Oil spill GF-1 remote sensing image segmentation using an evolutionary feedforward neural network

To improve self-made satellites in the marine oil spill monitoring accuracy, it is presented that a Gao Fen (GF-1) satellite marine oil spill remote sensing (RS) image classification algorithm based on a novel evolutionary neural network. First, a non-negative matrix factorization (NMF) algorithm is employed to extract the image features. Compared with basic features, such as the image spectrum and texture, structuring more targeted oil spill image localization non-negative character fits better for the physical significance of remote sensing images. Furthermore, on the basis of the new features, a new feedforward neural network structure with particle swarm optimization (PSO) algorithm is proposed for GF-1 RS image segmentation. Simulation results of the oil spill event substantiate the effectiveness of the proposed approach to GF-1 satellite image segmentation.