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[1]ZHANG Yuguo,WANG Fei,SUN Wenyi,et al.Terrace Information Extraction From SPOT Remote Sensing Image Based on Object-oriented Classification Method[J].Research of Soil and Water Conservation,2016,23(06):345-351.

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Terrace Information Extraction From SPOT Remote Sensing Image Based on Object-oriented Classification Method

Research of Soil and Water Conservation[ISSN 1005-3409/CN 61-1272/P] Volume: 23 Number of periods: 2016 06 Page number: 345-351 Column: Public date: 2016-12-28

Title:: Terrace Information Extraction From SPOT Remote Sensing Image Based on Object-oriented Classification Method

Author(s):: ZHANG Yuguo¹, WANG Fei^1,2,3, SUN Wenyi², AN Chunchun^2,3; 1. College of Natural Resources and Environment, Northwest A & F University, Yangling, Shaanxi 712100, China;
2. Institute of Soil and Water Conservation, Chinese Academy of Sciences and Ministry of Water Resources, Yangling, Shaanxi 712100, China;
3. University of Chinese Academy of Sciences, Beijing 100049, China

Keywords:: terrace; object-oriented classification; information extraction; remote sensing image; Loess Plateau

CLC:: TP79;S343.3

DOI:: -

Abstract:: Accurate and rapid extraction of terrace is one of the core technologies for the dynamic monitoring and evaluation of soil and water conservation at a regional scale, and remote sensing technology provides an effective and efficient means to extract land cover information. In this paper, image segementation and rule based feature extraction were conducted to interpret the terrace information from the high spatial-resolution SPOT5 imageries in Yan’gou watershed on the Loess Plateau based on object-oriented classification method. Firstly, the image objects were set through the image segmentation. Secondly, the rules for terrace information extraction from remote sensing datasets were established via the analysis of spectral, texture and spatial feature of image objects. In this case, the automatic extraction of terrace was achieved. Finally, the accuracy of extraction results was evaluated through its comparison with visual interpretation results. The results showed that the position of terrace in complex geomorphic regions could be successfully identified through the object-oriented classification method, and the overall accuracy reached to 78.38%. The method developed in this paper is expected to provide a reference for the interpretation of terrace information from remote sensing imageries across the Loess Plateau.

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