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dc.contributor.authorOkwuashi, Onuwa
dc.contributor.authorNdehedehe, Christopher
dc.contributor.authorOlayinka, Dupe Nihinlola
dc.contributor.authorEyoh, Aniekan
dc.contributor.authorAttai, Hosanna
dc.date.accessioned2021-08-24T05:11:40Z
dc.date.available2021-08-24T05:11:40Z
dc.date.issued2021
dc.identifier.issn0143-1161
dc.identifier.doi10.1080/01431161.2021.1939910
dc.identifier.urihttp://hdl.handle.net/10072/407244
dc.description.abstractThe main problem posed by Polarimetric Synthetic Aperture Radar (PolSAR) image classification in remote sensing is the ability to develop classifiers that can substantially discern the different classes inherent in natural and man-made targets. Emphasis has shifted from the use of conventional classifiers to modern non-parametric classifiers such as the Artificial Neural Network (ANN) and Support Vector Machine (SVM), and most recently the hybrid Deep Neural Network (DNN) which is a fusion of Deep Learning (DL) and ANN. This research therefore presents the novel application of Deep Support Vector Machine (DSVM), which is a fusion of DL and SVM to PolSAR image classification. Two PolSAR images of Flevoland region in the Netherlands and Winnipeg in Canada are used as test beds for the experiment. The Lee filter is used to filter the images to suppress the speckle noise in the images. The Pauli decomposition is applied to decompose the images into |SHH+SVV|, |SHH−SVV|, |SHV| polarimetric channels. Then, the Gray Level Co-occurrence Matrix (GLCM) texture feature for |SHH+SVV|, |SHH−SVV|, |SHV| are extracted based on correlation, contrast, energy, and homogeneity statistics, using GLCM directions 0°, 45°, 90°, and 135° with an offset distance of 60. To enhance the efficiency of the model 8, 16, 32, 64, 128, and 256 quantization levels are explored. The DSVM classifier is implemented with four kernel functions: Exponential Radial Basis Function (ERBF), Gaussian Radial Basis Function (GRBF), neural, and polynomial. The first set of results is a comparison of the DSVM and SVM. The result of Flevoland image for ERBF, GRBF, neural, and polynomial kernels are 99.17 (73.39), 99.32 (74.62), 98.64 (71.28), and 99.34 (77.21), respectively; for the Winnipeg image for ERBF, GRBF, neural, and polynomial kernels are 98.65 (72.68), 98.67 (73.54), 98.27 (70.15), and 99.46 (75.03), respectively. The second set of results is a comparison of DSVM, SVM, DNN, Gaussian Mixture Model (GMM), K Nearest Neighbour (KNN), and K Means (KM) classifiers; the results for Flevoland image for DSVM, SVM, DNN, GMM, KNN, and KM are 99.12, 74.13, 96.29, 75.06, 75.85, and 21.43, respectively, while the results for Winnipeg image for DSVM, SVM, DNN, GMM, KNN, and KM are 98.76, 72.85, 95.64, 73.20, 73.91, and 25.60, respectively. Since the Kappa coefficient is presumed to be a more accurate measure for accuracy estimation, it is used to evaluate the performances of all the models. The computed Kappa coefficients for of DSVM, SVM, DNN, GMM, KNN, and KM for Flevoland are 92.45, 70.71, 88.76, 68.59, 68.62, and 18.89, respectively; while the computed Kappa coefficients for DSVM, SVM, DNN, GMM, KNN, and KM for Winnepeg are 92.45, 70.71, 88.76, 68.59, 68.62, and 18.89, respectively. Based on the metrics used to evaluate the performances of the experiments; the results show that the DSVM outperformed the other classifiers. The high accuracy obtained with the DSVM shows it is a state-of-the-art algorithm for PolSAR image classification and a significant progress in the latest of DL applications.
dc.description.peerreviewedYes
dc.languageen
dc.publisherTaylor & Francis
dc.relation.ispartofpagefrom6498
dc.relation.ispartofpageto6536
dc.relation.ispartofissue17
dc.relation.ispartofjournalInternational Journal of Remote Sensing
dc.relation.ispartofvolume42
dc.subject.fieldofresearchPhysical geography and environmental geoscience
dc.subject.fieldofresearchGeomatic engineering
dc.subject.fieldofresearchPhotogrammetry and remote sensing
dc.subject.fieldofresearchGeomatic engineering not elsewhere classified
dc.subject.fieldofresearchcode3709
dc.subject.fieldofresearchcode4013
dc.subject.fieldofresearchcode401304
dc.subject.fieldofresearchcode401399
dc.titleDeep support vector machine for PolSAR image classification
dc.typeJournal article
dc.type.descriptionC1 - Articles
dcterms.bibliographicCitationOkwuashi, O; Ndehedehe, C; Olayinka, DN; Eyoh, A; Attai, H, Deep support vector machine for PolSAR image classification, International Journal of Remote Sensing, 2021, 42 (17), pp. 6498-6536
dc.date.updated2021-08-24T05:05:44Z
dc.description.versionAccepted Manuscript (AM)
gro.rights.copyrightThis is an Author's Accepted Manuscript of an article published in the International Journal of Remote Sensing, 42 (17), pp. 6498-6536, 08 Aug 2021, copyright Taylor & Francis, available online at: https://doi.org/10.1080/01431161.2021.1939910
gro.hasfulltextFull Text
gro.griffith.authorNdehedehe, Christopher E.


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