Proceedings ArticleDOI
Multi-scale segmentation for remote sensing imagery based on minimum heterogeneity rule
Ryad Malik,Radja Kheddam,Aichouche Belhadj-Aissa +2 more
- pp 87-91
TLDR
A multi-scale segmentation method based on Minimum Heterogeneity Rule (MHR) for merging objects is presented and results show that this method can easily adapt its scale parameter to different scale image analysis tasks and any chosen scale object-extraction of interest.Abstract:
Image segmentation is an essential step toward higher level image processing in remote sensing. However, the traditional image segmentation approaches based on pixels spectral characteristics and single-scale image information extraction methods have obvious flaws in this respect. Currently, multi-scale image segmentation is seen as a promising alternative of traditional segmentation method and is one of the most useful approaches in object oriented classification of remotely sensed images. In this paper, we present a multi-scale segmentation method based on Minimum Heterogeneity Rule (MHR) for merging objects. Segmentation results show that this method can easily adapt its scale parameter to different scale image analysis tasks and any chosen scale object-extraction of interest.read more
Citations
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A New Approach to Urban Road Extraction Using High-Resolution Aerial Image
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Object-oriented SVM classifier for ALSAT-2A high spatial resolution imagery: A case study of algiers urban area
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Patent
Automated method for determining optimal segmentation result of remote sensing image
TL;DR: In this paper, an automated method for determining an optimal segmentation result of a remote sensing image is proposed, which comprises the following steps of (1) establishing measurement indexes for spectral uniformity in segmented objects and spectral heterogeneity between adjacent segmentsed objects by utilizing a spectral information discretization degree index, and then establishing an image quality function; (2) performing analysis by adopting a variational method to obtain an overall optimal segmentations result of the image; and (3) establishing a segmented object heterogeneity degree measurement index.
Proceedings ArticleDOI
Toward an optimal object-oriented image classification using SVM and MLLH approaches
TL;DR: Comparative analysis clearly revealed that higher overall classification accuracy was observed in the object-based classification using the optimal segmentation scale, and the determination of suitable object segmentation Scale leading to an improved object-oriented classification result is discussed and performed.
Patent
Segmentation method and device for urban functional area in remote sensing image
TL;DR: In this article, the authors presented a segmentation method and device for an urban functional area in a remote sensing image, which comprises the steps of obtaining a heterogeneity increment between any two adjacent objects; according to the heterogeneity increment and the self-adaptive segmentation scale, carrying out selfadaptively segmentation; iteratively combining all objects in the target remote sensing images to obtain an urban function area.
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