Topic Modelling for Object-Based Classification of VHR Satellite Images Based on Multiscale Segmentations

Shen, Li; Wu, Linmei; Li, Zhipeng

doi:10.5194/isprsarchives-xli-b7-359-2016

Cited by 4 publications

(1 citation statement)

References 6 publications

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“…This paper extends and improves on a preliminary work [31], which presents our initial ideas and results. In this paper: (1) a novel strategy of integrating multiple classification results at different scales into a unique one is added, which can ensure an adaptive smoothing classification result can be achieved; (2) a constraint specified by the mixture distribution of geo-objects, which can characterize the co-occurrence relationships of various geo-objects, is incorporated to correct the KL similarity between the histogram distribution of each segment and that of each geo-object; and (3) a more thorough presentation of introduction, methodology, experimental analysis, and discussion is conducted.…”

Section: Introductionmentioning

confidence: 64%

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

Shen

Dai

et al. 2017

Remote Sensing

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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.

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Section: Introductionmentioning

confidence: 64%