Spectral–Spatial Hyperspectral Image Classification With Edge-Preserving Filtering
Abstract
The integration of spatial context in the classification of hyperspectral images is known to be an effective way in improving classification accuracy. In this paper, a novel spectral-spatial classification framework based on edge-preserving filtering is proposed. The proposed framework consists of the following three steps. First, the hyperspectral image is classified using a pixelwise classifier, e.g., the support vector machine classifier. Then, the resulting classification map is represented as multiple probability maps, and edge-preserving filtering is conducted on each probability map, with the first principal component or the first three principal components of the hyperspectral image serving as the gray or color guidance image. Finally, according to the filtered probability maps, the class of each pixel is selected based on the maximum probability. Experimental results demonstrate that the proposed edge-preserving filtering based classification method can improve the classification accuracy significantly in a very short time. Thus, it can be easily applied in real applications.
Integrity signals
Registry
Cites
Statistics
Claims the paper makes
Claims other papers make about this one
Each is checked against this paper’s own text: does it say what the citing paper says it does?
Feature extraction and feature selection are dedicated to learning temporal, spatial and spectral features in images and even intrinsic priors in the production line to narrow intraclass variation and expand interclass distance.
“Essentially, feature extraction and feature selection are dedicated to learning the temporal, spatial [13-15] and spectral features [16, 17] in images and even the intrinsic priors in the production line [18] to narrow the intraclass variation and expand the interclass distance.”
From Automated Visual Defect Classification for Flat Steel Surface: A Survey (2020) · cites this paper as [15]
Cites
LIBSVM: A library for support vector machines
Chang, C.-C., & Lin, C.-J. (2011) · ACM Transactions on Intelligent Systems and Technology · doi:10.1145/1961189.1961199 · Cited by 40,918 · see citing works
Bilateral Filtering for Gray and Color Images
Tomasi, C., & Manduchi, R. (2002) · Sixth International Conference on Computer Vision (IEEE Cat. No.98CH36271) · doi:10.1109/iccv.1998.710815 · Cited by 8,177 · cited 2× · see citing works
“…the last decade, many different EPFs, e.g., the joint bilateral filter [33], the weighted least-squares (WLS) filter [36], the guided filter [34], the domain transform filter [45], the local linear Stein’s unbiased risk estimate filter [46], and the L0 gradient filter [47], have been proposed.”
BackgroundMethodSingle image haze removal using dark channel prior
He, K., Sun, J., & Tang, X. (2011) · IEEE Transactions on Pattern Analysis and Machine Intelligence · doi:10.1109/tpami.2010.168 · Cited by 7,601 · see citing works
He, K., Sun, J., & Tang, X. (2013) · IEEE Transactions on Pattern Analysis and Machine Intelligence · doi:10.1109/tpami.2012.213 · Cited by 5,503 · see citing works
Classification of hyperspectral remote sensing images with support vector machines
Melgani, F., & Bruzzone, L. (2004) · IEEE Transactions on Geoscience and Remote Sensing · doi:10.1109/tgrs.2004.831865 · Cited by 4,426 · see citing works
