The improvement of an object-oriented classification using multi-temporal MODIS EVI satellite data

Gao, Yan; Mas, Jean-François; Navarrete, A.

doi:10.1080/17538940902818311

Cited by 14 publications

(7 citation statements)

References 27 publications

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“…This finding is in conformity with previous reports documented by Wang et al, 5 Yan et al, 13 Chen et al, 37 Gao et al, 38 and Myint et al 18 Despite the higher capability of object-oriented approach in image classification, differences in execution time between pixel-and object-based image analysis still remain an issue, especially for large areas. 3 Future development of more quantitative methods for selecting optimal image segmentation parameters, especially at the SL as demonstrated by Costa et al 39 and Drăgut et al, 40 will hopefully reduce the required time for object-oriented classification.…”

Section: Tablesupporting

confidence: 81%

Comparison between pixel- and object-based image classification of a tropical landscape using Système Pour l’Observation de la Terre-5 imagery

Memarian

Balasundram

Khosla

2013

J. Appl. Remote Sens

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Abstract. Based on the Système Pour l'Observation de la Terre-5 imagery, two main techniques of classifying land-use categories in a tropical landscape are compared using two supervised algorithms: maximum likelihood classifier (MLC) and K-nearest neighbor object-based classifier. Nine combinations of scale level (SL10, SL30, and SL50) and the nearest neighbor (NN3, NN5, and NN7) are investigated in the object-based classification. Accuracy assessment is performed using two main disagreement components, i.e., quantity disagreement and allocation disagreement. The MLC results in a higher total disagreement in total landscape as compared with object-based image classification. The SL30-NN5 object-based classifier reduces allocation error by 250% as compared with the MLC. Therefore, this classifier shows a higher performance in land-use classification of the Langat basin. © The Authors. Published by SPIE under a Creative Commons Attribution 3.0 Unported License. Distribution or reproduction of this work in whole or in part requires full attribution of the original publication, including its DOI.

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Section: Tablesupporting

confidence: 81%

Comparison between pixel- and object-based image classification of a tropical landscape using Système Pour l’Observation de la Terre-5 imagery

Memarian

Balasundram

Khosla

2013

J. Appl. Remote Sens

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“…The accuracy results were then compared to check their difference. This method was widely used in existing studies [27,41,42] as it can minimize the statistical and human bias in the process of validation sample selections [43]. It was implemented in three steps in this study.…”

Section: Classification Accuracy Assessment and Comparisonmentioning

confidence: 99%

Exploring the Use of Google Earth Imagery and Object-Based Methods in Land Use/Cover Mapping

et al. 2013

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Google Earth (GE) releases free images in high spatial resolution that may provide some potential for regional land use/cover mapping, especially for those regions with high heterogeneous landscapes. In order to test such practicability, the GE imagery was selected for a case study in Wuhan City to perform an object-based land use/cover classification. The classification accuracy was assessed by using 570 validation points generated by a random sampling scheme and compared with a parallel classification of QuickBird (QB) imagery based on an object-based classification method. The results showed that GE has an overall classification accuracy of 78.07%, which is slightly lower than that of QB. No significant difference was found between these two classification results by the adoption of Z-test, which strongly proved the potentials of GE in land use/cover mapping. Moreover, GE has different discriminating capacity for specific land use/cover types. It possesses some advantages for mapping those types with good spatial characteristics in terms of geometric, shape and context. The object-based method is recommended for imagery classification when using GE imagery for mapping land use/cover. However, GE has some limitations for those types classified by using only spectral characteristics largely due to its poor spectral characteristics. OPEN ACCESSRemote Sens. 2013, 5 6027

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“…This region-merging technique has been successfully applied in other mountainous regions (Dr agut & Blaschke, 2008;Gao, Mas, & Navarrete, 2009). Following a 'trial and error' approach (Im, Jensen, & Tullips, 2008;Robertson & King, 2011), the parameter settings were iteratively changed after the segmentation process if no visual resemblance to potential objects recognized from satellite imagery was observed.…”

Section: The Image Classification Processmentioning

confidence: 99%

Optimizing land cover classification accuracy for change detection, a combined pixel-based and object-based approach in a mountainous area in Mexico

2012

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The improvement of an object-oriented classification using multi-temporal MODIS EVI satellite data

Cited by 14 publications

References 27 publications

Comparison between pixel- and object-based image classification of a tropical landscape using Système Pour l’Observation de la Terre-5 imagery

Comparison between pixel- and object-based image classification of a tropical landscape using Système Pour l’Observation de la Terre-5 imagery

Exploring the Use of Google Earth Imagery and Object-Based Methods in Land Use/Cover Mapping

Optimizing land cover classification accuracy for change detection, a combined pixel-based and object-based approach in a mountainous area in Mexico

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