{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,6,3]],"date-time":"2026-06-03T02:02:28Z","timestamp":1780452148335,"version":"3.54.1"},"reference-count":53,"publisher":"MDPI AG","issue":"7","license":[{"start":{"date-parts":[[2020,3,30]],"date-time":"2020-03-30T00:00:00Z","timestamp":1585526400000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"name":"he National Natural Science Foundation 438 of China","award":["U1711267"],"award-info":[{"award-number":["U1711267"]}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Remote Sensing"],"abstract":"<jats:p>The classification of hyperspectral remote sensing images is difficult due to the curse of dimensionality. Therefore, it is necessary to find an effective way to reduce the dimensions of such images. The Relief-F method has been introduced for supervising dimensionality reduction, but the band subset obtained by this method has a large number of continuous bands, resulting in a reduction in the classification accuracy. In this paper, an improved method\u2014called Partitioned Relief-F\u2014is presented to mitigate the influence of continuous bands on classification accuracy while retaining important information. Firstly, the importance scores of each band are obtained using the original Relief-F method. Secondly, the whole band interval is divided in an orderly manner, using a partitioning strategy according to the correlation between the bands. Finally, the band with the highest importance score is selected in each sub-interval. To verify the effectiveness of the proposed Partitioned Relief-F method, a classification experiment is performed on three publicly available data sets. The dimensionality reduction methods Principal Component Analysis (PCA) and original Relief-F are selected for comparison. Furthermore, K-Means and Balanced Iterative Reducing and Clustering Using Hierarchies (BIRCH) are selected for comparison in terms of partitioning strategy. This paper mainly measures the effectiveness of each method indirectly, using the overall accuracy of the final classification. The experimental results indicate that the addition of the proposed partitioning strategy increases the overall accuracy of the three data sets by 1.55%, 3.14%, and 0.83%, respectively. In general, the proposed Partitioned Relief-F method can achieve significantly superior dimensionality reduction effects.<\/jats:p>","DOI":"10.3390\/rs12071104","type":"journal-article","created":{"date-parts":[[2020,4,1]],"date-time":"2020-04-01T03:44:13Z","timestamp":1585712653000},"page":"1104","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":28,"title":["Partitioned Relief-F Method for Dimensionality Reduction of Hyperspectral Images"],"prefix":"10.3390","volume":"12","author":[{"ORCID":"https:\/\/orcid.org\/0000-0002-1492-033X","authenticated-orcid":false,"given":"Jiansi","family":"Ren","sequence":"first","affiliation":[{"name":"School of Computer Science, China University of Geosciences, Wuhan 430078, China"},{"name":"Hubei Key Laboratory of Intelligent Geo-Information Processing, China University of Geosciences, Wuhan 430078, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-4588-8712","authenticated-orcid":false,"given":"Ruoxiang","family":"Wang","sequence":"additional","affiliation":[{"name":"School of Computer Science, China University of Geosciences, Wuhan 430078, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Gang","family":"Liu","sequence":"additional","affiliation":[{"name":"School of Computer Science, China University of Geosciences, Wuhan 430078, China"},{"name":"Hubei Key Laboratory of Intelligent Geo-Information Processing, China University of Geosciences, Wuhan 430078, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Ruyi","family":"Feng","sequence":"additional","affiliation":[{"name":"School of Computer Science, China University of Geosciences, Wuhan 430078, China"},{"name":"Hubei Key Laboratory of Intelligent Geo-Information Processing, China University of Geosciences, Wuhan 430078, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Yuanni","family":"Wang","sequence":"additional","affiliation":[{"name":"School of Computer