An Unmixing-Based Network for Underwater Target Detection From Hyperspectral Imagery

Detecting underwater targets from hyperspectral imagery makes a profound impact on marine exploration. Available methods mainly tackle this problem by modifying the land-based detection algorithms with classical bathymetric models, which usually fail to remove the interference of background and ignore the effect of depth information, leading to a poor detection performance. To achieve a more precise result, in this work we propose a novel network based on hyperspectral unmixing (HU) methodology and bathymetric models to detect the desired underwater targets. The proposed network, called underwater target detection network (UTD-Net), first develops a novel joint anomaly detector with classical HU methods to separate out target-water mixed pixels, which is devoted to eliminate the adverse influence of background. Then, we explore a bathymetric model-based autoencoder to unmix the target-water mixed pixels for acquiring the target-associated abundance values and maps. One dimension convolutional neural network is exploited to construct the encoder part of above autoencoder for the sake of addressing spectral variability problem. Moreover, considering the physical meaningless endmembers issue, a particular spectral constraint is imposed on the objective function as a training guidance. In this way, the autoencoder would be capable of generating specific endmembers and their corresponding abundance maps. Finally, according to the physical essence of abundance maps, we figure out the detection result by fusing the outcomes of autoencoder with weight coefficients determined by abundance values. Qualitative and quantitative illustrations demonstrate the effectiveness and efficiency of UTD-Net in comparison with the state-of-the-art underwater target detection methods.