{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,4,14]],"date-time":"2026-04-14T20:20:38Z","timestamp":1776198038500,"version":"3.50.1"},"reference-count":59,"publisher":"MDPI AG","issue":"2","license":[{"start":{"date-parts":[[2020,1,18]],"date-time":"2020-01-18T00:00:00Z","timestamp":1579305600000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Remote Sensing"],"abstract":"<jats:p>Crop classification in agriculture is one of important applications for polarimetric synthetic aperture radar (PolSAR) data. For agricultural crop discrimination, compared with single-temporal data, multi-temporal data can dramatically increase crop classification accuracies since the same crop shows different external phenomena as it grows up. In practice, the utilization of multi-temporal data encounters a serious problem known as a \u201cdimension disaster\u201d. Aiming to solve this problem and raise the classification accuracy, this study developed a feature dimension reduction method using stacked sparse auto-encoders (S-SAEs) for crop classification. First, various incoherent scattering decomposition algorithms were employed to extract a variety of detailed and quantitative parameters from multi-temporal PolSAR data. Second, based on analyzing the configuration and main parameters for constructing an S-SAE, a three-hidden-layer S-SAE network was built to reduce the dimensionality and extract effective features to manage the \u201cdimension disaster\u201d caused by excessive scattering parameters, especially for multi-temporal, quad-pol SAR images. Third, a convolutional neural network (CNN) was constructed and employed to further enhance the crop classification performance. Finally, the performances of the proposed strategy were assessed with the simulated multi-temporal Sentinel-1 data for two experimental sites established by the European Space Agency (ESA). The experimental results showed that the overall accuracy with the proposed method was raised by at least 17% compared with the long short-term memory (LSTM) method in the case of a 1% training ratio. Meanwhile, for a CNN classifier, the overall accuracy was almost 4% higher than those of the principle component analysis (PCA) and locally linear embedded (LLE) methods. The comparison studies clearly demonstrated the advantage of the proposed multi-temporal crop classification methodology in terms of classification accuracy, even with small training ratios.<\/jats:p>","DOI":"10.3390\/rs12020321","type":"journal-article","created":{"date-parts":[[2020,1,20]],"date-time":"2020-01-20T04:27:09Z","timestamp":1579494429000},"page":"321","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":38,"title":["Feature Dimension Reduction Using Stacked Sparse Auto-Encoders for Crop Classification with Multi-Temporal, Quad-Pol SAR Data"],"prefix":"10.3390","volume":"12","author":[{"given":"Jiao","family":"Guo","sequence":"first","affiliation":[{"name":"College of Mechanical and Electronic Engineering, Northwest A &amp; F University, Yangling 712100, China"},{"name":"Shaanxi Key Laboratory of Agricultural Information Perception and Intelligent Service, Yangling 712100, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Henghui","family":"Li","sequence":"additional","affiliation":[{"name":"College of Mechanical and Electronic Engineering, Northwest A &amp; F University, Yangling 712100, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-7751-0936","authenticated-orcid":false,"given":"Jifeng","family":"Ning","sequence":"additional","affiliation":[{"name":"College of Information Engineering, Northwest A&amp;F University, Yangling 