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Novel Classification and Segmentation Techniques with Application to Remotely Sensed Images

  • B. Uma Shankar
Part of the Lecture Notes in Computer Science book series (LNCS, volume 4400)

Abstract

The article deals with some new results of investigation, both theoretical and experimental, in the area of image classification and segmentation of remotely sensed images. The article has mainly four parts. Supervised classification is considered in the first part. The remaining three parts address the problem of unsupervised classification (segmentation). The effectiveness of an active support vector classifier that requires reduced number of additional labeled data for improved learning is demonstrated in the first part. Usefulness of various fuzzy thresholding techniques for segmentation of remote sensing images is demonstrated in the second part. A quantitative index of measuring the quality of classification/ segmentation in terms of homogeneity of regions is introduced in this regard. Rough entropy (in granular computing framework) of images is defined and used for segmentation in the third part. In the fourth part a homogeneous region in an image is defined as a union of homogeneous line segments for image segmentation. Here Hough transform is used to generate these line segments. Comparative study is also made with related techniques.

Keywords

Active learning Support vector machine  Fuzzy sets Fuzzy entropy Fuzzy correlation Rough sets Rough entropy Granular computing Soft-computing Hough transform  Remotely sensed images 

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Copyright information

© Springer Berlin Heidelberg 2007

Authors and Affiliations

  • B. Uma Shankar
    • 1
  1. 1.Machine Intelligence Unit, Indian Statistical Institute, Kolkata 700 108India

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