A novel method of river detection for high resolution remote sensing image based on corner feature and SVM

  • Authors:

  • Ziheng Tian;Chengdong Wu;Dongyue Chen;Xiaosheng Yu;Li Wang

  • Affiliations:

  • College of Information Science & Engineering, Northeastern University, Shenyang, China;College of Information Science & Engineering, Northeastern University, Shenyang, China;College of Information Science & Engineering, Northeastern University, Shenyang, China;College of Information Science & Engineering, Northeastern University, Shenyang, China;College of Information Science & Engineering, Northeastern University, Shenyang, China

  • Venue:
  • ISNN'12 Proceedings of the 9th international conference on Advances in Neural Networks - Volume Part II
  • Year:
  • 2012

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Abstract

In this paper, a new method to detect rivers in high resolution remote sensing images based on corner feature and Support Vector Machine (SVM) is presented. It introduces corner feature into river detection for the first time. First, we detect corners in sample images and test images, and extract image corner feature with all the corners detected above. Then the corner feature and other feature of sample images, for example texture feature and entropy feature, are input into SVM for training. At last we obtain the water decision function, with which we classify each pixel into river region or background region. This method comprehensively utilizes the corner, entropy and texture feature of remote sensing images. Experimental results show that this method performances well in river automatic detection of remote sensing images.