712100, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Wenting","family":"Han","sequence":"additional","affiliation":[{"name":"Institute of Soil and Water Conservation, Northwest A&amp;F University, Yangling 712100, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Weitao","family":"Zhang","sequence":"additional","affiliation":[{"name":"School of Electronic Engineering, Xidian University, Xi\u2019an 710071, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Zheng-Shu","family":"Zhou","sequence":"additional","affiliation":[{"name":"Data61, Commonwealth Scientific and Industrial Research Organization, Kensington WA 6151, Australia"}],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"1968","published-online":{"date-parts":[[2020,1,18]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","first-page":"341","DOI":"10.1080\/10106049.2011.562309","article-title":"Monitoring US agriculture: The US department of agriculture, national agricultural statistics service, cropland data layer program","volume":"26","author":"Boryan","year":"2011","journal-title":"Geocarto Int."},{"key":"ref_2","doi-asserted-by":"crossref","first-page":"1312","DOI":"10.1016\/j.rse.2010.01.010","article-title":"A generalized regression-based model for forecasting winter wheat yields in Kansas and Ukraine using MODIS data","volume":"114","author":"Becker","year":"2010","journal-title":"Remote Sens. Environ."},{"key":"ref_3","doi-asserted-by":"crossref","first-page":"104","DOI":"10.1109\/JSTARS.2016.2605303","article-title":"Deriving maximum light use efficiency from crop growth model and satellite data to improve crop biomass estimation","volume":"10","author":"Dong","year":"2017","journal-title":"IEEE J. Sel. Top. Appl. Earth Obs. Remote Sens."},{"key":"ref_4","doi-asserted-by":"crossref","first-page":"1170","DOI":"10.1109\/TGRS.2015.2476352","article-title":"Terrain and surface modeling using polarimetric SAR data features","volume":"54","author":"Sabry","year":"2015","journal-title":"IEEE Trans. Geosci. Remote Sens."},{"key":"ref_5","doi-asserted-by":"crossref","first-page":"943","DOI":"10.1109\/JSTARS.2014.2371064","article-title":"Urban area SAR image man-made target extraction based on the product model and the time\u2013frequency analysis","volume":"8","author":"Wu","year":"2014","journal-title":"IEEE J. Sel. Top. Appl. Earth Obs. Remote Sens."},{"key":"ref_6","doi-asserted-by":"crossref","first-page":"1","DOI":"10.1109\/TGRS.2017.2666086","article-title":"Derivation of sea surface tidal current from spaceborne SAR constellation data","volume":"55","author":"Ren","year":"2017","journal-title":"IEEE Trans. Geosci. Remote Sens."},{"key":"ref_7","doi-asserted-by":"crossref","first-page":"3595","DOI":"10.1109\/JSTARS.2014.2387374","article-title":"A new method for land cover characterization and classification of polarimetric sar data using polarimetric signatures","volume":"8","author":"Jafari","year":"2015","journal-title":"IEEE J. Sel. Top. Appl. Earth Obs. Remote Sens."},{"key":"ref_8","doi-asserted-by":"crossref","first-page":"2299","DOI":"10.1080\/01431169408954244","article-title":"Classification of multi-look polarimetric SAR imagery based on complex Wishart distribution","volume":"15","author":"Lee","year":"1992","journal-title":"Int. J. Remote Sens."},{"key":"ref_9","doi-asserted-by":"crossref","first-page":"4143","DOI":"10.1109\/TGRS.2009.2023908","article-title":"Support vector machine for multifrequency SAR polarimetric data classification","volume":"47","author":"Lardeux","year":"2009","journal-title":"IEEE Trans. Geosci. Remote Sens."