Abstract
Multiscale segmentation is a key prerequisite step for object-based classification methods. However, it is often not possible to determine a sole optimal scale for the image to be classified because in many cases different geo-objects and even an identical geo-object may appear at different scales in one image. In this paper, an object-based classification method based on mutliscale segmentation results in the framework of topic modelling is proposed to classify VHR satellite images in an entirely unsupervised fashion. In the stage of topic modelling, grayscale histogram distributions for each geo-object class and each segment are learned in an unsupervised manner from multiscale segments. In the stage of classification, each segment is allocated a geo-object class label by the similarity comparison between the grayscale histogram distributions of each segment and each geo-object class. Experimental results show that the proposed method can perform better than the traditional methods based on topic modelling.
Highlights
Recent advances in remote sensor technology, those relating to spatial resolution, are helping to make detailed observation of the earth’s surface possible
For object-based image analysis (OBIA), a key prerequisite step is to perform image segmentation, which aims to produce a set of non-overlapping segments
This kind of methods conduct a series of image segmentations at multiple scales in scale space and sometimes propagates from coarse to fine scales followed by selecting an optimal scale for classification task (Drăguţ et al, 2014). These methods have been widely used, there exist some problems in their practical applications: 1) the determination of an optimal scale is often difficult; 2) it is often not possible to solely determine one optimal scale for the image to be classified because in many cases different geoobjects and even an identical geo-object may appear at different scales in one image. These observations motivate us to develop a new OBIA approach based on multiscale segmentation results, in which multiple segmentations are jointly utilized by means of topic modeling
Summary
Recent advances in remote sensor technology, those relating to spatial resolution, are helping to make detailed observation of the earth’s surface possible. These methods have been widely used, there exist some problems in their practical applications: 1) the determination of an optimal scale is often difficult; 2) it is often not possible to solely determine one optimal scale for the image to be classified because in many cases different geoobjects and even an identical geo-object may appear at different scales in one image These observations motivate us to develop a new OBIA approach based on multiscale segmentation results, in which multiple segmentations are jointly utilized by means of topic modeling. This paper presents a novel OBIA approach based on multiscale segmentation results using latent Dirichlet allocation model to classify VHR satellite images in an entirely unsupervised fashion.
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