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Edge detection in remote sensing images based on cluster information
Author(s)
Chumsamrong, Warin
Rataseree, Yongyot
Rangsanseri, Yuttapong
Thitimajshima, Punya
Peanvijarnpong, Chanchai
Date Issued
December 1, 1999
Type
Conference Paper
Abstract
In this paper, a multispectral image edge detection algorithm is proposed based on the idea that uses global multispectral information to guide local gradient computation. The image is first segmented into a small number of clusters through a clustering algorithm. According to these clusters, a set of linear projection vectors are generated. For a given image, if n clusters are found, there are n(n-1)/2 possible projection vectors. Edge detection is performed by calculating gradient magnitudes separately on each channel. An appropriate projection vector is chosen for each pixel to maximize gradient magnitude. In this way, edges are treated as transitions from one cluster to another. The algorithm has been tested on JERS-1/OPS images, and the experimental results demonstrate its potential usefulness.
Citation
International Geoscience and Remote Sensing Symposium IGARSS, 2, 1250-1252, 1999