},{"key":"ref_10","first-page":"139","article-title":"Polarimetric classification of boreal forest using nonparametric feature selection and multiple classifiers","volume":"19","author":"Maghsoudi","year":"2012","journal-title":"Int. J. Appl. Earth Obs. Geoinform."},{"key":"ref_11","doi-asserted-by":"crossref","unstructured":"Hellmann, M. (1998, January 6\u201310). A new approach for interpretation of SAR-data using polarimetric techniques. Sensing and Managing the Environment. Proceedings of the IEEE International Geoscience and Remote Sensing, Symposium (IGARSS), Seattle, WA, USA.","DOI":"10.1109\/IGARSS.1998.703784"},{"key":"ref_12","doi-asserted-by":"crossref","first-page":"498","DOI":"10.1109\/36.485127","article-title":"A review of target decomposition theorems in radar polarimetry","volume":"34","author":"Cloude","year":"1996","journal-title":"IEEE Trans. Geosci. Remote Sens."},{"key":"ref_13","unstructured":"Lee, J.S., Grunes, M.R., Ainsworth, T.L., Pottier, E., Krogager, E., and Boerner, W.M. (2000, January 24\u201328). Quantitative comparison of classification capability: Fully-polarimetric versus partially polarimetric SAR. Proceedings of the IEEE International Geoscience & Remote Sensing Symposium(IGARSS), Honolulu, HI, USA."},{"key":"ref_14","unstructured":"Mohan, S., Das, A., Haldar, D., and Maity, S. (2011, January 26\u201330). Monitoring and retrieval of vegetation parameter using multi-frequency polarimetric SAR data. Proceedings of the International Asia-pacific Conference on Synthetic Aperture Radar (APSAR), Seoul, Korea."},{"key":"ref_15","doi-asserted-by":"crossref","first-page":"627","DOI":"10.1109\/LGRS.2018.2799877","article-title":"PolSAR image classification using polarimetric-feature-driven deep convolutional neural network","volume":"15","author":"Chen","year":"2018","journal-title":"IEEE Geosci. Remote Sens. Lett."},{"key":"ref_16","doi-asserted-by":"crossref","first-page":"1935","DOI":"10.1109\/LGRS.2016.2618840","article-title":"Polarimetric sar image classification using deep convolutional neural networks","volume":"13","author":"Zhou","year":"2017","journal-title":"IEEE Geosci. Remote Sens. Lett."},{"key":"ref_17","doi-asserted-by":"crossref","first-page":"1081","DOI":"10.1109\/36.312896","article-title":"Knowledge-based classification of polarimetric SAR images","volume":"32","author":"Pierce","year":"1994","journal-title":"IEEE Trans. Geosci. Remote Sens."},{"key":"ref_18","doi-asserted-by":"crossref","first-page":"960","DOI":"10.1109\/36.752214","article-title":"Experimental and model investigation on radar classification capability","volume":"37","author":"Ferrazzoli","year":"1999","journal-title":"IEEE Trans. Geosci. Remote Sens."},{"key":"ref_19","doi-asserted-by":"crossref","first-page":"423","DOI":"10.1109\/JSTARS.2011.2106198","article-title":"Crop classification using short-revisit multitemporal SAR data","volume":"4","author":"Skriver","year":"2011","journal-title":"IEEE J. Sel. Top. Appl. Earth Obs. Remote Sens."},{"key":"ref_20","doi-asserted-by":"crossref","first-page":"2500","DOI":"10.1109\/JSTARS.2016.2560141","article-title":"Parcel-based crop classification in Ukraine using Landsat-8 data and Sentinel-1A data","volume":"9","author":"Kussul","year":"2016","journal-title":"IEEE J. Sel. Top. Appl. Earth Obs. Remote Sens."},{"key":"ref_21","doi-asserted-by":"crossref","first-page":"415","DOI":"10.1016\/j.rse.2017.07.015","article-title":"Understanding the temporal behavior of crops using Sentinel-1 and Sentinel-2-like data for agricultural applications","volume":"199","author":"Veloso","year":"2017","journal-title":"Remote Sens. Environ."