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Open Access Unsupervised Deep Feature Learning for Urban Village Detection from High-Resolution Remote Sensing Images

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Urban villages (UVs) are a typical informal settlement in China resulting from the rapid urbanization in recent decades. Their formation and demolition are attracting increasing interest. In the remote sensing community, UVs have been detected based on hand-crafted features. However, the hand-crafted features just consider one or several characteristics of UVs, and ignore many effective cues hiding in the image. Recently, deep learning has been used to automatically learn suitable feature representations from a huge amount of data, without much expertise or effort in designing features. Motivated by its great success, this paper aims to use deep learning for detecting UVs. Because of the scarce labeled samples, this paper presents a novel unsupervised deep learning method to learn a data-driven feature. Experiments show the data-driven feature obtained with the proposed method outperform the existing unsupervised deep neural networks, and achieve results comparable to that obtained using the best hand-crafted features.

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Document Type: Research Article

Publication date: August 1, 2017

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  • The official journal of the American Society for Photogrammetry and Remote Sensing - the Imaging and Geospatial Information Society (ASPRS). This highly respected publication covers all facets of photogrammetry and remote sensing methods and technologies.

    Founded in 1934, the American Society for Photogrammetry and Remote Sensing (ASPRS) is a scientific association serving over 7,000 professional members around the world. Our mission is to advance knowledge and improve understanding of mapping sciences to promote the responsible applications of photogrammetry, remote sensing, geographic information systems (GIS), and supporting technologies.
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