Representation of Block-Based Image Features in a Multi-Scale Framework for Built-Up Area Detection

Representation of Block-Based Image Features in a Multi-Scale Framework for Built-Up Area Detection

Blog Article

The accurate extraction and mapping of built-up areas play an important role in many social, economic, and environmental studies.In this paper, we propose a novel approach for built-up area detection from high spatial resolution remote sensing images, using a block-based multi-scale feature representation framework.First, an image is divided into small Ready Meals blocks, in which the spectral, textural, and structural features are extracted and represented using a multi-scale framework; a set of refined Harris corner points is then used to select blocks as training samples; finally, a built-up index image is obtained by minimizing the normalized spectral, textural, and structural distances to the training samples, and a built-up area map is obtained by thresholding the index image.

Experiments confirm that the proposed approach is effective for high-resolution optical COCONUT FOOT CREME and synthetic aperture radar images, with different scenes and different spatial resolutions.