The International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences
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Articles | Volume XLII-3/W6
Int. Arch. Photogramm. Remote Sens. Spatial Inf. Sci., XLII-3/W6, 609–615, 2019
https://doi.org/10.5194/isprs-archives-XLII-3-W6-609-2019
Int. Arch. Photogramm. Remote Sens. Spatial Inf. Sci., XLII-3/W6, 609–615, 2019
https://doi.org/10.5194/isprs-archives-XLII-3-W6-609-2019

  26 Jul 2019

26 Jul 2019

FEATURE EXTRACTION FOR URBAN AND AGRICULTURAL DOMAINS USING ECOGNITION DEVELOPER

M. Rana and S. Kharel M. Rana and S. Kharel
  • Regional Remote Sensing Centre-South, NRSC, Indian Space Research Organisation, Bengaluru, India

Keywords: Classification, Rule Based, Support Vector machine, Feature Space Optimization, KNN

Abstract. Feature extraction has always been a challenging task in Geo-Spatial studies both in urban areas as well as in agricultural areas. After the evolution of eCognition Developer, different segmentation techniques and classification algorithms which help in automating feature extraction have been developed in recent years which have been a boon for scientists and people conducting research in the field of geomatics. This research reflects a study depicting the potential of eCognition Developer in extracting features in Agricultural as well as urban areas using various classification techniques. Rule Based and SVM Classification techniques were used for feature extraction in urban areas whereas Feature Space Optimization and K-Nearest Neighbor were used for classifying agricultural features. Results reflect that rule based classification yields more accurate results for urban areas whereas Feature Space Optimization along with object–based classification gave more accuracy in case of agricultural areas.