XXII ISPRS Congress 2012: Technical Commission VII

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Multivolume work

Persistent identifier:: 1663813779

Title:: XXII ISPRS Congress 2012

Sub title:: Melbourne, Australia, 25 August-1 September 2012

Year of publication:: 2013

Place of publication:: Red Hook, NY

Publisher of the original:: Curran Associates, Inc.

Identifier (digital):: 1663813779

Language:: English

Additional Notes:: Kongress-Thema: Imaging a sustainable future

Corporations:: International Society for Photogrammetry and Remote Sensing, Congress, 22., 2012, Melbourne; International Society for Photogrammetry and Remote Sensing

Adapter:: International Society for Photogrammetry and Remote Sensing, Congress, 22., 2012, Melbourne; International Society for Photogrammetry and Remote Sensing

Founder of work:: International Society for Photogrammetry and Remote Sensing, Congress, 22., 2012, Melbourne; International Society for Photogrammetry and Remote Sensing

Other corporate:: International Society for Photogrammetry and Remote Sensing, Congress, 22., 2012, Melbourne; International Society for Photogrammetry and Remote Sensing

Document type:: Multivolume work

Volume

Persistent identifier:: 1663821976

Title:: Technical Commission VII

Scope:: 546 Seiten

Year of publication:: 2013

Place of publication:: Red Hook, NY

Publisher of the original:: Curran Associates, Inc.

Identifier (digital):: 1663821976

Illustration:: Illustrationen, Diagramme

Signature of the source:: ZS 312(39,B7)

Language:: English

Additional Notes:: Erscheinungsdatum des Originals ist ermittelt.; Literaturangaben

Usage licence:: Attribution 4.0 International (CC BY 4.0)

Corporations:: International Society for Photogrammetry and Remote Sensing, Congress, 22., 2012, Melbourne; International Society for Photogrammetry and Remote Sensing

Adapter:: International Society for Photogrammetry and Remote Sensing, Congress, 22., 2012, Melbourne; International Society for Photogrammetry and Remote Sensing

Founder of work:: International Society for Photogrammetry and Remote Sensing, Congress, 22., 2012, Melbourne; International Society for Photogrammetry and Remote Sensing

Other corporate:: International Society for Photogrammetry and Remote Sensing, Congress, 22., 2012, Melbourne; International Society for Photogrammetry and Remote Sensing

Publisher of the digital copy:: Technische Informationsbibliothek Hannover

Place of publication of the digital copy:: Hannover

Year of publication of the original:: 2019

Document type:: Volume

Collection:: Earth sciences

Chapter

Title:: [VII/4: METHODS FOR LAND COVER CLASSIFICATION]

Document type:: Multivolume work

Structure type:: Chapter

Chapter

Title:: SUPPORT VECTOR MACHINE CLASSIFICATION OF OBJECT-BASED DATA FOR CROP MAPPING, USING MULTI-TEMPORAL LANDSAT IMAGERY R. Devadas, R. J. Denham and M. Pringle

