LAUSR

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 the 3-D convolution operation to capture spectral–spatial features and gains finer detection based on predicted depth. In particular, 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 a pool dataset, sea dataset, and synthetic hyperspectral image (HSI), we evaluate the performance of the proposed framework in terms of receiver operating characteristic (ROC) curve and area under the ROC curve (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.