A critical synthesis of remotely sensed optical image change detection techniques

Tewkesbury, Andrew P.; Comber, Alexis; Tate, Nicholas; Lamb, Alistair; Fisher, Peter

doi:10.1016/j.rse.2015.01.006

Cited by 441 publications

(299 citation statements)

References 80 publications

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“…Image differencing is a simple and well-established CD approach [4,26,91]. In our study, difference layers were calculated for all spectral bands, vegetation indices, texture layers, Tasseled Cap Coefficients, and Disturbance Indices by subtracting the post-storm values from the pre-storm values.…”

Section: Appendix A2 Tasseled Cap Transformation and Disturbance Indexmentioning

confidence: 99%

“…The recent studies of Chen et al [22], Hussain et al [23], Hecheltjen et al [24], Bovolo and Bruzzone [25], and Tewkesbury et al [26] give a broad overview of established CD techniques. For a synopsis of forest disturbance CD studies, the reader is referred to Chehata et al [12].…”

Section: Introductionmentioning

confidence: 99%

“…Generally, CD techniques can be differentiated in pixel-based analysis (PBA) and object-based image analysis (OBIA) [15,17,26]. In OBIA, instead of analyzing single pixels, typically image segmentation is applied first.…”

Section: Introductionmentioning

confidence: 99%

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Windthrow Detection in European Forests with Very High-Resolution Optical Data

Einzmann

Immitzer

Böck

et al. 2017

Forests

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With climate change, extreme storms are expected to occur more frequently. These storms can cause severe forest damage, provoking direct and indirect economic losses for forestry. To minimize economic losses, the windthrow areas need to be detected fast to prevent subsequent biotic damage, for example, related to beetle infestations. Remote sensing is an efficient tool with high potential to cost-efficiently map large storm affected regions. Storm Niklas hit South Germany in March 2015 and caused widespread forest cover loss. We present a two-step change detection approach applying commercial very high-resolution optical Earth Observation data to spot forest damage. First, an object-based bi-temporal change analysis is carried out to identify windthrow areas larger than 0.5 ha. For this purpose, a supervised Random Forest classifier is used, including a semi-automatic feature selection procedure; for image segmentation, the large-scale mean shift algorithm was chosen. Input features include spectral characteristics, texture, vegetation indices, layer combinations and spectral transformations. A hybrid-change detection approach at pixel-level subsequently identifies small groups of fallen trees, combining the most important features of the previous processing step with Spectral Angle Mapper and Multivariate Alteration Detection. The methodology was evaluated on two test sites in Bavaria with RapidEye data at 5 m pixel resolution. The results regarding windthrow areas larger than 0.5 ha were validated with reference data from field visits and acquired through orthophoto interpretation. For the two test sites, the novel object-based change detection approach identified over 90% of the windthrow areas (≥0.5 ha). The red edge channel was the most important for windthrow identification. Accuracy levels of the change detection at tree level could not be calculated, as it was not possible to collect field data for single trees, nor was it possible to perform an orthophoto validation. Nevertheless, the plausibility and applicability of the pixel-based approach is demonstrated on a second test site.

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Section: Appendix A2 Tasseled Cap Transformation and Disturbance Indexmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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Windthrow Detection in European Forests with Very High-Resolution Optical Data

Einzmann

Immitzer

Böck

et al. 2017

Forests

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“…Various methods have been developed for land cover change analysis using remotely sensed data (Coppin, Jonckheere, Nackaerts, Muys, & Lambin, 2004;Hussain, Chen, Cheng, Wei, & Stanley, 2013;Lu, Mausel, Brondizio, & Moran, 2004;Tewkesbury, Comber, Tate, Lamb, & Fisher, 2015). These methods may be broadly classified into two categories: post-classification comparison and pre-classification change detection (Lu et al, 2004;Singh, 1989;Zhou, Troy, & Grove, 2008).…”

Section: Introductionmentioning

confidence: 99%

A new approach for land cover classification and change analysis: Integrating backdating and an object-based method

Zhou

Qian

et al. 2016

Remote Sensing of Environment

155

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“…The methodology of change detection through sub-pixel percent imperviousness mapping was proposed by Yang et al (2003). This approach is classifi ed to layer arithmetic change detection techniques (Tewkesbury, 2015). It should be noted however, that the input layers and their difference have semantic meaning.…”

Section: Introductionmentioning

confidence: 99%

Improving sub-pixel imperviousness change prediction by ensembling heterogeneous non-linear regression models

Drzewiecki

2016

Geodesy and Cartography

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Abstract:In this work nine non-linear regression models were compared for sub-pixel impervious surface area mapping from Landsat images. The comparison was done in three study areas both for accuracy of imperviousness coverage evaluation in individual points in time and accuracy of imperviousness change assessment. The performance of individual machine learning algorithms (Cubist, Random Forest, stochastic gradient boosting of regression trees, k-nearest neighbors regression, random k-nearest neighbors regression, Multivariate Adaptive Regression Splines, averaged neural networks, and support vector machines with polynomial and radial kernels) was also compared with the performance of heterogeneous model ensembles constructed from the best models trained using particular techniques. The results proved that in case of sub-pixel evaluation the most accurate prediction of change may not necessarily be based on the most accurate individual assessments. When single methods are considered, based on obtained results Cubist algorithm may be advised for Landsat based mapping of imperviousness for single dates. However, Random Forest may be endorsed when the most reliable evaluation of imperviousness change is the primary goal. It gave lower accuracies for individual assessments, but better prediction of change due to more correlated errors of individual predictions. Heterogeneous model ensembles performed for individual time points assessments at least as well as the best individual models. In case of imperviousness change assessment the ensembles always outperformed single model approaches. It means that it is possible to improve the accuracy of sub-pixel imperviousness change assessment using ensembles of heterogeneous non-linear regression models.

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A critical synthesis of remotely sensed optical image change detection techniques

Cited by 441 publications

References 80 publications

Windthrow Detection in European Forests with Very High-Resolution Optical Data

Windthrow Detection in European Forests with Very High-Resolution Optical Data

A new approach for land cover classification and change analysis: Integrating backdating and an object-based method

Improving sub-pixel imperviousness change prediction by ensembling heterogeneous non-linear regression models

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