2017
DOI: 10.1080/15481603.2017.1408892
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Less is more: optimizing classification performance through feature selection in a very-high-resolution remote sensing object-based urban application

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Cited by 191 publications
(136 citation statements)
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References 46 publications
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“…While these images typically feature few spectral bands (normally optical RGB plus potentially one infrared band), limiting their ability to differentiate land cover types based on spectral characteristics, their high resolution enables the human eye to differentiate most land cover types based on, for example, the shape and density of trees, and the advancement of object-based classification methods has meant that automated processes can also take advantage of this spatial information to produce accurate classifications [13][14][15][16]. However, the high costs and complexity of both the object-based image analysis, and the high cost and low availability of data at a sufficient resolution, remain as challenges for wider application [17,18]. Our study investigated whether publicly available data, namely Sentinel-2 (S2), can map complex landscapes in Southern Myanmar, including oil palm, rubber, and betel nut plantations using a Random Forest classifier on Google Earth Engine.…”
Section: Introductionmentioning
confidence: 99%
“…While these images typically feature few spectral bands (normally optical RGB plus potentially one infrared band), limiting their ability to differentiate land cover types based on spectral characteristics, their high resolution enables the human eye to differentiate most land cover types based on, for example, the shape and density of trees, and the advancement of object-based classification methods has meant that automated processes can also take advantage of this spatial information to produce accurate classifications [13][14][15][16]. However, the high costs and complexity of both the object-based image analysis, and the high cost and low availability of data at a sufficient resolution, remain as challenges for wider application [17,18]. Our study investigated whether publicly available data, namely Sentinel-2 (S2), can map complex landscapes in Southern Myanmar, including oil palm, rubber, and betel nut plantations using a Random Forest classifier on Google Earth Engine.…”
Section: Introductionmentioning
confidence: 99%
“…SSPO methods have been criticized for being cost-ineffective since they operate on a trial-and-error manner (for visual interpretation) or require reference segments to be digitized (for quantitative analysis), and also for being susceptible to the subjectivity of the user. To treat this issue, unsupervised segmentation parameter optimization (USPO) methods have been developed and are particularly important in the context of increasing data loads and automation purposes [14,[18][19][20][21][22][23][24][25]. To identify optimal segmentation parameters, USPO procedures usually employ a combination of geospatial metrics that describe spectral heterogeneity between and within image segments [9,[26][27][28].…”
Section: Introductionmentioning
confidence: 99%
“…Since they are sufficient for performing the feature selection step, this would save processing time and storage space [61]. Only the most discriminant features could then be computed for the whole AOI.…”
Section: Discussionmentioning
confidence: 99%