Science, China University of Geosciences, Wuhan 430078, China"},{"name":"Hubei Key Laboratory of Intelligent Geo-Information Processing, China University of Geosciences, Wuhan 430078, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Wei","family":"Wu","sequence":"additional","affiliation":[{"name":"School of Computer Science, China University of Geosciences, Wuhan 430078, China"}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"1968","published-online":{"date-parts":[[2020,3,30]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","first-page":"463","DOI":"10.1111\/tgis.12164","article-title":"Hyperspectral remote sensing classifications: A perspective survey","volume":"20","author":"Chutia","year":"2015","journal-title":"Trans. GIS"},{"key":"ref_2","doi-asserted-by":"crossref","first-page":"480","DOI":"10.1109\/TGRS.2004.842478","article-title":"Classification of hyperspectral data from urban areas based on extended morphological profiles","volume":"43","author":"Benediktsson","year":"2005","journal-title":"IEEE Trans. Geosci. Remote Sens."},{"key":"ref_3","doi-asserted-by":"crossref","first-page":"652","DOI":"10.1109\/JPROC.2012.2197589","article-title":"Advances in spectral-spatial classification of hyperspectral images","volume":"101","author":"Fauvel","year":"2013","journal-title":"Proc. IEEE"},{"key":"ref_4","unstructured":"Bellman, R.E. (1957). Rand Corporation. Dynamic Programming, Princeton University Press."},{"key":"ref_5","doi-asserted-by":"crossref","unstructured":"Chang, C.I. (2007). Hyperspectral Data Exploitation: Theory and Applications, John Wiley & Sons.","DOI":"10.1002\/0470124628"},{"key":"ref_6","doi-asserted-by":"crossref","first-page":"6","DOI":"10.1109\/MGRS.2013.2244672","article-title":"Hyperspectral remote sensing data analysis and future challenges","volume":"1","author":"Plaza","year":"2013","journal-title":"IEEE Geosci. Remote Sens. Mag."},{"key":"ref_7","doi-asserted-by":"crossref","first-page":"4581","DOI":"10.1109\/TGRS.2018.2828029","article-title":"SuperPCA: A superpixelwise PCA approach for unsupervised feature extraction of hyperspectral imagery","volume":"56","author":"Jiang","year":"2018","journal-title":"IEEE Trans. Geosci. Remote Sens."},{"key":"ref_8","first-page":"1839","article-title":"Subalpine and alpine vegetation classification based on hyperspectral APEX and simulated EnMAP images","volume":"38","author":"Zagajewski","year":"2007","journal-title":"Int. J. Remote Sens."},{"key":"ref_9","doi-asserted-by":"crossref","first-page":"1586","DOI":"10.1109\/TGRS.2005.863297","article-title":"Independent component analysis-based dimensionality reduction with applications in hyperspectral image analysis","volume":"44","author":"Wang","year":"2006","journal-title":"IEEE Trans. Geosci. Remote Sens."},{"key":"ref_10","doi-asserted-by":"crossref","first-page":"2077","DOI":"10.1109\/LGRS.2017.2751559","article-title":"Locality adaptive discriminant analysis for spectral\u2013spatial classification of hyperspectral images","volume":"14","author":"Wang","year":"2017","journal-title":"IEEE Geosci. Remote Sens. Lett."},{"key":"ref_11","doi-asserted-by":"crossref","first-page":"5421","DOI":"10.1088\/0031-9155\/50\/23\/001","article-title":"Hyperspectral and multispectral bioluminescence optical tomography for small animal imaging","volume":"50","author":"Chaudhari","year":"2005","journal-title":"Phys. Med. Biol."},{"key":"ref_12","doi-asserted-by":"crossref","first-page":"2331","DOI":"10.1109\/TGRS.2002.804721","article-title":"Dimensionality reduction of hyperspectral data using discrete wavelet transform feature extraction","volume":"40","author":"Bruce","year":"2002","journal-title":"IEEE Trans. Geosci. Remote Sens."},{"key":"ref_13","first-page":"1501","article-title":"Detection of infected tephritidae citrus fruit based on hyperspectral imaging and two-band ratio algorithm","volume":"311","author":"Jiang","year":"2011","journal-title":"Adv. Mater. Res. Trans. Tech. Publ."},{"key":"ref_14","doi-asserted-by":"crossref","first-page":"4352","DOI":"10.1109\/JSTARS.2015.2509461","article-title":"Automatic band selection using spatial-structure information and classifier-based clustering","volume":"9","author":"Cao","year":"2016","journal-title":"IEEE J. Sel. Top. Appl. Earth Obs. Remote Sens."