Object-oriented change detection approach for high-resolution remote sensing images based on multiscale fusion

Wang, Chao; Xu, Mengxi; Wang, Xin; Zheng, Shengnan; Ma, Zhiyi

doi:10.1117/1.jrs.7.073696

Cited by 21 publications

(8 citation statements)

References 26 publications

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“…The algorithm, which is based on modified evidence combination, can reduce the high contradiction using different classifiers [7]. Wang et al [30] introduced the method of multi-scale image segmentation based on the D-S evidence theory and weighted data fusion. The D-S evidence theory was used as a fusion strategy in the proposed approach to significantly diminish the segmentation error rate [30].…”

Section: Introductionmentioning

confidence: 99%

Semi-supervised classification framework of hyperspectral images based on the fusion evidence entropy

Wang

et al. 2017

Multimed Tools Appl

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Increasing attention is being paid to the classification of ground objects using hyperspectral spectrometer images. A key challenge of most hyperspectral classifications is the cost of training samples. It is difficult to acquire enough effective marked label sets using classification model frameworks. In this paper, a semi-supervised classification framework of hyperspectral images is proposed to better solve problems associated with hyperspectral image classification. The proposed method is based on an iteration process, making full use of the small amount of labeled data in a sample set. In addition, a new unlabeled data trainer in the self-training semi-supervised learning framework is explored and implemented by estimating the fusion evidence entropy of unlabeled samples using the minimum trust evaluation and maximum uncertainty. Finally, we employ different machine learning classification methods to compare the classification performance of different hyperspectral images. The experimental results indicate that the proposed approach outperforms traditional state-of-the-art methods in terms of low classification errors and better classification charts using few labeled samples.

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Section: Introductionmentioning

confidence: 99%

Semi-supervised classification framework of hyperspectral images based on the fusion evidence entropy

Wang

et al. 2017

Multimed Tools Appl

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“…Currently, SRR has been widely used in medicine, remote sensing, military surveillance, image compression and other imaging fields [3][4][5][6]. The concept of image SRR was firstly proposed by Tsai and Huang [7], then gradually developed [8].…”

Section: Introductionmentioning

confidence: 99%

Super-Resolution Reconstruction based on Tukey Norm and Adaptive Bilateral Total Variation

Shen¹,

Xu²,

Xu³

et al. 2013

IJSIP

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In Bilateral Total Variation (BTV) regularized super-resolution reconstruction (SRR), the fidelity item is only applicable to a specific noise model, and the fixed weight of BTV regularization term cannot adapt to the changes in an image. Thus, this paper proposes a SRR algorithm based on the Tukey fidelity term and adaptive BTV regularization term. The Tukey fidelity term has a more effective outliers suppression feature to deal with complex noises, and the weight of adaptive BTV regularization term can resize itself according to the changes of image textures, which can achieve the purposes of suppressing noises and preserving edges. Experimental results show that, compared with other algorithms, the proposed algorithm has better vision effects and higher Peak Signal-to-noise Ratio (PSNR) values.

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“…The object based methods divide the image into the objects composed of homogeneous pixels, and the features of the objects are extracted for comparison to detect changes (Wei et al, 2010, Wang et al, 2013. The disadvantage of these methods is that the existing segmentation algorithms have limitations, so it is difficult to achieve the best segmentation for the same class.…”

Section: Introductionmentioning

confidence: 99%

A Framework of Change Detection Based on Combined Morphologica Features and Multi-Index Classification

Zhang

Yang

2017

Int. Arch. Photogramm. Remote Sens. Spatial Inf. Sci.

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ABSTRACT:Remote sensing images are particularly well suited for analysis of land cover change. In this paper, we present a new framework for detection of changing land cover using satellite imagery. Morphological features and a multi-index are used to extract typical objects from the imagery, including vegetation, water, bare land, buildings, and roads. Our method, based on connected domains, is different from traditional methods; it uses image segmentation to extract morphological features, while the enhanced vegetation index (EVI), the differential water index (NDWI) are used to extract vegetation and water, and a fragmentation index is used to the correct extraction results of water. HSV transformation and threshold segmentation extract and remove the effects of shadows on extraction results. Change detection is performed on these results. One of the advantages of the proposed framework is that semantic information is extracted automatically using low-level morphological features and indexes. Another advantage is that the proposed method detects specific types of change without any training samples. A test on ZY-3 images demonstrates that our framework has a promising capability to detect change.

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Object-oriented change detection approach for high-resolution remote sensing images based on multiscale fusion

Cited by 21 publications

References 26 publications

Semi-supervised classification framework of hyperspectral images based on the fusion evidence entropy

Semi-supervised classification framework of hyperspectral images based on the fusion evidence entropy

Super-Resolution Reconstruction based on Tukey Norm and Adaptive Bilateral Total Variation

A Framework of Change Detection Based on Combined Morphologica Features and Multi-Index Classification

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