGC) algorithm to enhance the expression ability of deep spectral–spatial features. This algorithm generalizes the graph convolution process from a single kernel propagation process to a propagation process with an arbitrary number of kernels to more fully exploit spectral–spatial features in non-Euclidean spaces. In addition, a symmetric compressed refinement network (SCRN) is constructed to restore the weakened local structural information in the whole-image propagation, and an embedded edge compensation strategy (EECS) is combined to further strengthen the high-frequency features in the boundary regions. Based on this design, FMGCN is able to capture more comprehensive global dependency information in a single whole-image propagation, achieving a uniform balance of high accuracy and high efficiency. Experiments on four representative hyperspectral datasets show that FMGCN achieves the best classification performance, effectively alleviates over-smoothing in complex structural scenarios, and outperforms batch propagation models in terms of inference efficiency.

Structured pruning has become a mainstream approach for network lightweighting due to its advantages in terms of compression efficiency and deployment friendliness. Existing methods generally struggle to generate a complete Pareto front and cannot easily incorporate practical inference performance metrics directly into the optimization objective. This significantly constrains their capacity to recommend pruning subnet schemes and prevents them from adequately satisfying the actual requirements of specific tasks. To address the aforementioned challenges, we propose DRL-Pruner, a pruning framework guided by multi-objective reinforcement learning that caters to diverse budget optimization requirements. Leveraging the powerful generative capability of diffusion model, this method gradually iteratively refines randomly initialized pruning strategy into compression schemes that can fully cover the entire objective space. We statistically measure data such as the global computational variation and structural sensitivity of the network as state representation, providing the pruning framework with an observation set of structural states and performance trends. For remote sensing vision tasks, we conduct pruning experiments on ResNet-50, ViT-Tiny, and Faster R-CNN under the scene classification benchmark NaSC-TG2 and the object detection benchmark NWPU VHR-10 (v2). The results demonstrate that DRL-Pruner achieves an effective tradeoff between network accuracy and various inference budgets, providing pruning decisions that cover the Pareto front.

Deep learning has achieved tremendous success in the field of object detection. The efficient detection of infrared small targets using deep learning methods remains a challenging task. Infrared small targets are often detected in high-resolution features. Extracting high-level semantic features layer by layer in the network may lead to the loss of deep-layer targets. However, performing global detection on high-resolution feature maps results in high computational costs. To address this issue, we propose the aware distribute and sparse network (ADSNet) to preserve deep-layer small target features while accelerating inference speed. Specifically, we design the aware fusion distribute module (AFD) to aggregate global features and enhance the representation capability of deep-layer features. Subsequently, the aware cascaded sparse module (ACS) is utilized to guide step-by-step high-resolution feature sparsification. Experimental results demonstrate that the proposed method achieves accurate segmentation in various detection scenarios and for diverse target morphologies, effectively suppressing false alarms while controlling computational expenses. Ablation experiments further validate the effectiveness of each component.