18 October 2005 Integrating geometric activity images in ANN classification
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Abstract
In this paper we demonstrate how the interaction between innovative methods in the field of computer vision and methods for multi-spectral image classification can help in extracting detailed land-cover / land-use information from Very High Resolution (VHR) satellite imagery. We introduce the novel concept of "geometric activity images", which we define as images encoding the strength of the relationship between a pixel and surrounding features detected through dedicated computer vision methods. These geometric activity images are used as alternatives to more traditional texture images that better describe the geometry of man-made structures and that can be included as additional information in a non-parametric supervised classification framework. We present a number of findings resulting from the integration of geometric activity images and multi-spectral bands in an artificial neural network classification. The geometric activity images we use result from the use of a ridge detector for straight line detection, calculated for different window sizes and for all multi-spectral bands and band-ratio images in a VHR scene. A selection of the most relevant bands to use for classification is carried out using band selection based on a genetic algorithm. Sensitivity analysis is used to assess the importance of each input variable. An application of the proposed methods to part of a Quickbird image taken over the suburban fringe of the city of Ghent (Belgium) shows that we are able to identify roads with much higher accuracy than when using more traditional multi-spectral image classification techniques.
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William De Genst, William De Genst, Sidharta Gautama, Sidharta Gautama, Rik Bellens, Rik Bellens, Frank Canters, Frank Canters, } "Integrating geometric activity images in ANN classification", Proc. SPIE 5982, Image and Signal Processing for Remote Sensing XI, 598202 (18 October 2005); doi: 10.1117/12.627520; https://doi.org/10.1117/12.627520
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