Pratt, W. K. (2002) · Wiley eBooks · doi:10.1002/0471221325 · Cited by 4,348 · see citing works
On the mean accuracy of statistical pattern recognizers
Hughes, G. W. (1968) · IEEE Transactions on Information Theory · doi:10.1109/tit.1968.1054102 · Cited by 2,890 · see citing works
Image Fusion With Guided Filtering
Shutao Li, A., Kang, X., & Hu, J. (2013) · IEEE Transactions on Image Processing · doi:10.1109/tip.2013.2244222 · Cited by 1,789 · see citing works
Self-citationRecent advances in techniques for hyperspectral image processing
Plaza, A., Benediktsson, J. A., Boardman, J. W., Brazile, J., Bruzzone, L., Camps‐Valls, G., Chanussot, J., Fauvel, M., Gamba, P. E., Gualtieri, A. G., et al. (2009) · Remote Sensing of Environment · doi:10.1016/j.rse.2007.07.028 · Cited by 1,705 · see citing works
Self-citationEdge-preserving decompositions for multi-scale tone and detail manipulation
Farbman, Z., Fattal, R., Lischinski, D., et al. (2008) · ACM Transactions on Graphics · doi:10.1145/1360612.1360666 · Cited by 1,469 · see citing works
Classification of hyperspectral data from urban areas based on extended morphological profiles
Benediktsson, J. A., Palmason, J. A., & Sveinsson, J. R. (2005) · IEEE Transactions on Geoscience and Remote Sensing · doi:10.1109/tgrs.2004.842478 · Cited by 1,399 · see citing works
Self-citationInvestigation of the random forest framework for classification of hyperspectral data
Ham, J., Chen, Y., Crawford, M. M., & Ghosh, J. (2005) · IEEE Transactions on Geoscience and Remote Sensing · doi:10.1109/tgrs.2004.842481 · Cited by 1,289 · see citing works
Advances in Spectral-Spatial Classification of Hyperspectral Images
Fauvel, M., Tarabalka, Y., Benediktsson, J. A., Chanussot, J., & Tilton, J. C. (2012) · Proceedings of the IEEE · doi:10.1109/jproc.2012.2197589 · Cited by 1,288 · see citing works
Self-citationComposite Kernels for Hyperspectral Image Classification
Camps‐Valls, G., Gómez‐Chova, L., Muñoz-Marı́, J., Vila‐Francés, J., & Calpe‐Maravilla, J. (2006) · IEEE Geoscience and Remote Sensing Letters · doi:10.1109/lgrs.2005.857031 · Cited by 1,069 · see citing works
Image smoothing via L 0 gradient minimization
Xu, L., Lu, C., Xu, Y., & Jia, J. (2011) · ACM SIGGRAPH Conference and Exhibition on Computer Graphics and Interactive Techniques in Asia (SIGGRAPH Asia) · doi:10.1145/2024156.2024208 · Cited by 886 · see citing works
A Fast Approximation of the Bilateral Filter Using a Signal Processing Approach
Paris, S., & Durand, F. (2007) · International Journal of Computer Vision · doi:10.1007/s11263-007-0110-8 · Cited by 796 · see citing works
Image smoothing via L 0 gradient minimization
Li, X., Lu, C., Xu, Y., & Jia, J. (2011) · ACM Transactions on Graphics · doi:10.1145/2070781.2024208 · Cited by 786 · see citing works
Benediktsson, J. A., Pesaresi, M., & Arnason, K. (2003) · IEEE Transactions on Geoscience and Remote Sensing · doi:10.1109/tgrs.2003.814625 · Cited by 762 · see citing works
Self-citationSVM- and MRF-Based Method for Accurate Classification of Hyperspectral Images
Tarabalka, Y., Fauvel, M., Chanussot, J., & Benediktsson, J. A. (2010) · IEEE Geoscience and Remote Sensing Letters · doi:10.1109/lgrs.2010.2047711 · Cited by 749 · see citing works
Self-citationLi, J., Bioucas‐Dias, J. M., & Plaza, A. (2011) · IEEE Transactions on Geoscience and Remote Sensing · doi:10.1109/tgrs.2011.2162649 · Cited by 732 · see citing works
