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.
Highlights
Recent advances in remote sensing technology, those relating to spatial resolution, are helping to make detailed observations of the Earth’s surface possible
We compare the performance of different approaches for three typical of geographical scenes in terms of both qualitative and quantitative aspects
To evaluate the effectiveness on three aspects of classification accuracy, spatial smoothness, and semantic consistency, the performance of the proposed approach is compared with that of four state-of-the-art unsupervised classification methods based on image segmentation: (1) the spectral-spatial ISODATA, where the pixel-based ISODATA classification is followed by a majority voting within the adaptive neighborhoods defined by the over-segmentation [34]; (2) the spectral-spatial latent Dirichlet allocation (LDA), similar to O_ISODATA, where the same over-segmentation is applied to the classification result of the LDA model using just the single-scale image segmentation map as corpus [30]; (3) the msLDA proposed in [27]; and (4) the HDP_IBP proposed in [30]
Summary
Recent advances in remote sensing technology, those relating to spatial resolution, are helping to make detailed observations of the Earth’s surface possible. It is widely acknowledged that, compared to their pixel-based counterparts, object-based classification methods, which can take advantage of both spectral and spatial information, are probably more appropriate for VHR satellite images [2,3,4]. In a typical object-based classification framework, Remote Sens. The classification accuracy of object-based classification is dependent, to a large extent, on the quality of image segmentation [5]. As pointed out by Dragut et al [6], the SP controls the average segment size, i.e., a smaller value of the SP produces segmentations with small regions and detailed structures, and a larger value allows for more merges, preserving only large segments and coarse features. The SP needs to be appropriately determined in order to create segments that can match the actual boundaries of landscape features of various sizes as much as possible [7]. There exist several problems in practical applications: (1) the determination of the optimal SP, in many cases, still relies on a trial-and-error optimization, which is time-consuming when it applies to complex image scenes [8]; (2) it is often impossible to determine a unique optimal
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