2018
DOI: 10.3390/rs10111809
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Multi-Scale Object Histogram Distance for LCCD Using Bi-Temporal Very-High-Resolution Remote Sensing Images

Abstract: To improve the performance of land-cover change detection (LCCD) using remote sensing images, this study utilises spatial information in an adaptive and multi-scale manner. It proposes a novel multi-scale object histogram distance (MOHD) to measure the change magnitude between bi-temporal remote sensing images. Three major steps are related to the proposed MOHD. Firstly, multi-scale objects for the post-event image are extracted through a widely used algorithm called the fractional net evaluation approach. The… Show more

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Cited by 16 publications
(11 citation statements)
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References 40 publications
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“…In the proposed CD framework, the co-segmentation using the fractal net evolution approach (FNEA) is applied directly to the stacked bitemporal images to create spatially corresponding objects. FNEA is a region growing algorithm based on a minimum heterogeneity criteria and builds a multi-scale hierarchical structure by merging the neighboring image objects [57,58]. The segment parameters for the FNEA-based segmentation are adjusted and determined with the aid of the ESP tool in this paper.…”
Section: Deep Difference Feature Extractionmentioning
confidence: 99%
See 1 more Smart Citation
“…In the proposed CD framework, the co-segmentation using the fractal net evolution approach (FNEA) is applied directly to the stacked bitemporal images to create spatially corresponding objects. FNEA is a region growing algorithm based on a minimum heterogeneity criteria and builds a multi-scale hierarchical structure by merging the neighboring image objects [57,58]. The segment parameters for the FNEA-based segmentation are adjusted and determined with the aid of the ESP tool in this paper.…”
Section: Deep Difference Feature Extractionmentioning
confidence: 99%
“…In the experiments, five unsupervised CD methods are selected as the comparison algorithms to verify the advantages of the proposed CD approach, including the classic PCA-K-Means method [17], multi-scale superpixel and deep neural networks (MSDNN) for CD [61], region-based level set evolution (RLSE) method [26], multi-scale object histogram distance (MOHD) method [57], and the object-based unsupervised CD based on the SVM method, denoted as object-based SVM (OSVM) [25].…”
Section: Evaluation Criteria and Experimental Settingsmentioning
confidence: 99%
“…Using multi-source data, the Special Issue includes papers focusing on deep learning [3][4][5], multi-angle image processing [6], multi-source image fusion in heterogeneous environments [2,3,5], and object-based image analysis [5,7,8]. These interesting techniques applied various change detection applications, while most of them simultaneously mined the change information from the spectral, spatial, and temporal domains.…”
Section: Overview Of Contributionsmentioning
confidence: 99%
“…The object-based automatic land-cover change detection proposed by Lv et al [8] followed the unsupervised paradigm of bi-temporal feature difference. In this work, an object-level bin-to-bin histogram distance was adapted to measure the change magnitude between the pairwise objects of the bi-temporal images, which presented superiority to the current unsupervised spectral-spatial change detection techniques.…”
Section: Overview Of Contributionsmentioning
confidence: 99%
“…Image processing methods based on histograms are also widely used in the field of object detection. Lv et al [23] proposed a novel multi-scale object histogram distance (MOHD) to detect the target region of the remote sensing image. The method first calculates the frequency histogram of the image, then uses the bin-to-bin distance to measure the target change, and uses the Otsu algorithm to complete the target area segmentation.…”
Section: Introductionmentioning
confidence: 99%