Textural analysis is now a commonly used technique in digital image processing. In this paper, we present an application of textural analysis to high resolution SPOT satellite images. The purpose of the methodology is to improve classification results, i.e. image segmentation in remote sensing. Remote sensing techniques, based on high resolution satellite data offer good perspectives for the cartography of littoral environment. Textural information contained in the pan-chromatic channel of ten meters resolution is introduced in order to separate different types of structures. The technique we used is based on statistical pattern recognition models and operates in two steps. A first step, features extraction, is derived by using a stepwise algorithm. Segmentation is then performed by cluster analysis using these extracted. features. The texture features are computed over the immediate neighborhood of the pixel using two methods : the cooccurence matrices method and the grey level difference statistics method. Image segmentation based only on texture features is then performed by pixel classification and finally discussed. In a future paper, we intend to compare the results with aerial data in view of the management of the littoral resources.

Date Published: 21 April 1986
PDF: 8 pages
Proc. SPIE 0596, Architectures and Algorithms for Digital Image Processing III, (21 April 1986); doi: 10.1117/12.952296

Published in SPIE Proceedings Vol. 0596:
Architectures and Algorithms for Digital Image Processing III
Francis J. Corbett; Howard Jay Siegel; Michael J. Duff, Editor(s)