A robust multi-kernel change detection framework for detecting leaf beetle defoliation using Landsat 7 ETM+ data

Anees, Asim; Aryal, Jagannath; O’Reilly, Małgorzata M.; Gale, Timothy J.; Wardlaw, Tim

doi:10.1016/j.isprsjprs.2016.10.011

Cited by 13 publications

(20 citation statements)

References 63 publications

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“…Besides thresholding, many researchers used classifiers, e.g., Naïve Bayes [43] and support vector machine [44], to determine the final result. In addition, a non-parametric framework using the least squares probabilistic classifier [45] is proposed and outperforms other classifiers.…”

Section: (B) Weights For Cdsv (A) Weights For Cdssmentioning

confidence: 99%

A Novel Approach to Unsupervised Change Detection Based on Hybrid Spectral Difference

Wang

Zhan

et al. 2018

Remote Sensing

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The most commonly used features in unsupervised change detection are spectral characteristics. Traditional methods describe the degree of the change between two pixels by quantifying the difference in spectral values or spectral shapes (spectral curve shapes). However, traditional methods based on variation in spectral shapes tend to miss the change between two pixels if their spectral curves are close to flat; and traditional methods based on variation in spectral values tend to miss the change between two pixels if their values are low (dark objects). To inhibit the weaknesses of traditional methods, a novel approach to unsupervised change detection based on hybrid spectral difference (HSD) is proposed which combines the difference between spectral values and spectral shapes. First, a new method referred to as change detection based on spectral shapes (CDSS) is proposed that fuses the difference images produced by the spectral correlation mapper (SCM) and spectral gradient difference (SGD) in order to describe the variation in spectral shapes. Second, a method called change detection based on spectral values (CDSV), computing the Euclidean distance between two spectral vectors, is used to obtain a difference image based on the variation in spectral values. Then, the credibility of CDSS and CDSV for every pixel is calculated to describe how appropriate these two methods are for detecting the change. Finally, the difference images produced by CDSS and CDSV are fused with the corresponding credibility to generate the hybrid spectral difference image. Two experiments were carried out on worldview-2/3 and Landsat-7 Enhanced Thematic Mapper Plus (ETM+) datasets, and both qualitative and quantitative results indicated that HSD had superior capabilities of change detection compared with standard change vector analysis (CVA), SCM, SGD and multivariate alteration detection (MAD). The accuracy of CDSS is higher than CDSV in case-1 but lower in case-2 and, compared to the higher one, the overall accuracy and the kappa coefficient of HSD improved by 3.45% and 6.92%, respectively, in the first experiment, and by 1.66% and 3.31%, respectively, in the second experiment. The omission rate dropped by approx. 4.4% in both tests.

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Section: (B) Weights For Cdsv (A) Weights For Cdssmentioning

confidence: 99%

A Novel Approach to Unsupervised Change Detection Based on Hybrid Spectral Difference

Wang

Zhan

et al. 2018

Remote Sensing

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“…The combination of several kernels is often done linearly, each kernel being weighted according to its relevance. The weights can be learned using the multiple kernel learning framework [27,28] or simply be determined by cross-validation when the number of kernels is low [21,29]. Such an approach has been applied for combining spectral and spatial information extracted from multi-source [30] and multi-temporal [31] remote sensing images.…”

Section: Data Fusion With Multiple Remote Sensing Imagesmentioning

confidence: 99%

Scalable Bag of Subpaths Kernel for Learning on Hierarchical Image Representations and Multi-Source Remote Sensing Data Classification

2017

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Abstract:The geographic object-based image analysis (GEOBIA) framework has gained increasing interest for the last decade. One of its key advantages is the hierarchical representation of an image, where object topological features can be extracted and modeled in the form of structured data. We thus propose to use a structured kernel relying on the concept of bag of subpaths to directly cope with such features. The kernel can be approximated using random Fourier features, allowing it to be applied on a large structure size (the number of objects in the structured data) and large volumes of data (the number of pixels or regions for training). With the so-called scalable bag of subpaths kernel (SBoSK), we also introduce a novel multi-source classification approach performing machine learning directly on a hierarchical image representation built from two images at different resolutions under the GEOBIA framework. Experiments run on an urban classification task show that the proposed approach run on a single image improves the classification overall accuracy in comparison with conventional approaches from 2% to 5% depending on the training set size and that fusing two images allows a supplementary 4% accuracy gain. Additional evaluations on public available large-scale datasets illustrate further the potential of SBoSK, with overall accuracy rates improvement ranging from 1% to 11% depending on the considered setup.

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“…With the development of remote sensing system, change detection (CD) has attracted widespread interest as one of the most important applications in remote sensing [1]. e accurate processing and understanding of the changes of land covers is a significant issue in different applications pertaining human activities, such as dynamic monitoring of land use, vegetation health, and environment [2][3][4]. e wild use of the new generation of high-resolution sensors (e.g., IKONOS, QuickBird, and GF2) has further broadened the applications of CD technology [5].…”

Section: Introductionmentioning

confidence: 99%

High-Resolution Remote-Sensing Image-Change Detection Based on Morphological Attribute Profiles and Decision Fusion

et al. 2020

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Change detection (CD) is essential for accurate understanding of land surface changes with multitemporal Earth observation data. Due to the great advantages in spatial information modeling, Morphological Attribute Profiles (MAPs) are becoming increasingly popular for improving the recognition ability in CD applications. However, most of the MAPs-based CD methods are implemented by setting the scale parameters of Attribute Profiles (APs) manually and ignoring the uncertainty of change information from different sources. To address these issues, a novel method for CD in high-resolution remote sensing (HRRS) images based on morphological attribute profiles and decision fusion is proposed in this study. By establishing the objective function based on the minimum of average interscale correlation, a morphological attribute profile with adaptive scale parameters (ASP-MAPs) is presented to exploit the spatial structure information. On this basis, a multifeature decision fusion framework based on the Dempster–Shafer (D-S) theory is constructed for obtaining the CD map. Experiments of multitemporal HRRS images from different sensors have shown that the proposed method outperforms the other advanced comparison CD methods, and the overall accuracy (OA) can reach more than 83.9%.

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A robust multi-kernel change detection framework for detecting leaf beetle defoliation using Landsat 7 ETM+ data

Cited by 13 publications

References 63 publications

A Novel Approach to Unsupervised Change Detection Based on Hybrid Spectral Difference

A Novel Approach to Unsupervised Change Detection Based on Hybrid Spectral Difference

Scalable Bag of Subpaths Kernel for Learning on Hierarchical Image Representations and Multi-Source Remote Sensing Data Classification

High-Resolution Remote-Sensing Image-Change Detection Based on Morphological Attribute Profiles and Decision Fusion

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