},{"key":"ref_15","doi-asserted-by":"crossref","first-page":"237","DOI":"10.1016\/j.rse.2007.01.003","article-title":"Detecting Tamarisk species (Tamarix spp.) in riparian habitats of Southern California using high spatial resolution hyperspectral imagery","volume":"109","author":"Hamada","year":"2007","journal-title":"Remote Sens. Environ."},{"key":"ref_16","doi-asserted-by":"crossref","first-page":"1135","DOI":"10.1109\/LGRS.2011.2158185","article-title":"Semisupervised band clustering for dimensionality reduction of hyperspectral imagery","volume":"8","author":"Su","year":"2011","journal-title":"IEEE Geosci. Remote Sens. Lett."},{"key":"ref_17","doi-asserted-by":"crossref","first-page":"2659","DOI":"10.1109\/JSTARS.2014.2312539","article-title":"Optimized hyperspectral band selection using particle swarm optimization","volume":"7","author":"Su","year":"2014","journal-title":"IEEE J. Sel. Top. Appl. Earth Obs. Remote Sens."},{"key":"ref_18","doi-asserted-by":"crossref","first-page":"352","DOI":"10.1016\/j.postharvbio.2015.09.027","article-title":"Nondestructive detection of chilling injury in cucumber fruit using hyperspectral imaging with feature selection and supervised classification","volume":"111","author":"Cen","year":"2016","journal-title":"Postharvest Biol. Technol."},{"key":"ref_19","doi-asserted-by":"crossref","first-page":"580","DOI":"10.1016\/j.ecss.2006.05.048","article-title":"Imaging spectroscopy as a tool to study sediment characteristics on a tidal sandbank in the Westerschelde","volume":"69","author":"Deronde","year":"2006","journal-title":"Estuar. Coast. Shelf Sci."},{"key":"ref_20","doi-asserted-by":"crossref","first-page":"1360","DOI":"10.1109\/36.934069","article-title":"A new search algorithm for feature selection in hyperspectral remote sensing images","volume":"39","author":"Serpico","year":"2001","journal-title":"IEEE Trans. Geosci. Remote Sens."},{"key":"ref_21","doi-asserted-by":"crossref","first-page":"2516","DOI":"10.1109\/JSTARS.2013.2294961","article-title":"New optimized spectral indices for identifying and monitoring winter wheat diseases","volume":"7","author":"Huang","year":"2014","journal-title":"IEEE J. Sel. Top. Appl. Earth Obs. Remote Sens."},{"key":"ref_22","doi-asserted-by":"crossref","first-page":"1007","DOI":"10.1080\/01431160512331314083","article-title":"Support vector machines for classification in remote sensing","volume":"26","author":"Pal","year":"2005","journal-title":"Int. J. Remote Sens."},{"key":"ref_23","doi-asserted-by":"crossref","first-page":"612","DOI":"10.1109\/TIP.2010.2076296","article-title":"Kernel maximum autocorrelation factor and minimum noise fraction transformations","volume":"20","author":"Nielsen","year":"2010","journal-title":"IEEE Trans. Image Process."},{"key":"ref_24","doi-asserted-by":"crossref","unstructured":"Fauvel, M., Chanussot, J., and Benediktsson, J.A. (2006, January 7\u20139). Kernel Principal Component Analysis for Feature Reduction in Hyperspectrale Images Analysis. Proceedings of the 7th Nordic Signal Processing Symposium-NORSIG 2006, Rejkjavik, Iceland.","DOI":"10.1109\/NORSIG.2006.275232"},{"key":"ref_25","doi-asserted-by":"crossref","unstructured":"Li, X., Zhang, L., and You, J. (2018). Hyperspectral Image Classification Based on Two-Stage Subspace Projection. Remote Sens., 10.","DOI":"10.3390\/rs10101565"},{"key":"ref_26","doi-asserted-by":"crossref","unstructured":"Zhang, L., Su, H., and Shen, J. (2019). Hyperspectral Dimensionality Reduction Based on Multiscale Superpixelwise Kernel Principal Component Analysis. Remote Sens., 11.","DOI":"10.3390\/rs11101219"},{"key":"ref_27","doi-asserted-by":"crossref","first-page":"9632569","DOI":"10.1155\/2018\/9632569","article-title":"Ensemble Learning Based Multiple Kernel Principal Component Analysis for Dimensionality Reduction and Classification of Hyperspectral Imagery","volume":"2018","author":"Binol","year":"2018","journal-title":"Math. Probl. Eng."