Date of download: 6/29/2016 Copyright © 2016 SPIE. All rights reserved. Overview of the sparse correlaton model for land-use scene classification. Figure Legend: From: Sparse coding-based correlaton model for land-use scene classification in high- resolution remote-sensing images J. Appl. Remote Sens. 2016;10(4): doi: /1.JRS
Date of download: 6/29/2016 Copyright © 2016 SPIE. All rights reserved. Examples of the ground truth images from the 21 land-use scene dataset. Figure Legend: From: Sparse coding-based correlaton model for land-use scene classification in high- resolution remote-sensing images J. Appl. Remote Sens. 2016;10(4): doi: /1.JRS
Date of download: 6/29/2016 Copyright © 2016 SPIE. All rights reserved. Classification performances using OVO and OVA schemes produced by the (a) BoVW, 8 (b) SCBoVW, 28 (c) SPM, 12 (d) correlaton, 20 (e) MS-based correlaton, 21 and (f) the proposed sparse correlaton methods. The error bars indicate the standard deviation. Figure Legend: From: Sparse coding-based correlaton model for land-use scene classification in high- resolution remote-sensing images J. Appl. Remote Sens. 2016;10(4): doi: /1.JRS
Date of download: 6/29/2016 Copyright © 2016 SPIE. All rights reserved. Comparison of classification accuracy with different numbers of visual words using the BoVW, 8 SCBoVW, 28 SPM, 12 correlaton, 20 and MS-based correlaton, 21 and the proposed sparse correlaton models. Figure Legend: From: Sparse coding-based correlaton model for land-use scene classification in high- resolution remote-sensing images J. Appl. Remote Sens. 2016;10(4): doi: /1.JRS
Date of download: 6/29/2016 Copyright © 2016 SPIE. All rights reserved. Classification performance variation using different sizes of sparse correlaton codebook with different sizes of visual codebook for the sparse correlaton model. Figure Legend: From: Sparse coding-based correlaton model for land-use scene classification in high- resolution remote-sensing images J. Appl. Remote Sens. 2016;10(4): doi: /1.JRS
Date of download: 6/29/2016 Copyright © 2016 SPIE. All rights reserved. Confusion matrix for the land-use dataset using the sparse correlaton model. The average performance of the dataset is 84.31±0.51%. Accuracy higher than 5% is shown in the figure. Figure Legend: From: Sparse coding-based correlaton model for land-use scene classification in high- resolution remote-sensing images J. Appl. Remote Sens. 2016;10(4): doi: /1.JRS