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

Zhongwen Hu,Qian Zhang,Qingquan Li,Guofeng Wu

doi:10.3390/rs8020155

Zhongwen Hu, Qian Zhang + Show 2 more

Open Access

https://doi.org/10.3390/rs8020155

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Abstract

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 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 and synthetic aperture radar images, with different scenes and different spatial resolutions.

Highlights

Mapping landscapes is an important task for remote sensing applications
We present a novel approach for detecting built-up areas from high-resolution optical and synthetic aperture radar (SAR) images as follows: (1) A block-based method is used, in which the spectral, textural, and structural features are represented in a multi-scale framework; (2) A set of refined Harris corner points are used as training samples; (3) A new built-up index is obtained by computing, normalizing, and minimizing the feature distances to the training samples
The proposed method is composed of four main steps (Figure 1): (1) Harris corner points are detected and refined; (2) the image is subdivided into small blocks, and their spectral, textural, and structural features are extracted and represented in a multi-scale framework; (3) spectral, textural, and structural built-up indexes are obtained using a supervised approach, and a built-up index image is obtained by normalizing and minimizing the normalized spectral, textural, and structural distances; and (4) a built-up area map is obtained by threshold segmentation of the index image

Summary

Introduction

Mapping landscapes is an important task for remote sensing applications. In recent years, mapping the distribution, growth, and characteristics of built-up areas has attracted increasing attention because it can provide important information for many applications. The accurate extraction and mapping of built-up areas plays an important role in many social, economic, and environmental studies, such as urban and transport planning [1,2], environmental protection [3], and assessment, rescue, and rebuilding efforts in disaster zones [4]. Much effort has been devoted to detecting and mapping built-up areas using remote sensing images. Due to their broad coverage, coarse resolution images such as the Moderate Resolution

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