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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,