Spectral–Spatial Classification of Hyperspectral Imagery Based on Partitional Clustering Techniques
Tarabalka, Y., Benediktsson, J. A., & Chanussot, J. (2009) · IEEE Transactions on Geoscience and Remote Sensing · doi:10.1109/tgrs.2009.2016214 · Cited by 680 · see citing works
Self-citationFast Cost-Volume Filtering for Visual Correspondence and Beyond
Hosni, A., Rhemann, C., Bleyer, M., Rother, C., & Gelautz, M. (2012) · IEEE Transactions on Pattern Analysis and Machine Intelligence · doi:10.1109/tpami.2012.156 · Cited by 676 · see citing works
Domain transform for edge-aware image and video processing
Gastal, E. S. L., & Oliveira, M. M. (2011) · ACM Transactions on Graphics · doi:10.1145/2010324.1964964 · Cited by 637 · see citing works
Domain transform for edge-aware image and video processing
Gastal, E. S. L., & Oliveira, M. M. (2011) · ACM SIGGRAPH Conference Papers (SIGGRAPH) · doi:10.1145/1964921.1964964 · Cited by 591 · see citing works
Segmentation and classification of hyperspectral images using watershed transformation
Tarabalka, Y., Chanussot, J., & Benediktsson, J. A. (2010) · Pattern Recognition · doi:10.1016/j.patcog.2010.01.016 · Cited by 562 · see citing works
Self-citationHyperspectral Image Classification via Kernel Sparse Representation
Chen, Y., Nasrabadi, N. M., & Tran, T. D. (2012) · IEEE Transactions on Geoscience and Remote Sensing · doi:10.1109/tgrs.2012.2201730 · Cited by 515 · see citing works
Locality-Preserving Dimensionality Reduction and Classification for Hyperspectral Image Analysis
Li, W., Prasad, S., Fowler, J. E., & Bruce, L. M. (2011) · IEEE Transactions on Geoscience and Remote Sensing · doi:10.1109/tgrs.2011.2165957 · Cited by 472 · see citing works
Huang, X., & Zhang, L. (2012) · IEEE Transactions on Geoscience and Remote Sensing · doi:10.1109/tgrs.2012.2202912 · Cited by 463 · see citing works
Hyperspectral Image Classification With Independent Component Discriminant Analysis
Villa, A., Benediktsson, J. A., Chanussot, J., & Christian Jutten, B. (2011) · IEEE Transactions on Geoscience and Remote Sensing · doi:10.1109/tgrs.2011.2153861 · Cited by 429 · see citing works
Self-citationMura, M. D., Villa, A., Benediktsson, J. A., Chanussot, J., & Bruzzone, L. (2010) · IEEE Geoscience and Remote Sensing Letters · doi:10.1109/lgrs.2010.2091253 · Cited by 421 · see citing works
Self-citationHyperspectral Image Segmentation Using a New Bayesian Approach With Active Learning
Li, J., Bioucas‐Dias, J. M., & Plaza, A. (2011) · IEEE Transactions on Geoscience and Remote Sensing · doi:10.1109/tgrs.2011.2128330 · Cited by 418 · see citing works
Plaza, A., Martinez, P., Plaza, J., et al. (2005) · IEEE Transactions on Geoscience and Remote Sensing · doi:10.1109/tgrs.2004.841417 · Cited by 416 · see citing works
On Combining Multiple Features for Hyperspectral Remote Sensing Image Classification
Zhang, L., Zhang, L., Tao, D., et al. (2011) · IEEE Transactions on Geoscience and Remote Sensing · doi:10.1109/tgrs.2011.2162339 · Cited by 404 · see citing works
Adaptive Bilateral Filter for Sharpness Enhancement and Noise Removal
Zhang, B., & Allebach, J. P. (2008) · IEEE Transactions on Image Processing · doi:10.1109/tip.2008.919949 · Cited by 403 · see citing works
Tree Species Classification in Boreal Forests With Hyperspectral Data
Dalponte, M., Ørka, H. O., Gobakken, T., Gianelle, D., & Næsset, E. (2012) · IEEE Transactions on Geoscience and Remote Sensing · doi:10.1109/tgrs.2012.2216272 · Cited by 382 · see citing works
