Change Detection Using Global and Local Multifractal Description

Aleksandrowicz, Sebastian; Wawrzaszek, Anna; Drzewiecki, Wojciech; Krupiński, M.

doi:10.1109/lgrs.2016.2574940

Cited by 19 publications

(12 citation statements)

References 25 publications

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“…Change detection on panchromatic remote sensing images seems to be a promising field of application [Aleksandrowicz et al, 2016]. The study could be also continued to assess the sensitivity of proposed parameters to eg.…”

Section: Discussionmentioning

confidence: 99%

“…Utilisation of multifractal features to derive local spatial information from the image may be considered as another direction of future research. Aleksandrowicz et al [2016] used Hölder exponents to characterise texture at pixel neighborhood level. Incorporation of such textural (spatial) features into classification of multispectral and hiperspectral images constitutes one of actual research fields in remote sensing [Wang et al, 2016].…”

Section: Discussionmentioning

confidence: 99%

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Applicability of multifractal features as global characteristics of WorldView-2 panchromatic satellite images

Drzewiecki

Wawrzaszek

Krupiński

et al. 2016

European Journal of Remote Sensing

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In this work we analyse fractal and multifractal characteristics for description and extraction of information from VHR satellite images. We propose the degree of multifractality as a global descriptor of satellite image content and investigate its usefulness for classification of WorldView-2 image chips into main land cover types. The research confirmed that it is possible to use the textural features as efficient global descriptors of WorldView-2 panchromatic image content. Results show that the degree of multifractality is related to land cover type prevailing in the imaged area. It was also proved that multifractal parameters should be considered as valuable textural features in the context of land cover classification.

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Section: Discussionmentioning

confidence: 99%

Section: Discussionmentioning

confidence: 99%

Applicability of multifractal features as global characteristics of WorldView-2 panchromatic satellite images

Drzewiecki

Wawrzaszek

Krupiński

et al. 2016

European Journal of Remote Sensing

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“…In this paper, SPS is well extended to two‐dimensional singular power spectrum (2D‐SPS). Also, Holder exponents [6], 2D‐MFS [6] and improved 2D‐SPS are well combined for SAR target detection and segmentation.…”

Section: Methodsmentioning

confidence: 99%

“…Gan and Shouhong [5] studied the multi‐fractal characteristics of sea clutter and proposed a fuzzy detection method. Some other detection methods related to multi‐fractal spectrum are introduced in [6–10]. While the generality of above studies on multi‐fractal spectrum (MFS) are based on spatial differentiability characteristics, they failed to provide the power distribution of fractal signal along with the singularity exponent [11].…”

Section: Introductionmentioning

confidence: 99%

Ship target detection and segmentation method based on multi‐fractal analysis

Zhu

Xiong

Wang

2019

J. eng.

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“…al., 2016;Aleksandrowicz et al, 2016). ROC graph is a two-dimensional graph of the true positive rate (recall) plotted against the false positive rate (fpr), calculated as:…”

Section: Change Detection Accuracy Assessmentmentioning

confidence: 99%

Thorough statistical comparison of machine learning regression models and their ensembles for sub-pixel imperviousness and imperviousness change mapping

Drzewiecki¹

2017

Geodesy and Cartography

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Abstract:We evaluated the performance of nine machine learning regression algorithms and their ensembles for sub-pixel estimation of impervious areas coverages from Landsat imagery. The accuracy of imperviousness mapping in individual time points was assessed based on RMSE, MAE and R 2 . These measures were also used for the assessment of imperviousness change intensity estimations. The applicability for detection of relevant changes in impervious areas coverages at sub-pixel level was evaluated using overall accuracy, F-measure and ROC Area Under Curve. The results proved that Cubist algorithm may be advised for Landsat-based mapping of imperviousness for single dates. Stochastic gradient boosting of regression trees (GBM) may be also considered for this purpose. However, Random Forest algorithm is endorsed for both imperviousness change detection and mapping of its intensity. In all applications the heterogeneous model ensembles performed at least as well as the best individual models or better. They may be recommended for improving the quality of sub-pixel imperviousness and imperviousness change mapping. The study revealed also limitations of the investigated methodology for detection of subtle changes of imperviousness inside the pixel. None of the tested approaches was able to reliably classify changed and non-changed pixels if the relevant change threshold was set as one or three percent. Also for fi ve percent change threshold most of algorithms did not ensure that the accuracy of change map is higher than the accuracy of random classifi er. For the threshold of relevant change set as ten percent all approaches performed satisfactory.

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Change Detection Using Global and Local Multifractal Description

Cited by 19 publications

References 25 publications

Applicability of multifractal features as global characteristics of WorldView-2 panchromatic satellite images

Applicability of multifractal features as global characteristics of WorldView-2 panchromatic satellite images

Ship target detection and segmentation method based on multi‐fractal analysis

Thorough statistical comparison of machine learning regression models and their ensembles for sub-pixel imperviousness and imperviousness change mapping

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