Hyperspectral Target Detection Based on Prior Spectral Perception and Local Graph Fusion

高光谱成像 计算机科学 人工智能 模式识别(心理学) 图形 计算机视觉 理论计算机科学
作者
Xiaobin Zhao,Jun Huang,Yunquan Gao,Qingwang Wang
出处
期刊:IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing [Institute of Electrical and Electronics Engineers]
卷期号:17: 13936-13948 被引量:3
标识
DOI:10.1109/jstars.2024.3439560
摘要

With the development of hyperspectral sensing technology, hyperspectral target detection technology plays an important role in remote target detection. However, existing hyperspectral target detection models are poorly adapted to complex backgrounds and mainly focus on the spectral domain, making less use of spatial structure information leading to low target detection rates. Therefore, a new target detection algorithm based on the prior spectral perception and local graph fusion (SPLGF) is proposed. Firstly, the prior spectrum-guided target extraction method is established. This method can take full advantage of the background and target spectral information by local inner and outer window linkage, reduce the impact of spectral variability on target acquisition performance, and improve detection stability. Secondly, the target enhancement strategy based on the Gabor multi-feature graph is proposed. This technique makes full use of multi-directional and multi-scale spatial information, which can reduce the influence of brightness, contrast and amplitude variation on detection performance due to light and angle. Finally, spatial-spectral fusion is executed to achieve target detection. It can make full use of spectral and spatial structure information to improve the target detection effect. Publicly available datasets and real collected datasets are adopted to check the validity of the proposed method. After comparison, it is found that the proposed algorithm has better detection effect than existing baseline methods. The maximum improvement in AUC values are 16.56%-88.16% across the eight datasets.
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