Document type:: Multivolume work

Structure type:: Chapter

nt nd n. of ng al nd te up ed ter ol. ial CE International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences, Volume XXXIX-B7, 2012 XXII ISPRS Congress, 25 August — 01 September 2012, Melbourne, Australia SUPPORT VECTOR MACHINE CLASSIFICATION OF OBJECT-BASED DATA FOR CROP MAPPING, USING MULTI-TEMPORAL LANDSAT IMAGERY R. Devadas*^ ', R. J. Denham* and M. Pringle* ? Remote Sensing Centre, Ecosciences Percinct, GPO Box 2454, Brisbane, Queensland 4001, Australia — (rakhesh.devadas, robert.denham, matthew.pringle) 9? derm.gld.gov.au VII/4: Methods for Land Cover Classification KEY WORDS: object-based, classification, Landsat, crop, artificial intelligence/S VM, multitemporal ABSTRACT: Crop mapping and time series analysis of agronomic cycles are critical for monitoring land use and land management practices, and analysing the issues of agro-environmental impacts and climate change. Multi-temporal Landsat data can be used to analyse decadal changes in cropping patterns at field level, owing to its medium spatial resolution and historical availability. This study attempts to develop robust remote sensing techniques, applicable across a large geographic extent, for state-wide mapping of cropping history in Queensland, Australia. In this context, traditional pixel-based classification was analysed in comparison with image object-based classification using advanced supervised machine-learning algorithms such as Support Vector Machine (SVM). For the Darling Downs region of southern Queensland we gathered a set of Landsat TM images from the 2010-2011 cropping season. Landsat data, along with the vegetation index images, were subjected to multiresolution segmentation to obtain polygon objects. Object-based methods enabled the analysis of aggregated sets of pixels, and exploited shape-related and textural variation, as well as spectral characteristics. SVM models were chosen after examining three shape-based parameters, twenty-three textural parameters and ten spectral parameters of the objects. We found that the object-based methods were superior to the pixel-based methods for classifying 4 major landuse/land cover classes, considering the complexities of within field spectral heterogeneity and spectral mixing. Comparative analysis clearly revealed that higher overall classification accuracy (95%) was observed in the object-based SVM compared with that of traditional pixel-based classification (89%) using maximum likelihood classifier (MLC). Object-based classification also resulted speckle-free images. Further, object-based SVM models were used to classify different broadacre crop types for summer and winter seasons. The influence of different shape, textural and spectral variables, and their weights on crop-mapping accuracy, was also examined. Temporal change in the spectral characteristics, specifically through vegetation indices derived from multi-temporal Landsat data, was found to be the most critical information that affects the accuracy of classification. However, use of these variables was constrained by the data availability and cloud cover. 1. INTRODUCTION Land management practices have significant impacts on the condition of land and water and the profitability and sustainability of agriculture. Crop mapping and time series analysis of agronomic cycles are critical for monitoring landuse and land management practices, and analysing the issues of agro-environmental impacts and climate change. Developments in remote sensing techniques offer a powerful and cost effective means for land use/land cover mapping, by virtue of their synoptic coverage and their ability to collect data at different spatial, spectral, radiometric and temporal resolutions. Multi-temporal Landsat data can be used to analyse decadal changes in cropping patterns at paddock level, owing to its medium spatial resolution and historical availability. Various investigations have demonstrated the benefits of crop mapping using remote sensing data (Congalton et al., 1998; Oetter et al., 2001; Ulaby et al., 1982). Utilisation of time series satellite data was proved to be essential for high accuracy of crop classification (Barbosa et al, 1996; Serra and Pons, 2008; Simonneaux et al., 2008). Object-based techniques have been increasingly implemented in remotely sensed image analysis to overcome problems due to pixel heterogeneity and crop variability within the field (Blaschke, 2010; Castillejo-Gonzâlez et al, 2009; Peña- Barragán et al., 2011). Object-based image analysis segments the image and constructs a hierarchical network of homogeneous objects. Object-based methods enable the analysis of aggregated sets of pixels, and exploit shape-related and textural variation, as well as spectral characteristics (Baatz and Schäpe, 2000). In the classification process, all pixels in the entire objects are assigned to the same class, thus removing the problems of spectral variability and mixed pixels (Peña- Barragán et al., 2011). Numerous classification algorithms have been developed since acquisition of the first Landsat image in early 1970s (Townshend, 1992). Maximum likelihood classifier (MLC), a parametric classifier, is one of the most widely used classifiers (Dixon and Candade, 2007; Hansen et al., 1996). The support vector machine (SVM) represents a group of theoretically superior non-parametric machine learning algorithms. There is no assumption made on the distribution of underlying data (Boser et al., 1992; Vapnik, 1979; Vapnik, 1998). The SVM employs optimization algorithms to locate the optimal boundaries between classes (Huang et al., 2002) and can be successfully applied to the problems of image classification with large input dimensionality. SVMs are particularly appealing in the remote sensing field due to their ability to generalize well even with limited training samples, a common limitation for remote sensing applications (Mountrakis et al., 2011). In this context, this study attempts to develop robust SVM- based techniques for classification of object-based data generated from multi-temporal Landsat images. Operational application of these techniques across a large geographic extent,

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