A perception-inspired building index for automatic built-up area detection in high-resolution satellite images

Abstract

This paper addresses the problem of automatic extraction of built-up areas from high-resolution remote sensing images. We propose a new building presence index from the point view of perception. We argue that built-up areas usually result in significant corners and junctions in high-resolution satellite images, due to the man-made structures and occlusion, and thus can be measured by the geometrical structures they contained. More precisely, we first detect corners and junctions by relying on a perception-inspired corner detector, called an a-contrario junction detector. Each detected corner is associated with a perceptual significance, which measures the structural saliency of the corner in the image and is independent of the contrast and scale. All these detected corners together with their significance are then used to compute the building index. The proposed approach is evaluated on a high-resolution satellite image set, including 15 big images from GeoEye-1, QuickBird and IKONOS. The results demonstrated that our method achieves the state-of-the-art results and can be used in practical applications.

Cauchy Graph Embedding Optimization for Built-Up Areas Detection From High-Resolution Remote Sensing Images

“…However, the feature points also probably occur in the texture areas (e.g., vegetation), as reported in [15] and [16]. In order to extract a reliable Harris corner set, the high-resolution remote sensing image is first smoothed by the anisotropic diffusion [38] to suppress the false responses from the texture areas.…”

“…However, in Harris corner-based approaches [14], [15], the corner density calculation is generally time-consuming since the size of the corner set is often very large. In addition, the size of the feature point set is often very large, which may also happen in [13] and [16]. On the other hand, the overwhelming majority of existing approaches adopt a global statistical threshold to segment the feature map for the final extraction, which is unable to output the accurate boundaries of the built-up areas.…”

“…Fonte et al [12] concluded that the corner detectors can indicate the structure of man-made objects in remote sensing images from 2.0-m to 0.17-m resolution. Afterward, the Gabor-based local detector [13], the improved Harris corner detectors [14], [15], and the a contrario junction detector [16] were adopted to calculate the density of the local feature points as the presence index of the built-up areas. Sirmacek and Ünsalan [17] utilized the SIFT descriptors to form the graph, and proposed a multiple subgraph matching method to detect urban areas.…”

An Index Based on Joint Density of Corners and Line Segments for Built-Up Area Detection from High Resolution Satellite Imagery

“…However, forested areas, which contain high PanTex values due to tree shadows, are subject to be taken as built-up areas. Building-density-based approaches: Huang and Zhang [14] propose a building detection method using the difference of morphological profiles, and the corresponding building-density-based feature is employed to extract the built-up areas in [7]. However, the building extraction itself is still a difficult problem and faces great challenges, and it often fails to extract built-up areas.…”

“…However, the building extraction itself is still a difficult problem and faces great challenges, and it often fails to extract built-up areas. Corner-density-based approaches: The local key point features such as SIFT (Scale Invariant Feature Transform) [15], local feature point extraction using Gabor filters [3], junctions [7] and Harris corners [16] are widely employed to detect built-up areas. To improve the detection accuracy, the literature presents some variants of corner detection methods such as improved Harris [2] and modified Harris for edges and corners [17].…”