Spectral–Spatial Depth-Based Framework for Hyperspectral Underwater Target Detection

高光谱成像 水下 计算机科学 稳健性(进化) 遥感 人工智能 模式识别(心理学) 光谱特征 地质学 生物化学 基因 海洋学 化学
作者
Qi Li,Jinghua Li,Tong Li,Zheyong Li,Pei Zhang
出处
期刊:IEEE Transactions on Geoscience and Remote Sensing [Institute of Electrical and Electronics Engineers]
卷期号:61: 1-15 被引量:8
标识
DOI:10.1109/tgrs.2023.3275147
摘要

Ocean-related research is of critical significance to the national marine military force. Hyperspectral underwater target detection (HUTD) has attracted widespread attention in recent years. However, most of the previous methods only relied on the spectral features of underwater targets and did not fully exploit their spatial characteristics. To address the issue, a spectral-spatial depth-based framework is proposed, which utilizes 3D convolution operation to capture spectral-spatial features and gains finer detection based on predicted depth. Especially, the proposed framework adopts the data transferring network to remove the noise interference by transferring the real hyperspectral data into corresponding synthetic data, which are exploited to train models. Then, considering that underwater target spectra highly depend on its depth in water, the depth estimation network is utilized to predict an accurate depth of a target, which can contribute to selecting a suitable detection network and gaining a general contour of the target. Since the underwater target spectrum is jointly determined by the target and the surrounding water column, the spectral-spatial detection network extracts the spectral-spatial features for underwater target detection. Using pool dataset, sea dataset and a synthetic HSI, we evaluate the performance of the proposed framework in terms of ROC curve and AUC value, both qualitatively and quantitatively. Meanwhile, extensive detection experiments demonstrate the robustness and effectiveness of the TDSS-UTD over several state-of-the-art methods.
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