A sea-land segmentation algorithm based on multi-feature fusion for a large-field remote sensing image

Jing Li; Weixin Xie; Jihong Pei

doi:10.1117/12.2285779

8 March 2018 A sea-land segmentation algorithm based on multi-feature fusion for a large-field remote sensing image

Jing Li, Weixin Xie, Jihong Pei

Proceedings Volume 10611, MIPPR 2017: Remote Sensing Image Processing, Geographic Information Systems, and Other Applications; 106110I (2018) https://doi.org/10.1117/12.2285779
Event: Tenth International Symposium on Multispectral Image Processing and Pattern Recognition (MIPPR2017), 2017, Xiangyang, China

Abstract

Sea-land segmentation is one of the key technologies of sea target detection in remote sensing images. At present, the existing algorithms have the problems of low accuracy, low universality and poor automatic performance. This paper puts forward a sea-land segmentation algorithm based on multi-feature fusion for a large-field remote sensing image removing island. Firstly, the coastline data is extracted and all of land area is labeled by using the geographic information in large-field remote sensing image. Secondly, three features (local entropy, local texture and local gradient mean) is extracted in the sea-land border area, and the three features combine a 3D feature vector. And then the MultiGaussian model is adopted to describe 3D feature vectors of sea background in the edge of the coastline. Based on this multi-gaussian sea background model, the sea pixels and land pixels near coastline are classified more precise. Finally, the coarse segmentation result and the fine segmentation result are fused to obtain the accurate sea-land segmentation. Comparing and analyzing the experimental results by subjective vision, it shows that the proposed method has high segmentation accuracy, wide applicability and strong anti-disturbance ability.

Citation Download Citation

Jing Li, Weixin Xie, and Jihong Pei "A sea-land segmentation algorithm based on multi-feature fusion for a large-field remote sensing image ", Proc. SPIE 10611, MIPPR 2017: Remote Sensing Image Processing, Geographic Information Systems, and Other Applications, 106110I (8 March 2018); https://doi.org/10.1117/12.2285779

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