},{"key":"ref_22","doi-asserted-by":"crossref","first-page":"3082","DOI":"10.1109\/JSTARS.2016.2586102","article-title":"Rice mapping using RADARSAT-2 dual-and quad-pol data in a complex land-use watershed: Cau River Basin (Vietnam)","volume":"9","author":"Hoang","year":"2016","journal-title":"IEEE J. Sel. Top. Appl. Earth Obs. Remote Sens."},{"key":"ref_23","doi-asserted-by":"crossref","unstructured":"White, L., Millard, K., Banks, S., Richardson, M., Pasher, J., and Duffe, J. (2017). Moving to the RADARSAT constellation mission: Comparing synthesized compact polarimetry and dual polarimetry data with fully polarimetric RADARSAT-2 data for image classification of peatlands. Remote Sens., 9.","DOI":"10.3390\/rs9060573"},{"key":"ref_24","doi-asserted-by":"crossref","first-page":"3981","DOI":"10.1109\/TGRS.2009.2026052","article-title":"The contribution of ALOS PALSAR multipolarization and polarimetric data to crop classification","volume":"47","author":"McNairn","year":"2009","journal-title":"IEEE Trans. Geosci. Remote Sens."},{"key":"ref_25","doi-asserted-by":"crossref","first-page":"589","DOI":"10.1071\/MF13177","article-title":"Contribution of L-band SAR to systematic global mangrove monitoring","volume":"65","author":"Lucas","year":"2014","journal-title":"Mar. Freshw. Res."},{"key":"ref_26","doi-asserted-by":"crossref","first-page":"2928","DOI":"10.1109\/TGRS.2013.2267780","article-title":"Algorithm for sea surface wind retrieval from TerraSAR-X and TanDEM-X data","volume":"52","author":"Li","year":"2013","journal-title":"IEEE Trans. Geosci. Remote Sens."},{"key":"ref_27","doi-asserted-by":"crossref","unstructured":"Mattia, F., Satalino, G., Balenzano, A., D\u2019Urso, G., Capodici, F., Iacobellis, V., Milella, P., Gioia, A., Rinaldi, M., and Ruggieri, S. (2012, January 22\u201327). Time series of COSMO-SkyMed data for landcover classification and surface parameter retrieval over agricultural sites. Proceedings of the 2012 IEEE International Geoscience and Remote Sensing Symposium (IGARSS), Munich, Germany.","DOI":"10.1109\/IGARSS.2012.6352738"},{"key":"ref_28","doi-asserted-by":"crossref","unstructured":"Wei, S., Zhang, H., Wang, C., Wang, Y., and Xu, L. (2019). Multi-temporal SAR data large-scale crop mapping based on U-Net model. Remote Sens., 11.","DOI":"10.3390\/rs11010068"},{"key":"ref_29","doi-asserted-by":"crossref","unstructured":"Teimouri, N., Dyrmann, M., and J\u00f8rgensen, R.N. (2019). A Novel Spatio-Temporal FCN-LSTM Network for Recognizing Various Crop Types Using Multi-Temporal Radar Images. Remote Sens., 11.","DOI":"10.3390\/rs11080990"},{"key":"ref_30","doi-asserted-by":"crossref","first-page":"1619","DOI":"10.3390\/rs11131619","article-title":"DCN-Based Spatial Features for Improving Parcel-Based Crop Classification Using High-Resolution Optical Images and Multi-Temporal SAR Data","volume":"11","author":"Zhou","year":"2019","journal-title":"Remote Sens."},{"key":"ref_31","doi-asserted-by":"crossref","first-page":"430","DOI":"10.1016\/j.rse.2018.11.032","article-title":"Deep learning based multi-temporal crop classification","volume":"221","author":"Zhong","year":"2019","journal-title":"Remote Sen Environ."},{"key":"ref_32","first-page":"226","article-title":"Field-based rice classification in Wuhua county through integration of multi-temporal Sentinel-1A and Landsat-8 OLI data","volume":"69","author":"Yang","year":"2018","journal-title":"Int. J. Appl. Earth Obs. Geoinf."},{"key":"ref_33","doi-asserted-by":"crossref","first-page":"6111","DOI":"10.1109\/TGRS.2018.2832054","article-title":"Crop classification based on differential characteristics of H\/Alpha scattering parameters for multitemporal quad-and dual-polarization SAR images","volume":"56","author":"Guo","year":"2018","journal-title":"IEEE Trans. Geosci. Remote Sens."