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Open AccessArticle

Urban Land-Cover Classification Using Side-View Information from Oblique Images

by 1,2, 2,3,* and 1
1
Future Cities Laboratory, Singapore-ETH Centre, ETH Zurich, 1 Create Way, CREATE Tower, #06-01, Singapore 138602, Singapore
2
Department of Civil, Environmental and Geodetic Engineering, The Ohio State University, Columbus, OH 43210, USA
3
Department of Electrical and Computer Engineering, The Ohio State University, Columbus, OH 43210, USA
*
Author to whom correspondence should be addressed.
Remote Sens. 2020, 12(3), 390; https://doi.org/10.3390/rs12030390
Received: 17 December 2019 / Revised: 23 January 2020 / Accepted: 23 January 2020 / Published: 26 January 2020
(This article belongs to the Special Issue Feature-Based Methods for Remote Sensing Image Classification)
Land-cover classification on very high resolution data (decimetre-level) is a well-studied yet challenging problem in remote sensing data processing. Most of the existing works focus on using images with orthographic view or orthophotos with the associated digital surface models (DSMs). However, the use of the nowadays widely-available oblique images to support such a task is not sufficiently investigated. In the effort of identifying different land-cover classes, it is intuitive that information of side-views obtained from the oblique can be of great help, yet how this can be technically achieved is challenging due to the complex geometric association between the side and top views. We aim to address these challenges in this paper by proposing a framework with enhanced classification results, leveraging the use of orthophoto, digital surface models and oblique images. The proposed method contains a classic two-step of (1) feature extraction and (2) a classification approach, in which the key contribution is a feature extraction algorithm that performs simplified geometric association between top-view segments (from orthophoto) and side-view planes (from projected oblique images), and joint statistical feature extraction. Our experiment on five test sites showed that the side-view information could steadily improve the classification accuracy with both kinds of training samples (1.1% and 5.6% for evenly distributed and non-evenly distributed samples, separately). Additionally, by testing the classifier at a large and untrained site, adding side-view information showed a total of 26.2% accuracy improvement of the above-ground objects, which demonstrates the strong generalization ability of the side-view features. View Full-Text
Keywords: land-cover classification; side-view; oblique image; photogrammetry land-cover classification; side-view; oblique image; photogrammetry
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MDPI and ACS Style

Xiao, C.; Qin, R.; Ling, X. Urban Land-Cover Classification Using Side-View Information from Oblique Images. Remote Sens. 2020, 12, 390. https://doi.org/10.3390/rs12030390

AMA Style

Xiao C, Qin R, Ling X. Urban Land-Cover Classification Using Side-View Information from Oblique Images. Remote Sensing. 2020; 12(3):390. https://doi.org/10.3390/rs12030390

Chicago/Turabian Style

Xiao, Changlin; Qin, Rongjun; Ling, Xiao. 2020. "Urban Land-Cover Classification Using Side-View Information from Oblique Images" Remote Sens. 12, no. 3: 390. https://doi.org/10.3390/rs12030390

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