Sugeno Fuzzy-Inference-System-Based Land Cover Classification of Remotely Sensed Images

Sugeno Fuzzy-Inference-System-Based Land Cover Classification of Remotely Sensed Images

Jenicka S (Anna University Chennai, India)
DOI: 10.4018/978-1-5225-2990-3.ch015
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Abstract

Accuracy of land cover classification in remotely sensed images relies on the features extracted and the classifier used. Texture features are significant in land cover classification. Traditional texture models capture only patterns with discrete boundaries whereas fuzzy patterns need to be classified by assigning due weightage to uncertainty. When remotely sensed image contains noise, the image may have fuzzy patterns characterizing land covers and fuzzy boundaries separating land covers. So a fuzzy texture model is proposed for effective classification of land covers in remotely sensed images and the model uses Sugeno Fuzzy Inference System (FIS). Support Vector Machine (SVM) is used for precise and fast classification of image pixels. Hence it is proposed to use a hybrid of fuzzy texture model and SVM for land cover classification of remotely sensed images. In this chapter, land cover classification of IRS-P6, LISS-IV remotely sensed image is performed using multivariate version of the proposed texture model.
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Background

Texture is a surface property that characterizes the intensity variations in each local neighborhood of an image. For classifying a single pixel, in contrast to pixel based techniques that considers the intensity of the current pixel alone, texture based techniques operate on local neighborhood. The objective of the chapter is to propose a fuzzy texture model and hybridize it with support vector machine (SVM) for land cover classification of remotely sensed images.

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