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Remote Sens. 2017, 9(8), 840; doi:10.3390/rs9080840

Topic Modelling for Object-Based Unsupervised Classification of VHR Panchromatic Satellite Images Based on Multiscale Image Segmentation

1,2,* , 1,2
,
1,2
,
1,2
and
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1
State-Province Joint Engineering Laboratory of Spatial Information Technology for High-Speed Railway Safety, Southwest Jiaotong University, Chengdu 611756, China
2
Faculty of Geosciences and Environmental Engineering, Southwest Jiaotong University, Chengdu 611756, China
*
Author to whom correspondence should be addressed.
Academic Editor: Qi Wang
Received: 31 May 2017 / Revised: 10 August 2017 / Accepted: 10 August 2017 / Published: 14 August 2017
(This article belongs to the Collection Learning to Understand Remote Sensing Images)
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Abstract

Image segmentation is a key prerequisite for object-based classification. However, it is often difficult, or even impossible, to determine a unique optimal segmentation scale due to the fact that various geo-objects, and even an identical geo-object, present at multiple scales in very high resolution (VHR) satellite images. To address this problem, this paper presents a novel unsupervised object-based classification for VHR panchromatic satellite images using multiple segmentations via the latent Dirichlet allocation (LDA) model. Firstly, multiple segmentation maps of the original satellite image are produced by means of a common multiscale segmentation technique. Then, the LDA model is utilized to learn the grayscale histogram distribution for each geo-object and the mixture distribution of geo-objects within each segment. Thirdly, the histogram distribution of each segment is compared with that of each geo-object using the Kullback-Leibler (KL) divergence measure, which is weighted with a constraint specified by the mixture distribution of geo-objects. Each segment is allocated a geo-object category label with the minimum KL divergence. Finally, the final classification map is achieved by integrating the multiple classification results at different scales. Extensive experimental evaluations are designed to compare the performance of our method with those of some state-of-the-art methods for three different types of images. The experimental results over three different types of VHR panchromatic satellite images demonstrate the proposed method is able to achieve scale-adaptive classification results, and improve the ability to differentiate the geo-objects with spectral overlap, such as water and grass, and water and shadow, in terms of both spatial consistency and semantic consistency. View Full-Text
Keywords: very high resolution (VHR) satellite image; topic modelling; object-based image analysis; image segmentation; unsupervised classification; multiscale representation very high resolution (VHR) satellite image; topic modelling; object-based image analysis; image segmentation; unsupervised classification; multiscale representation
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Shen, L.; Wu, L.; Dai, Y.; Qiao, W.; Wang, Y. Topic Modelling for Object-Based Unsupervised Classification of VHR Panchromatic Satellite Images Based on Multiscale Image Segmentation. Remote Sens. 2017, 9, 840.

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