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Building change detection via a combination of CNNs using only RGB aerial imageries
Author(s): Keisuke Nemoto; Ryuhei Hamaguchi; Masakazu Sato; Aito Fujita; Tomoyuki Imaizumi; Shuhei Hikosaka
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Paper Abstract

Building change information extracted from remote sensing imageries is important for various applications such as urban management and marketing planning. The goal of this work is to develop a methodology for automatically capturing building changes from remote sensing imageries. Recent studies have addressed this goal by exploiting 3-D information as a proxy for building height. In contrast, because in practice it is expensive or impossible to prepare 3-D information, we do not rely on 3-D data but focus on using only RGB aerial imageries. Instead, we employ deep convolutional neural networks (CNNs) to extract effective features, and improve change detection accuracy in RGB remote sensing imageries. We consider two aspects of building change detection, building detection and subsequent change detection. Our proposed methodology was tested on several areas, which has some differences such as dominant building characteristics and varying brightness values. On all over the tested areas, the proposed method provides good results for changed objects, with recall values over 75 % with a strict overlap requirement of over 50% in intersection-over-union (IoU). When the IoU threshold was relaxed to over 10%, resulting recall values were over 81%. We conclude that use of CNNs enables accurate detection of building changes without employing 3-D information.

Paper Details

Date Published: 4 October 2017
PDF: 12 pages
Proc. SPIE 10431, Remote Sensing Technologies and Applications in Urban Environments II, 104310J (4 October 2017); doi: 10.1117/12.2277912
Show Author Affiliations
Keisuke Nemoto, PASCO Corp. (Japan)
Ryuhei Hamaguchi, PASCO Corp. (Japan)
Masakazu Sato, PASCO Corp. (Japan)
Aito Fujita, PASCO Corp. (Japan)
Tomoyuki Imaizumi, PASCO Corp. (Japan)
Shuhei Hikosaka, PASCO Corp. (Japan)


Published in SPIE Proceedings Vol. 10431:
Remote Sensing Technologies and Applications in Urban Environments II
Thilo Erbertseder; Nektarios Chrysoulakis; Ying Zhang; Wieke Heldens, Editor(s)

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