Multispectral image segmentation using a multiscale model

Bouman, Charles A.; Shapiro, M. V.

doi:10.1109/icassp.1992.226150

Cited by 23 publications

(18 citation statements)

References 6 publications

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“…It operates at different scales and resolutions, and it uses assessments at wider scales to drive assessments at scales that are more detailed. It enables one to reduce the spatial dimension of errors and to prevent the homologation of neighboring pixels to the same class, by reducing smoothing operations, if their position in the image changes often [37][38][39].…”

Section: Supervised Classification With the Smap Algorithmmentioning

confidence: 99%

A SMAP Supervised Classification of Landsat Images for Urban Sprawl Evaluation

Palma

Amato

Nolè

et al. 2016

IJGI

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Abstract:The negative impacts of land take on natural components and economic resources affect planning choices and territorial policies. The importance of land take monitoring, in Italy, has been only recently considered, but despite this awareness, in the great part of the country, effective monitoring and containment measures have not been started, yet. This research proposes a methodology to map and monitor land use changes. To this end, a time series from 1985-2010, based on the multi-temporal Landsat data Thematic Mapper (TM), has been analyzed in the Vulture Alto-Bradano area, a mountain zone of the Basilicata region (Southern Italy). Results confirm a double potentiality of using these data: on the one hand, the use of multi-temporal Landsat data allows going very back in time, producing accurate datasets that provide a phenomenon trend over time; on the other hand, these data can be considered a first experience of open data in the field of spatial information. The proposed methodology provides agencies, local authorities and practitioners with a valuable tool to implement monitoring actions. This represents the first step to pursue territorial governance methods based on sustainability, limiting the land take.

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Section: Supervised Classification With the Smap Algorithmmentioning

confidence: 99%

A SMAP Supervised Classification of Landsat Images for Urban Sprawl Evaluation

Palma

Amato

Nolè

et al. 2016

IJGI

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“…SMAP [43,44] is based on contextual classification, a classification of the pixels by region and not individually; in this sense, it can also be considered a segmentation method. It is assumed that the cells that are close in the image are more likely to belong to the same class, so it works by dividing the image into various resolutions.…”

Section: Sequential Maximum a Posteriorimentioning

confidence: 99%

Improving Classification Accuracy of Multi-Temporal Landsat Images by Assessing the Use of Different Algorithms, Textural and Ancillary Information for a Mediterranean Semiarid Area from 2000 to 2015

2017

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Abstract:The aim of this study was to evaluate three different strategies to improve classification accuracy in a highly fragmented semiarid area using, (i) different classification algorithms with parameter optimization in some cases; (ii) different feature sets including spectral, textural and terrain features; and (iii) different seasonal combinations of images. A three-way ANOVA was used to discern which of these approaches and their interactions significantly increases accuracy. Tukey-Kramer contrast using a heteroscedasticity-consistent estimation of the kappa covariances matrix was used to check for significant differences in accuracy. The experiment was carried out with Landsat TM, ETM and OLI images corresponding to the period 2000-2015. A combination of four images using random forest and the three feature sets was the best way to improve accuracy. Maximum likelihood, random forest and support vector machines do not significantly increase accuracy when textural information was added, but do so when terrain features were taken into account. On the other hand, sequential maximum a posteriori increased accuracy when textural features were used, but reduced accuracy substantially when terrain features were included. Random forest using the three feature subsets and sequential maximum a posteriori with spectral and textural features had the largest kappa values, around 0.9.

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Section: Sequencial Maximum a Posteriorimentioning

confidence: 99%

Using Different Algorithms and Multi-Seasonal, Textural and Ancillary Information to Increase Classification Accuracy during the Period 2000–2015 in a Mediterranean Semiarid Area

Castillo¹,

Alonso‐Sarría²,

Cánovas-García³

2017

Preprint

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Abstract:The aim of this study is to evaluate three different strategies to improve classification accuracy in a highly fragmented semiarid area. i) Using different classification algorithms: Maximum Likelihood, Random Forest, Support Vector Machines and Sequential Maximum a Posteriori, with parameter optimisation in the second and third cases; ii) using different feature sets: spectral features, spectral and textural features, and spectral, textural and terrain features; and iii) using different image-sets: winter, spring, summer, autumn, winter+summer, winter+ spring+summer; and a four seasons combination. A 3-way ANOVA is used to discern which of these approaches and their interactions significantly increases accuracy. Tukey-Kramer contrast using a heteroscedasticity-consistent estimation of the kappa covariances matrix was used to check for significant differences in accuracy. The experiment was carried out with Landsat TM, ETM, and OLI images corresponding to the period 2000-2015. A combination of four images was the best way to improve accuracy. Maximum Likelihood, Random Forest and Support Vector Machines do not significantly increase accuracy when textural information is added, but do so when terrain features are taken into account. On the other hand, Sequential Maximum a Posteriori increases accuracy when textural features are used, but reduces accuracy substantially when terrain features are included. Random Forest using the three feature subsets and Sequential Maximum a Posteriori with spectral and textural features had the largest kappa values, around 0.9.

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Multispectral image segmentation using a multiscale model

Cited by 23 publications

References 6 publications

A SMAP Supervised Classification of Landsat Images for Urban Sprawl Evaluation

A SMAP Supervised Classification of Landsat Images for Urban Sprawl Evaluation

Improving Classification Accuracy of Multi-Temporal Landsat Images by Assessing the Use of Different Algorithms, Textural and Ancillary Information for a Mediterranean Semiarid Area from 2000 to 2015

Using Different Algorithms and Multi-Seasonal, Textural and Ancillary Information to Increase Classification Accuracy during the Period 2000–2015 in a Mediterranean Semiarid Area

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Multispectral image segmentation using a multiscale model

Cited by 23 publications

References 6 publications

A SMAP Supervised Classification of Landsat Images for Urban Sprawl Evaluation

A SMAP Supervised Classification of Landsat Images for Urban Sprawl Evaluation

Improving Classification Accuracy of Multi-Temporal Landsat Images by Assessing the Use of Different Algorithms, Textural and Ancillary Information for a Mediterranean Semiarid Area from 2000 to 2015

Using Different Algorithms and Multi-Seasonal, Textural and Ancillary Information to Increase Classification Accuracy during the Period 2000&ndash;2015 in a Mediterranean Semiarid Area

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Using Different Algorithms and Multi-Seasonal, Textural and Ancillary Information to Increase Classification Accuracy during the Period 2000–2015 in a Mediterranean Semiarid Area