Li, J., Bioucas‐Dias, J. M., & Plaza, A. (2012) · IEEE Transactions on Geoscience and Remote Sensing · doi:10.1109/tgrs.2012.2205263 · Cited by 339 · see citing works
Semisupervised Neural Networks for Efficient Hyperspectral Image Classification
Ratle, F., Camps‐Valls, G., & Weston, J. (2010) · IEEE Transactions on Geoscience and Remote Sensing · doi:10.1109/tgrs.2009.2037898 · Cited by 318 · see citing works
Tensor Discriminative Locality Alignment for Hyperspectral Image Spectral–Spatial Feature Extraction
Zhang, L., Zhang, L., Tao, D., & Huang, X. (2012) · IEEE Transactions on Geoscience and Remote Sensing · doi:10.1109/tgrs.2012.2197860 · Cited by 286 · see citing works
A spatial–spectral kernel-based approach for the classification of remote-sensing images
Fauvel, M., Chanussot, J., & Benediktsson, J. A. (2011) · Pattern Recognition · doi:10.1016/j.patcog.2011.03.035 · Cited by 281 · see citing works
Self-citationTarabalka, Y., Chanussot, J., & Benediktsson, J. A. (2010) · IEEE Transactions on Systems Man and Cybernetics Part B (Cybernetics) · doi:10.1109/tsmcb.2009.2037132 · Cited by 275 · see citing works
Self-citationFast multi-exposure image fusion with median filter and recursive filter
Li, S., & Kang, X. (2012) · IEEE Transactions on Consumer Electronics · doi:10.1109/tce.2012.6227469 · Cited by 248 · see citing works
Self-citationMultiple Spectral–Spatial Classification Approach for Hyperspectral Data
Tarabalka, Y., Benediktsson, J. A., Chanussot, J., et al. (2010) · IEEE Transactions on Geoscience and Remote Sensing · doi:10.1109/tgrs.2010.2062526 · Cited by 229 · see citing works
Self-citationZhong, Y., & Zhang, L. (2011) · IEEE Transactions on Geoscience and Remote Sensing · doi:10.1109/tgrs.2011.2162589 · Cited by 209 · see citing works
Moser, G., & Serpico, S. B. (2012) · IEEE Transactions on Geoscience and Remote Sensing · doi:10.1109/tgrs.2012.2211882 · Cited by 173 · see citing works
Switching Bilateral Filter With a Texture/Noise Detector for Universal Noise Removal
Lin, C.-H., Tsai, J.-S., & Chiu, C. (2010) · IEEE Transactions on Image Processing · doi:10.1109/tip.2010.2047906 · Cited by 156 · see citing works
Castrodad, A., Xing, Z., Greer, J. B., Bosch, E. H., Carin, L., & Sapiro, G. (2011) · IEEE Transactions on Geoscience and Remote Sensing · doi:10.1109/tgrs.2011.2163822 · Cited by 120 · see citing works
Spatio-Spectral Remote Sensing Image Classification With Graph Kernels
Camps‐Valls, G., Shervashidze, N., & Borgwardt, K. (2010) · IEEE Geoscience and Remote Sensing Letters · doi:10.1109/lgrs.2010.2046618 · Cited by 119 · see citing works
Di, W., & Crawford, M. M. (2011) · IEEE Transactions on Geoscience and Remote Sensing · doi:10.1109/tgrs.2011.2168566 · Cited by 116 · see citing works
Marpu, P., Pedergnana, M., Mura, M. D., et al. (2012) · IEEE Geoscience and Remote Sensing Letters · doi:10.1109/lgrs.2012.2203784 · Cited by 112 · see citing works
Visualization of Hyperspectral Images Using Bilateral Filtering
Kotwal, K., & Chaudhuri, S. (2010) · IEEE Transactions on Geoscience and Remote Sensing · doi:10.1109/tgrs.2009.2037950 · Cited by 110 · see citing works
Hyperspectral Image Classification by Bootstrap AdaBoost With Random Decision Stumps
Kawaguchi, S., & Nishii, R. (2007) · IEEE Transactions on Geoscience and Remote Sensing · doi:10.1109/tgrs.2007.903708 · Cited by 65 · see citing works
