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A Framework for Automatic Building Detection from Low-Contrast Satellite Images

Aamir, Muhammad ; Pu, Yi-Fei ; Rahman, Ziaur ; Tahir, Muhammad ; Naeem, Hamad ; Dai, Qiang

Symmetry (Basel), 2019-01, Vol.11 (1), p.3 [Periódico revisado por pares]

Basel: MDPI AG

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Título:
A Framework for Automatic Building Detection from Low-Contrast Satellite Images
Autor: Aamir, Muhammad ; Pu, Yi-Fei ; Rahman, Ziaur ; Tahir, Muhammad ; Naeem, Hamad ; Dai, Qiang
Assuntos: Algorithms ; Brain ; building extraction ; Buildings ; Computer vision ; Deep learning ; Digitization ; Discrete Wavelet Transform ; DWT–SVD ; High resolution ; high-resolution satellite imagery ; Image contrast ; Image enhancement ; image equalization ; Image resolution ; low-contrast satellite image ; Object recognition ; perceptual grouping ; Remote sensing ; Satellite imagery ; Satellites ; Segments ; Singular value decomposition ; Technology adoption ; Wavelet transforms
É parte de: Symmetry (Basel), 2019-01, Vol.11 (1), p.3
Descrição: Building detection in satellite images has been considered an essential field of research in remote sensing and computer vision. There are currently numerous techniques and algorithms used to achieve building detection performance. Different algorithms have been proposed to extract building objects from high-resolution satellite images with standard contrast. However, building detection from low-contrast satellite images to predict symmetrical findings as of past studies using normal contrast images is considered a challenging task and may play an integral role in a wide range of applications. Having received significant attention in recent years, this manuscript proposes a methodology to detect buildings from low-contrast satellite images. In an effort to enhance visualization of satellite images, in this study, first, the contrast of an image is optimized to represent all the information using singular value decomposition (SVD) based on the discrete wavelet transform (DWT). Second, a line-segment detection scheme is applied to accurately detect building line segments. Third, the detected line segments are hierarchically grouped to recognize the relationship of identified line segments, and the complete contours of the building are attained to obtain candidate rectangular buildings. In this paper, the results from the method above are compared with existing approaches based on high-resolution images with reasonable contrast. The proposed method achieves high performance thus yields more diversified and insightful results over conventional techniques.
Editor: Basel: MDPI AG
Idioma: Inglês

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A Framework for Automatic Building Detection from Low-Contrast Satellite Images

Aamir, Muhammad ; Pu, Yi-Fei ; Rahman, Ziaur ; Tahir, Muhammad ; Naeem, Hamad ; Dai, Qiang

Basel: MDPI AG

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