An Objective Analysis of Support Vector Machine Based Classification for Remote Sensing

Oommen, Thomas; Misra, Debasmita; Twarakavi, Navin K. C.; Prakash, Anupma; Sahoo, Bhaskar; Bandopadhyay, Sukumar

doi:10.1007/s11004-008-9156-6

Cited by 159 publications

(102 citation statements)

References 12 publications

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“…The MLC is a parametric classifier based on the Bayesian decision theory and is the most popular conventional supervised classification technique [68,69]. However, for the MLC method, it is difficult to satisfy the assumption of a normally distributed dataset.…”

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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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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“…The mean OA of each classifier method ranges between 59% and 72%. SVMR obtained higher accuracy than SVMP and SVML [26,71]. Lower accuracy of SVML means that linear decision boundaries are not suitable for classifying patterns in this data [72].…”

Section: Classmentioning

confidence: 86%

“…The second approach to increase classification accuracy (i.e., by developing new algorithms) has been extensively used by the remote sensing community, which has rapidly adopted and adapted novel machine learning image classification approaches [25][26][27]. The combination of existing classifiers (ensemble of classifiers) has, however, received comparatively little attention, although it is known that ensemble classifiers increase classification accuracy because no single classifier outperforms the others [28].…”

Section: Introductionmentioning

confidence: 99%

A Cloud-Based Multi-Temporal Ensemble Classifier to Map Smallholder Farming Systems

et al. 2018

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Smallholder farmers cultivate more than 80% of the cropland area available in Africa. The intrinsic characteristics of such farms include complex crop-planting patterns, and small fields that are vaguely delineated. These characteristics pose challenges to mapping crops and fields from space. In this study, we evaluate the use of a cloud-based multi-temporal ensemble classifier to map smallholder farming systems in a case study for southern Mali. The ensemble combines a selection of spatial and spectral features derived from multi-spectral Worldview-2 images, field data, and five machine learning classifiers to produce a map of the most prevalent crops in our study area. Different ensemble sizes were evaluated using two combination rules, namely majority voting and weighted majority voting. Both strategies outperform any of the tested single classifiers. The ensemble based on the weighted majority voting strategy obtained the higher overall accuracy (75.9%). This means an accuracy improvement of 4.65% in comparison with the average overall accuracy of the best individual classifier tested in this study. The maximum ensemble accuracy is reached with 75 classifiers in the ensemble. This indicates that the addition of more classifiers does not help to continuously improve classification results. Our results demonstrate the potential of ensemble classifiers to map crops grown by West African smallholders. The use of ensembles demands high computational capability, but the increasing availability of cloud computing solutions allows their efficient implementation and even opens the door to the data processing needs of local organizations.

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“…The method relies on the assumption that data of each used band as input of algorithm have normal distribution and it is only need to select a number of pixels to correctly estimate the mean vector and covariance-variance matrix [12]. The technique classifies each pixel of satellite image only in a single class based on its features [13].…”

Section: Image Processing Algorithmsmentioning

confidence: 99%

Advanced Investigation of Remote Sensing to Geological Mapping of Zefreh Region in Central Iran

Mohammadizad¹,

Arfania²

2017

OJG

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This study has tried to prove the ability of remote sensing techniques to extract information necessary for preparation of geological mapping of the earth's surface using multi-spectral satellite images which are rich sources of Earth's surface information. In this study, the surface geological mappings of Zefreh region have been investigated through ASTER, OLI, and IRS-PAN remote sensing data. To prepare the geological map, preprocessing steps and reducing noises from data using MNF algorithm were firstly carried out. Then a set of processing algorithms and image classification methods are included; the band rationing, color composite and pixel classification based on maximum likelihood, spectral and sub-pixel classification methods of spectral angle mapper (SAM), spectral feature fitting (SFF), linear spectral differentiation (LSU), hill-shade images and automatic lineament extraction were used. Confusion matrix was formed for all classified images through control points were randomly selected from 1:25,000 map of the region to determine the accuracy of obtained results, which indicated the maximum accuracy (up to 90%) of output images. Comparing the results obtained from these methods with the map prepared by ground operations confirmed accuracy results. Finally, the surface geology and fault map of Zafreh region was produced by combining detected geological formations and tectonic lineaments.

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An Objective Analysis of Support Vector Machine Based Classification for Remote Sensing

Cited by 159 publications

References 12 publications

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

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

A Cloud-Based Multi-Temporal Ensemble Classifier to Map Smallholder Farming Systems

Advanced Investigation of Remote Sensing to Geological Mapping of Zefreh Region in Central Iran

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