Hyperspectral Marine Oil Spill Detection Network With Enhanced Superpixel Segmentation and Attention Mechanisms

In recent years, marine oil spills have occurred frequently, causing serious damage to the marine ecological environment. Hyperspectral images (HSIs) can provide rich spectral and spatial information, and have broad development prospects in marine oil spill detection. This article proposes a hyperspectral marine oil spill detection network, HMOSDN, that integrates improved superpixel segmentation and a mixed attention mechanism (MAM). First, to deal with the extensive clutter and diverse morphology of marine oil spill areas in HSIs, we propose a new superpixel segmentation algorithm based on improved simple linear iterative clustering (ISLIC), which achieves preliminary extraction of spatial features and reduces spatial noise via a Gaussian filter and a pixel intensity smoothing technique (PIST). Then, to further fuse spectral and spatial features and strengthen the feature mining and utilization of fused information, we design a spectral-spatial feature extraction network with an MAM, MAM-SSFEN, which adds a spectral attention module and a spatial attention module, further improving the performance of the deep feature extraction network for oil spill detection. Experiments on the hyperspectral oil spill database (HOSD) demonstrate that our proposed method, HMOSDN, outperforms several other detection techniques regarding area under the curve (AUC) and recall evaluation metrics.