},{"key":"ref_28","doi-asserted-by":"crossref","unstructured":"Zhao, B., Gao, L., and Zhang, B. (2016, January 10\u201315). An Optimized Method of Kernel Minimum noise Fraction for Dimensionality Reduction of Hyperspectral Imagery. Proceedings of the 2016 IEEE International Geoscience and Remote Sensing Symposium (IGARSS), Beijing, China.","DOI":"10.1109\/IGARSS.2016.7729003"},{"key":"ref_29","doi-asserted-by":"crossref","unstructured":"G\u00f3mez-Chova, L., Nielsen, A.A., and Camps-Valls, G. (2011, January 24\u201329). Explicit Signal to Noise Ratio in Reproducing Kernel Hilbert Spaces. Proceedings of the 2011 IEEE International Geoscience and Remote Sensing Symposium, Vancouver, BC, Canada.","DOI":"10.1109\/IGARSS.2011.6049993"},{"key":"ref_30","doi-asserted-by":"crossref","unstructured":"Song, S., Zhou, H., Qin, H., Qian, K., Cheng, K., and Qian, J. (2017, January 24\u201326). Hyperspectral Image Anomaly Detecting Based on Kernel Independent Component Analysis. Proceedings of the Fourth Seminar on Novel Optoelectronic Detection Technology and Application, Nanjing, China.","DOI":"10.1117\/12.2309936"},{"key":"ref_31","doi-asserted-by":"crossref","unstructured":"Han, Z., Wan, J., Deng, L., and Liu, K. (2016). Oil Adulteration identification by hyperspectral imaging using QHM and ICA. PLoS ONE, 11.","DOI":"10.1371\/journal.pone.0146547"},{"key":"ref_32","doi-asserted-by":"crossref","first-page":"2035","DOI":"10.1109\/JSTARS.2013.2290316","article-title":"Spectral-spatial classification of hyperspectral image based on discriminant analysis","volume":"7","author":"Yuan","year":"2013","journal-title":"IEEE J. Sel. Top. Appl. Earth Obs. Remote Sens."},{"key":"ref_33","doi-asserted-by":"crossref","first-page":"103","DOI":"10.1016\/j.neucom.2012.11.016","article-title":"Rare signal component extraction based on kernel methods for anomaly detection in hyperspectral imagery","volume":"108","author":"Gu","year":"2013","journal-title":"Neurocomputing"},{"key":"ref_34","doi-asserted-by":"crossref","first-page":"4978","DOI":"10.1016\/j.optcom.2010.08.009","article-title":"Wavelet SVM in reproducing kernel Hilbert space for hyperspectral remote sensing image classification","volume":"283","author":"Du","year":"2010","journal-title":"Opt. Commun."},{"key":"ref_35","doi-asserted-by":"crossref","first-page":"2319","DOI":"10.1126\/science.290.5500.2319","article-title":"A global geometric framework for nonlinear dimensionality reduction","volume":"290","author":"Tenenbaum","year":"2000","journal-title":"Science"},{"key":"ref_36","doi-asserted-by":"crossref","first-page":"349","DOI":"10.15388\/Informatica.2019.209","article-title":"Hyperspectral Image Classification Using Isomap with SMACOF","volume":"30","author":"Filatovas","year":"2019","journal-title":"Informatica"},{"key":"ref_37","first-page":"695","article-title":"Hyperspectral image classification based on ISOMAP algorithm using neighborhood distance","volume":"29","author":"Songyang","year":"2014","journal-title":"Remote Sens. Technol. Appl."},{"key":"ref_38","doi-asserted-by":"crossref","first-page":"478","DOI":"10.1016\/j.rse.2014.11.024","article-title":"Improved time series land cover classification by missing-observation-adaptive nonlinear dimensionality reduction","volume":"158","author":"Yan","year":"2015","journal-title":"Remote Sens. Environ."},{"key":"ref_39","unstructured":"Qian, S.E., and Chen, G. (2007, January 23\u201328). A New Nonlinear Dimensionality Reduction Method with Application to Hyperspectral Image Analysis. Proceedings of the 2007 IEEE International Geoscience and Remote Sensing Symposium, Barcelona, Spain."},{"key":"ref_40","doi-asserted-by":"crossref","unstructured":"Feng, F., Li, W., Du, Q., and Zhang, B. (2017). Dimensionality reduction of hyperspectral image with graph-based discriminant analysis considering spectral similarity. Remote Sens., 9.","DOI":"10.3390\/rs9040323"},{"key":"ref_41","doi-asserted-by":"crossref","first-page":"118","DOI":"10.1109\/MGRS.2019.2911100","article-title":"Hyperspectral band selection: A review","volume":"7","author":"Sun","year":"2019","journal-title":"IEEE Geosci. Remote Sens. Mag."