Colour and texture based change detection for urban disaster analysis

Tomowski, Daniel; Ehlers, Manfred; Klonus, Sascha

doi:10.1109/jurse.2011.5764786

Cited by 32 publications

(14 citation statements)

References 10 publications

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“…It produces stable spectral components which allows developing baseline spectral information for long-term studies of forest disturbances (Jin, Sader, 2005) or vegetation change (Rogan et al, 2002). Different texture-based transforms are developed and used, for example, for urban disaster analysis (Tomowski et al, 2011) and land use change detection (Erbek et al, 2004).…”

Section: Related Workmentioning

confidence: 99%

Change Detection via Selective Guided Contrasting Filters

Vizilter

Rubis

Zheltov

2017

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

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ABSTRACT:Change detection scheme based on guided contrasting was previously proposed. Guided contrasting filter takes two images (test and sample) as input and forms the output as filtered version of test image. Such filter preserves the similar details and smooths the nonsimilar details of test image with respect to sample image. Due to this the difference between test image and its filtered version (difference map) could be a basis for robust change detection. Guided contrasting is performed in two steps: at the first step some smoothing operator (SO) is applied for elimination of test image details; at the second step all matched details are restored with local contrast proportional to the value of some local similarity coefficient (LSC). The guided contrasting filter was proposed based on local average smoothing as SO and local linear correlation as LSC. In this paper we propose and implement new set of selective guided contrasting filters based on different combinations of various SO and thresholded LSC. Linear average and Gaussian smoothing, nonlinear median filtering, morphological opening and closing are considered as SO. Local linear correlation coefficient, morphological correlation coefficient (MCC), mutual information, mean square MCC and geometrical correlation coefficients are applied as LSC. Thresholding of LSC allows operating with non-normalized LSC and enhancing the selective properties of guided contrasting filters: details are either totally recovered or not recovered at all after the smoothing. These different guided contrasting filters are tested as a part of previously proposed change detection pipeline, which contains following stages: guided contrasting filtering on image pyramid, calculation of difference map, binarization, extraction of change proposals and testing change proposals using local MCC. Experiments on real and simulated image bases demonstrate the applicability of all proposed selective guided contrasting filters. All implemented filters provide the robustness relative to weak geometrical discrepancy of compared images. Selective guided contrasting based on morphological opening/closing and thresholded morphological correlation demonstrates the best change detection result.

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Section: Related Workmentioning

confidence: 99%

Change Detection via Selective Guided Contrasting Filters

Vizilter

Rubis

Zheltov

2017

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

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“…Tasseled cap transformation (Kauth and Thomas, 1976) produces stable spectral components for long-term studies of forest and vegetation (Jin, Sader, 2005;Rogan et al, 2002). Some other texture-based transforms are developed in (Tomowski et al, 2011).…”

Section: Related Workmentioning

confidence: 99%

“…The Direct Object change detection (DOCD) approach is based on the comparison of object geometrical properties (Lefebvre et al, 2008), spectral information (Miller et al, 2005;Hall and Hay, 2003) or texture features (Lefebvre et al, 2008;Tomowski et al, 2011). In Classified Objects change detection (COCD) approach the extracted objects are compared based on the geometry and class labels (Chant, Kelly, 2009).…”

Section: Related Workmentioning

confidence: 99%

Change Detection via Morphological Comparative Filters

Vizilter¹,

Rubis²,

Zheltov³

et al. 2016

ISPRS Ann. Photogramm. Remote Sens. Spatial Inf. Sci.

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ABSTRACT:In this paper we propose the new change detection technique based on morphological comparative filtering. This technique generalizes the morphological image analysis scheme proposed by Pytiev. A new class of comparative filters based on guided contrasting is developed. Comparative filtering based on diffusion morphology is implemented too. The change detection pipeline contains: comparative filtering on image pyramid, calculation of morphological difference map, binarization, extraction of change proposals and testing change proposals using local morphological correlation coefficient. Experimental results demonstrate the applicability of proposed approach.

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“…For pre-classification methods, basic image features such as local descriptors [8,14], texture features [11] and morphological profiles [4,6] are used to capture intricate changes brought by increased spatial definition. However, without special arrangement, basic image features lacks the ability to comprehend the semantic meaning of user interested changes [10].…”

Section: Introductionmentioning

confidence: 99%

Specific changes detection in visible-band VHR images using classification likelihood space

Zhu

et al. 2015

2015 3rd IAPR Asian Conference on Pattern Recognition (ACPR)

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Object-based post-classification change detection methods are effective for very high resolution images, but their effectiveness is limited by incomplete class hierarchy and complex image object comparison. In this paper, a novel Classification Likelihood Space (CLS) is proposed to synthesize the effective object-based image analysis and easyto-implement post-classification comparison, serving as a well tradeoff between performance and complexity. The proposed algorithm is tested on a dataset which comprises 102 pairs of visible-band very high resolution real satellite images, and a great improvement is observed over traditional post-classification comparison.

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Colour and texture based change detection for urban disaster analysis

Cited by 32 publications

References 10 publications

Change Detection via Selective Guided Contrasting Filters

Change Detection via Selective Guided Contrasting Filters

Change Detection via Morphological Comparative Filters

Specific changes detection in visible-band VHR images using classification likelihood space

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