},{"key":"ref_34","doi-asserted-by":"crossref","unstructured":"Ji, S., Zhang, C., Xu, A., Shi, Y., and Duan, Y. (2018). 3D convolutional neural networks for crop classification with multi-temporal remote sensing images. Remote Sens., 10.","DOI":"10.3390\/rs10010075"},{"key":"ref_35","doi-asserted-by":"crossref","unstructured":"Sonobe, R. (2019). Parcel-Based Crop Classification Using Multi-Temporal TerraSAR-X Dual Polarimetric Data. Remote Sens., 11.","DOI":"10.3390\/rs11101148"},{"key":"ref_36","doi-asserted-by":"crossref","unstructured":"Xu, L., Zhang, H., Wang, C., Zhang, B., and Liu, M. (2019). Crop Classification Based on Temporal Information Using Sentinel-1 SAR Time-Series Data. Remote Sens., 11.","DOI":"10.3390\/rs11010053"},{"key":"ref_37","doi-asserted-by":"crossref","first-page":"963","DOI":"10.1109\/36.673687","article-title":"A three-component scattering model for polarimetric SAR data","volume":"36","author":"Freeman","year":"1998","journal-title":"IEEE Trans. Geosci. Remote Sens."},{"key":"ref_38","doi-asserted-by":"crossref","unstructured":"Huynen, J.R. (1978). Phenomenological Theory of Radar Targets, Technical University.","DOI":"10.1016\/B978-0-12-709650-6.50020-1"},{"key":"ref_39","doi-asserted-by":"crossref","first-page":"1699","DOI":"10.1109\/TGRS.2005.852084","article-title":"Four-component scattering model for polarimetric SAR image decomposition","volume":"104","author":"Yamaguchi","year":"2005","journal-title":"IEEE Trans. Geosci. Remote Sens."},{"key":"ref_40","doi-asserted-by":"crossref","first-page":"68","DOI":"10.1109\/36.551935","article-title":"An entropy based classification scheme for land applications of polarimetric SAR","volume":"35","author":"Cloude","year":"1997","journal-title":"IEEE Trans. Geosci. Remote Sens."},{"key":"ref_41","unstructured":"Jolliffe, I.T. (2002). Principal Component Analysis, Springer."},{"key":"ref_42","doi-asserted-by":"crossref","first-page":"2323","DOI":"10.1126\/science.290.5500.2323","article-title":"Nonlinear dimensionality reduction by locally linear embedding","volume":"290","author":"Roweis","year":"2000","journal-title":"Science"},{"key":"ref_43","doi-asserted-by":"crossref","first-page":"223","DOI":"10.1016\/j.ces.2003.09.012","article-title":"Nonlinear process monitoring using kernel principal component analysis","volume":"59","author":"Lee","year":"2004","journal-title":"Chem. Eng. Sci."},{"key":"ref_44","doi-asserted-by":"crossref","first-page":"1053","DOI":"10.1016\/j.patcog.2005.07.011","article-title":"Robust locally linear embedding","volume":"39","author":"Hong","year":"2006","journal-title":"Pattern Recognit."},{"key":"ref_45","doi-asserted-by":"crossref","first-page":"3639","DOI":"10.1109\/TGRS.2016.2636241","article-title":"Deep recurrent neural networks for hyperspectral image classification","volume":"55","author":"Mou","year":"2017","journal-title":"IEEE Trans. Geosci. Remote Sens."},{"key":"ref_46","doi-asserted-by":"crossref","unstructured":"Shrivastava, A., Gupta, A., and Girshick, R. (2016, January 27\u201330). Training region-based object detectors with online hard example mining. Proceedings of the IEEE Conference on Computer Vision & Pattern Recognition(CVPR), Las Vegas, NV, USA.","DOI":"10.1109\/CVPR.2016.89"},{"key":"ref_47","doi-asserted-by":"crossref","first-page":"504","DOI":"10.1126\/science.1127647","article-title":"Reducing the dimensionality of data with neural networks","volume":"313","author":"Hinton","year":"2006","journal-title":"Science"},{"key":"ref_48","doi-asserted-by":"crossref","first-page":"8","DOI":"10.1109\/MGRS.2017.2762307","article-title":"Deep learning in remote sensing: A comprehensive review and list of resources","volume":"5","author":"Zhu","year":"2017","journal-title":"IEEE Geosci. Remote Sens. Mag."