},{"key":"ref_42","doi-asserted-by":"crossref","first-page":"033556","DOI":"10.1117\/1.3257626","article-title":"Modified vegetation indices for Ganoderma disease detection in oil palm from field spectroradiometer data","volume":"3","author":"Shafri","year":"2009","journal-title":"J. Appl. Remote Sens."},{"key":"ref_43","doi-asserted-by":"crossref","first-page":"332","DOI":"10.1109\/TASE.2006.888048","article-title":"Band selection of hyperspectral images for automatic detection of poultry skin tumors","volume":"4","author":"Du","year":"2007","journal-title":"IEEE Trans. Autom. Sci. Eng."},{"key":"ref_44","doi-asserted-by":"crossref","first-page":"21","DOI":"10.1016\/j.rse.2012.09.019","article-title":"Development of spectral indices for detecting and identifying plant diseases","volume":"128","author":"Mahlein","year":"2013","journal-title":"Remote Sens. Environ."},{"key":"ref_45","doi-asserted-by":"crossref","first-page":"695","DOI":"10.1109\/LGRS.2011.2178815","article-title":"A new sequential algorithm for hyperspectral endmember extraction","volume":"9","author":"Du","year":"2012","journal-title":"IEEE Geosci. Remote Sens. Lett."},{"key":"ref_46","doi-asserted-by":"crossref","first-page":"2697","DOI":"10.1109\/JSTARS.2014.2320299","article-title":"A new band selection method for hyperspectral image based on data quality","volume":"7","author":"Sun","year":"2014","journal-title":"IEEE J. Sel. Top. Appl. Earth Obs. Remote Sens."},{"key":"ref_47","doi-asserted-by":"crossref","first-page":"2935","DOI":"10.1109\/TGRS.2014.2367010","article-title":"A novel feature selection approach based on FODPSO and SVM","volume":"53","author":"Ghamisi","year":"2014","journal-title":"IEEE Trans. Geosci. Remote Sens."},{"key":"ref_48","doi-asserted-by":"crossref","first-page":"225","DOI":"10.1016\/j.isprsjprs.2007.05.006","article-title":"A hyperspectral band selector for plant species discrimination","volume":"62","author":"Vaiphasa","year":"2007","journal-title":"ISPRS J. Photogramm. Remote Sens."},{"key":"ref_49","doi-asserted-by":"crossref","first-page":"4158","DOI":"10.1109\/TGRS.2007.904951","article-title":"Clustering-based hyperspectral band selection using information measures","volume":"45","author":"Pla","year":"2007","journal-title":"IEEE Trans. Geosci. Remote Sens."},{"key":"ref_50","doi-asserted-by":"crossref","unstructured":"Imbiriba, T., Bermudez, J.C.M., Richard, C., and Tourneret, J.Y. (September, January 31). Band Selection in RKHS for Fast Nonlinear Unmixing of Hyperspectral Images. Proceedings of the 2015 23rd European Signal Processing Conference (EUSIPCO), Nice, France.","DOI":"10.1109\/EUSIPCO.2015.7362664"},{"key":"ref_51","doi-asserted-by":"crossref","unstructured":"Li, S., and Qi, H. (2016, January 11\u201314). Sparse Representation Based Band Selection for Hyperspectral Images. Proceedings of the 2011 18th IEEE International Conference on Image Processing, Brussels, Belgium.","DOI":"10.1109\/ICIP.2011.6116223"},{"key":"ref_52","doi-asserted-by":"crossref","unstructured":"Sun, W., Jiang, M., Li, W., and Liu, Y. (2016). A symmetric sparse representation based band selection method for hyperspectral imagery classification. Remote Sens., 8.","DOI":"10.3390\/rs8030238"},{"key":"ref_53","doi-asserted-by":"crossref","first-page":"103","DOI":"10.1145\/235968.233324","article-title":"BIRCH: An efficient data clustering method for very large databases","volume":"25","author":"Zhang","year":"1996","journal-title":"ACM Sigmod Rec."}],"container-title":["Remote Sensing"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/www.mdpi.com\/2072-4292\/12\/7\/1104\/pdf","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,10,11]],"date-time":"2025-10-11T09:13:33Z","timestamp":1760174013000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.mdpi.com\/2072-4292\/12\/7\/1104"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2020,3,30]]},"references-count":53,"journal-issue":{"issue":"7","published-online":{"date-parts":[[2020,4]]}},"alternative-id":["rs12071104"],"URL":"https:\/\/doi.org\/10.3390\/rs12071104","relation":{},"ISSN":["2072-4292"],"issn-type":[{"value":"2072-4292","type":"electronic"}],"subject":[],"published":{"date-parts":[[2020,3,30]]}}}