LLSURE: Local Linear SURE-Based Edge-Preserving Image Filtering
Qiu, T., Wang, A., Yu, N., & Song, A. (2012) · IEEE Transactions on Image Processing · doi:10.1109/tip.2012.2214052 · Cited by 55 · see citing works
Flagged first, then by citations
Cited by
Sorted by citations
Deep Learning for Hyperspectral Image Classification: An Overview
Li, S., Song, W., Fang, L., Chen, Y., Ghamisi, P., & Benediktsson, J. A. (2019) · IEEE Transactions on Geoscience and Remote Sensing · doi:10.1109/tgrs.2019.2907932 · Cited by 2,007 · see citing works
Self-citationHe, L., Li, J., Liu, C., & Li, S. (2018) · IEEE Transactions on Geoscience and Remote Sensing · doi:10.1109/tgrs.2017.2765364 · Cited by 630 · see citing works
Self-citationClassification of Hyperspectral Images by Gabor Filtering Based Deep Network
Kang, X., Li, C., Li, S., & Lin, H. (2018) · IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing · doi:10.1109/jstars.2017.2767185 · Cited by 151 · see citing works
Self-citationAutomated Visual Defect Classification for Flat Steel Surface: A Survey
Luo, Q., Fang, X., Su, J., Zhou, J., Zhou, B., Yang, C., Liu, L., Gui, W., & Tian, L. (2020) · IEEE Transactions on Instrumentation and Measurement · doi:10.1109/tim.2020.3030167 · Cited by 148 · see citing works
- Authors
- Kang, X., Li, S., & Benediktsson, J. A.
- Affiliations
- University of Iceland (IS)
- ORCID
- Kang, Xudong
- Venue
- IEEE Transactions on Geoscience and Remote Sensing
- Published
- 2014-05
- Volume
- 52
- Issue
- 5
- Pages
- 2666-2677
- ISSN
- 0196-2892 (ISSN-L)
- 1558-0644
- OpenAlex
- W2166923144
- Cited by
- 704
- References
- 52
- Citations by year
- 2026: 10 · 2025: 18 · 2024: 44 · 2023: 59 · 2022: 64 · 2021: 77 · 2020: 79 · 2019: 95 · 2018: 95 · 2017: 73 · 2016: 50 · 2015: 25 · 2014: 14 · 2013: 1
- FWCI
- 58.48
- Type
- journal article
- Language
- en
- Open access
- closed
- Locations
- IEEE Transactions on Geoscience and Remote Sensing (publishedVersion)
- Funders
- National Natural Science Foundation of China
- Hunan Provincial Innovation Foundation for Postgraduate
- Chinese Scholarship Award for Excellent Doctoral Student
- Topics
- Physical Sciences › Engineering › Media Technology › Remote-Sensing Image Classification
- Physical Sciences › Engineering › Media Technology › Advanced Image Fusion Techniques
- Physical Sciences › Chemistry › Analytical Chemistry › Spectroscopy and Chemometric Analyses
- Keywords
- hyperspectral image classification, spectral-spatial classification, edge-preserving filtering, spatial context, probability maps, support vector machine, principal components
About these checks
Registry, retraction, Cites and statistics results are computed, not judged, with one exception: a printed reference that no index lists is read once by a model before it counts as a citation error. Claims are extracted by a model and kept only when their quote appears verbatim in the text; each cited source is then checked, with a model’s help, for whether it says what the claim relies on. Those verdicts can be wrong — read the claim’s quote and the cited source before relying on one.
Statistics and arithmetic are recomputed, not judged.
Confidence bands are shown only for fields calibrated on our annotated test set. None is calibrated yet, so no band is shown.
What counts as a claim ›How we measure confidence ›How we check this ›
Are you an author, or do you see a problem with this page? Request a review
