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Proceedings Paper

A BP neural network for the sea ice recognition using SAR Image
Author(s): Jubai An; Liang Shuai
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Paper Abstract

This article proposed a method for the recognition of the sea ice in a SAR image. It's based on the Artificial Neural Network (ANN). We use a BP neural network model incorporating with image texture features extracted by Gray Level Co-occurrence Matrix from the SAR image. The BP neural network fed with the feature vector of SAR image presents the analysis of texture features and outputs the estimation results of the sea ice. The BP neural network is trained using sample data set to the neural network. And then the BP neural network trained is tested to recognize sea ice in a SAR image waiting for the classification . The results of tests show that the BP network model for the sea ice recognition in SAR image is feasible. The BP network output shows the recognition accuracy of the model for the sea ice recognition in SAR image can be 86%. Among the 4 directions for computing the texture features, the most valuable direction is 0°, the next is 90°, and then is 135°. The two most distinguishing texture features are inertia moment and entropy.

Paper Details

Date Published: 19 May 2006
PDF: 8 pages
Proc. SPIE 6199, Remote Sensing and Space Technology for Multidisciplinary Research and Applications, 61990D (19 May 2006); doi: 10.1117/12.673662
Show Author Affiliations
Jubai An, Dalian Maritime Univ. (China)
Liang Shuai, Dalian Maritime Univ. (China)


Published in SPIE Proceedings Vol. 6199:
Remote Sensing and Space Technology for Multidisciplinary Research and Applications
Qingxi Tong; Xiuwan Chen; Allen Huang; Wei Gao, Editor(s)

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