Imaging Science and Photochemistry ›› 2016, Vol. 34 ›› Issue (2): 190-197.DOI: 10.7517/j.issn.1674-0475.2016.02.190

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An Edge Detection Method for Remote Sensing Images in Corn Field Areas Based on Blocky Fuzzy Enhancement

LIANG Ruofei1, YANG Fengbao1, WANG Yimin2, LI Dawei1, FENG Peipei1   

  1. 1. Information and Communication Engineering College, North University of China, Taiyuan 030051, Shanxi, P. R. China;
    2. Remote Sensing Center of Agriculture of Shanxi Province, Taiyuan 030051, Shanxi, P. R. China
  • Received:2015-07-29 Revised:2015-11-14 Online:2016-03-15 Published:2016-03-15

Abstract:

This paper proposed a corn field segmentation method for GF-1 satellite remote sensing image based on blocky fuzzy enhancement and min edge extraction. This proposed method improves the accuracy of statistics of corn field area by solving the complexity of spectrum and reducing fuzziness of field edges. First, the color remote sense image was transformed from RGB format into I1I2I3 format, and the monochrome figure I1 which has rich characteristics was gotten. Then the image I1 was enhanced by blocks with fuzzy theory, and the edge of I1 was extracted by using min algorithm. Last, edge was optimized through Full Lambda-Schedule algorithm. Proposed method was compared with Canny and Sobel algorithm through experiments. The results showed that proposed method was effective in segmenting corn fields and detecting edge of color remote sensing image. The complexity and fuzziness were reduced effectively, and the results were more inline with the actual characteristics of corn distribution. Also the accuracy of statistics cornfield area was improved.

Key words: blocky fuzzy enhancement, edge detection, corn fields parcel segmentation