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Detecting Large-Scale Urban Land Cover Changes from Very High Resolution Remote Sensing Images Using CNN-Based Classification

1,†, 1,†, 1,* and 2
1
School of Remote Sensing and Information Engineering, Wuhan University, 129 Luoyu Road, Wuhan 430079, China
2
Department of Physical Geography, Faculty of Geoscience, Utrecht University, Princetonlaan 8, 3584 CB Utrecht, The Netherlands
*
Author to whom correspondence should be addressed.
Co-first author.
ISPRS Int. J. Geo-Inf. 2019, 8(4), 189; https://doi.org/10.3390/ijgi8040189
Received: 9 March 2019 / Revised: 5 April 2019 / Accepted: 9 April 2019 / Published: 11 April 2019
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Abstract

The study investigates land use/cover classification and change detection of urban areas from very high resolution (VHR) remote sensing images using deep learning-based methods. Firstly, we introduce a fully Atrous convolutional neural network (FACNN) to learn the land cover classification. In the FACNN an encoder, consisting of full Atrous convolution layers, is proposed for extracting scale robust features from VHR images. Then, a pixel-based change map is produced based on the classification map of current images and an outdated land cover geographical information system (GIS) map. Both polygon-based and object-based change detection accuracy is investigated, where a polygon is the unit of the GIS map and an object consists of those adjacent changed pixels on the pixel-based change map. The test data covers a rapidly developing city of Wuhan (8000 km2), China, consisting of 0.5 m ground resolution aerial images acquired in 2014, and 1 m ground resolution Beijing-2 satellite images in 2017, and their land cover GIS maps. Testing results showed that our FACNN greatly exceeded several recent convolutional neural networks in land cover classification. Second, the object-based change detection could achieve much better results than a pixel-based method, and provide accurate change maps to facilitate manual urban land cover updating. View Full-Text
Keywords: classification; change detection; convolutional neural networks; Atrous convolution; very-high-resolution remote sensing images classification; change detection; convolutional neural networks; Atrous convolution; very-high-resolution remote sensing images
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Zhang, C.; Wei, S.; Ji, S.; Lu, M. Detecting Large-Scale Urban Land Cover Changes from Very High Resolution Remote Sensing Images Using CNN-Based Classification. ISPRS Int. J. Geo-Inf. 2019, 8, 189.

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