Assessment of the effectiveness of support vector machines for hyperspectral data

Pal, Mahesh; Mather, Paul M.

doi:10.1016/j.future.2003.11.011

Cited by 191 publications

(108 citation statements)

References 26 publications

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“…Non-parametric classifiers are used to estimate the probability density function when it is unknown (Kumar and Sahoo 2012) such as support vector machines (SVM) and artificial neural networks (ANN). Statistical classifiers depend on some predefined data model and the performance of these classifiers depends on how well the data match the predefined model (Pal and Mather 2004). Adam et al (2014) confirmed the performance of machinelearning random forest (RF) and SVM classifiers to map heterogeneous land in South Africa using RapidEye high resolution imagery.…”

Section: Introductionmentioning

confidence: 81%

“…Figure 2 illustrates a simple scenario of a two-class separable classification problem in a two-dimensional input space. SVM aim to determine the optimal separating hyperplane (OSH) among all the possible hyperplanes (Srivastava et al 2012) and this is done through an optimization problem using Lagrange multipliers and quadratic programming methods (Pal and Mather 2004).…”

Section: Support Vector Machinesmentioning

confidence: 99%

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Semiautomatic approach for land cover classification: a remote sensing study for arid climate in southeastern Tunisia

Bouaziz

Eisold

Guermazi

2017

Euro-Mediterr J Environ Integr

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The land use and land cover (LULC) classification has great potential to contribute to the monitoring of land degradation and climatic disasters. The purpose of this study was to assess the performance of parametric and nonparametric classification methods using remotely sensed Landsat satellite data of arid and semiarid areas, based on the computed producer's accuracy, user's accuracy, overall accuracy, and Cohen's kappa coefficient. Three LULC classes were identified, and supervised classifications were applied to Landsat 8 imagery. The results show that the support vector machines (SVM) classification method produced more accurate results, using two different kernel functions, compared with the maximum likelihood classification (MLC) and the minimum distance classification (MDC). The basis radial function affords the highest overall classification accuracy of 91.20% and a mean kappa coefficient of 0.87. This classification method is very well suited to accurately map LULC in arid and semiarid regions where the main vegetation type is oasis or steppes.

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Section: Introductionmentioning

confidence: 81%

Section: Support Vector Machinesmentioning

confidence: 99%

Semiautomatic approach for land cover classification: a remote sensing study for arid climate in southeastern Tunisia

Bouaziz

Eisold

Guermazi

2017

Euro-Mediterr J Environ Integr

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“…Parametric methods are based on the assumption that the data of each class are normally distributed [7,49,67]; however, non-parametric techniques do not assume specific data class distributions [11,41]. Supervised classification is one of the most commonly used techniques for the classification of remotely sensed data.…”

Section: Classification Methodsmentioning

confidence: 99%

Fusion of Airborne Discrete-Return LiDAR and Hyperspectral Data for Land Cover Classification

Luo

Wang

et al. 2015

Remote Sensing

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Accurate land cover classification information is a critical variable for many applications. This study presents a method to classify land cover using the fusion data of airborne discrete return LiDAR (Light Detection and Ranging) and CASI (Compact Airborne Spectrographic Imager) hyperspectral data. Four LiDAR-derived images (DTM, DSM, nDSM, and intensity) and CASI data (48 bands) with 1 m spatial resolution were spatially resampled to 2, 4, 8, 10, 20 and 30 m resolutions using the nearest neighbor resampling method. These data were thereafter fused using the layer stacking and principal components analysis (PCA) methods. Land cover was classified by commonly used supervised classifications in remote sensing images, i.e., the support vector machine (SVM) and maximum likelihood (MLC) classifiers. Each classifier was applied to four types of datasets (at seven different spatial resolutions): (1) the layer stacking fusion data; (2) the PCA fusion data; (3) the LiDAR data alone; and (4) the CASI data alone. In this study, the land cover category was classified into seven classes, i.e., buildings, road, water bodies, forests, grassland, cropland and barren land. A total of 56 classification results were produced, and the classification accuracies were assessed and compared. The results show that the classification accuracies produced from two fused datasets were higher than that of the single LiDAR and CASI data at all seven spatial resolutions. Moreover, we find that the layer stacking method produced higher overall classification accuracies than the PCA fusion method using both the SVM and MLC classifiers. The highest classification accuracy obtained (OA = 97.8%, kappa = 0.964) using the SVM classifier on the layer stacking fusion data at 1 m spatial resolution. Compared with the best classification results of the CASI and LiDAR data alone, the overall classification accuracies improved by 9.1% and 19.6%, respectively. Our findings also demonstrated that the SVM classifier generally performed better than the MLC when classifying multisource data; however, none of the classifiers consistently produced higher accuracies at all spatial resolutions.

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“…In locating the support vectors, SVMs tend to use only a subset of the training data and so they are particularly advocated for use with high-dimensional data sets primarily because it is believed that the decision making is not constrained by the Hughes effect [16,56,57]. Although others dispute this somewhat [58], use of the SVM as a classifier should benefit complex classification problems-e.g., where fine spatial resolution imagery is used to map detailed classification schema in heterogeneous environments-and can perform better than ML classification for urban environments using VHR imagery [59,60]. However, as a pixel-based approach, it may still suffer from within-feature variation leading to some degree of misclassification [1,23].…”

Section: Pixel-based Classificationmentioning

confidence: 99%

Mapping Complex Urban Land Cover from Spaceborne Imagery: The Influence of Spatial Resolution, Spectral Band Set and Classification Approach

2016

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Detailed land cover information is valuable for mapping complex urban environments. Recent enhancements to satellite sensor technology promise fit-for-purpose data, particularly when processed using contemporary classification approaches. We evaluate this promise by comparing the influence of spatial resolution, spectral band set and classification approach for mapping detailed urban land cover in Nottingham, UK. A WorldView-2 image provides the basis for a set of 12 images with varying spatial and spectral characteristics, and these are classified using three different approaches (maximum likelihood (ML), support vector machine (SVM) and object-based image analysis (OBIA)) to yield 36 output land cover maps. Classification accuracy is evaluated independently and McNemar tests are conducted between all paired outputs (630 pairs in total) to determine which classifications are significantly different. Overall accuracy varied between 35% for ML classification of 30 m spatial resolution, 4-band imagery and 91% for OBIA classification of 2 m spatial resolution, 8-band imagery. The results demonstrate that spatial resolution is clearly the most influential factor when mapping complex urban environments, and modern "very high resolution" or VHR sensors offer great advantage here. However, the advanced spectral capabilities provided by some recent sensors, coupled with contemporary classification approaches (especially SVMs and OBIA), can also lead to significant gains in mapping accuracy. Ongoing development in instrumentation and methodology offer huge potential here and imply that urban mapping opportunities will continue to grow.

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Assessment of the effectiveness of support vector machines for hyperspectral data

Cited by 191 publications

References 26 publications

Semiautomatic approach for land cover classification: a remote sensing study for arid climate in southeastern Tunisia

Semiautomatic approach for land cover classification: a remote sensing study for arid climate in southeastern Tunisia

Fusion of Airborne Discrete-Return LiDAR and Hyperspectral Data for Land Cover Classification

Mapping Complex Urban Land Cover from Spaceborne Imagery: The Influence of Spatial Resolution, Spectral Band Set and Classification Approach

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