HJ-1 satellite remote sensing image segmentation in the oil spill of Mexico Gulf base on the non-negative matrix factorization and support vector machine
FAN Jian-chao1,2, WANG Tao1,3
1. National Marine Environmental Monitoring Center, Dalian 116023, China;
2. School of Mathematical Science, Dalian University of Technology, Dalian 116023, China;
3. School of Economics & Management, Dalian Ocean University, Dalian 116023, China
Abstract：To improve self-made satellites in the marine oil spill monitoring accuracy,it is presented that an environmental mitigation satellite (HJ-1) marine oil spill remote sensing image classification algorithm based on non-negative matrix factorization and support vector machine algorithm.Focusing on remote sensing images of HJ-1 satellite,a non-negative matrix factorization algorithm is adopted 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,based on the new features,the support vector machine is employed for remote sensing image classification.It remedies the problem of small sample training.Simulation results of the Gulf of Mexico oil spill event substantiate the effectiveness of the proposed approach for HJ-1 satellite image classification.
FAN Jian-chao,WANG Tao. HJ-1 satellite remote sensing image segmentation in the oil spill of Mexico Gulf base on the non-negative matrix factorization and support vector machine[J]. Marine Environmental Science, 2015, 34(3): 441-446.
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