},{"key":"ref_49","doi-asserted-by":"crossref","first-page":"2094","DOI":"10.1109\/JSTARS.2014.2329330","article-title":"Deep learning-based classification of hyperspectral data","volume":"7","author":"Chen","year":"2017","journal-title":"IEEE J. Sel. Top. Appl. Earth Obs. Remote Sens."},{"key":"ref_50","doi-asserted-by":"crossref","first-page":"265","DOI":"10.1016\/j.isprsjprs.2018.02.001","article-title":"Spectral-spatial classification of hyperspectral data with mutual information based segmented stacked autoencoder approach","volume":"138","author":"Paul","year":"2018","journal-title":"ISPRS J. Photogramm. Remote Sens."},{"key":"ref_51","first-page":"2249","article-title":"Unsupervised classification using polarimetric decomposition and the complex Wishart classifier","volume":"37","author":"Lee","year":"2002","journal-title":"IEEE Trans. Geosci. Remote Sens."},{"key":"ref_52","doi-asserted-by":"crossref","unstructured":"Bai, Y., Peng, D., Yang, X., Chen, L., and Yang, W. (2014, January 19\u201323). Supervised feature selection for polarimetric SAR classification. Proceedings of the 2014 12th International Conference on Signal Processing (ICSP), Hangzhou, China.","DOI":"10.1109\/ICOSP.2014.7015156"},{"key":"ref_53","doi-asserted-by":"crossref","first-page":"44","DOI":"10.1109\/MGRS.2018.2853555","article-title":"A Review of the Autoencoder and Its Variants: A Comparative Perspective from Target Recognition in Synthetic-Aperture Radar Images","volume":"6","author":"Dong","year":"2018","journal-title":"IEEE Geosci. Remote Sens. Mag."},{"key":"ref_54","unstructured":"Ng, A. (2011). Sparse Autoencoder, Stanford. CS294A Lecture notes."},{"key":"ref_55","doi-asserted-by":"crossref","unstructured":"Szegedy, C., Liu, W., Jia, Y., Sermanet, P., Reed, S., Anguelov, D., Erhan, D., Vanhoucke, V., and Rabinovich, A. (2015, January 8\u201312). Going Deeper with Convolutions. Proceedings of the IEEE Conference on Computer Vision & Pattern Recognition(CVPR), Boston, MA, USA.","DOI":"10.1109\/CVPR.2015.7298594"},{"key":"ref_56","unstructured":"Caves, R. (2009). Final Report: Technical Assistance for the Implementation of the AgriSAR 2009 Campaign, ESA. Tech. Rep. 22689\/09."},{"key":"ref_57","unstructured":"Caves, R., Davidson, G., Padda, J., and Ma, A. (2011). Data Analysis-Crop Classification, ESA. Tech. Rep. 22689\/09\/NL\/FF\/ef."},{"key":"ref_58","unstructured":"Caves, R., Davidson, G., Padda, J., and Ma, A. (2011). Data Analysis-Multi-Temporal Filtering, ESA. Tech. Rep. 22689\/09\/NL\/FF\/ef."},{"key":"ref_59","doi-asserted-by":"crossref","first-page":"660","DOI":"10.3390\/rs9070660","article-title":"PolSAR land cover classification based on roll-invariant and selected hidden polarimetric features in the rotation domain","volume":"9","author":"Tao","year":"2017","journal-title":"Remote Sens."}],"container-title":["Remote Sensing"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/www.mdpi.com\/2072-4292\/12\/2\/321\/pdf","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,10,13]],"date-time":"2025-10-13T13:43:57Z","timestamp":1760363037000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.mdpi.com\/2072-4292\/12\/2\/321"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2020,1,18]]},"references-count":59,"journal-issue":{"issue":"2","published-online":{"date-parts":[[2020,1]]}},"alternative-id":["rs12020321"],"URL":"https:\/\/doi.org\/10.3390\/rs12020321","relation":{},"ISSN":["2072-4292"],"issn-type":[{"value":"2072-4292","type":"electronic"}],"subject":[],"published":{"date-parts":[[2020,1,18]]}}}