{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,10,12]],"date-time":"2025-10-12T04:21:13Z","timestamp":1760242873308,"version":"build-2065373602"},"reference-count":41,"publisher":"MDPI AG","issue":"9","license":[{"start":{"date-parts":[[2016,9,2]],"date-time":"2016-09-02T00:00:00Z","timestamp":1472774400000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"DOI":"10.13039\/501100001809","name":"National Natural Science Foundation of China","doi-asserted-by":"publisher","award":["61301224"],"award-info":[{"award-number":["61301224"]}],"id":[{"id":"10.13039\/501100001809","id-type":"DOI","asserted-by":"publisher"}]},{"name":"National Hightech R&amp;D Program of China (863 Program)","award":["2015AA01A706"],"award-info":[{"award-number":["2015AA01A706"]}]},{"name":"Basic and Advanced Research Project in Chongqing","award":["cstc2016jcyjA0134"],"award-info":[{"award-number":["cstc2016jcyjA0134"]}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Sensors"],"abstract":"<jats:p>Classification of target microwave images is an important application in much areas such as security, surveillance, etc. With respect to the task of microwave image classification, a recognition algorithm based on aspect-aided dynamic non-negative least square (ADNNLS) sparse representation is proposed. Firstly, an aspect sector is determined, the center of which is the estimated aspect angle of the testing sample. The training samples in the aspect sector are divided into active atoms and inactive atoms by smooth self-representative learning. Secondly, for each testing sample, the corresponding active atoms are selected dynamically, thereby establishing dynamic dictionary. Thirdly, the testing sample is represented with      \u2113 1     -regularized non-negative sparse representation under the corresponding dynamic dictionary. Finally, the class label of the testing sample is identified by use of the minimum reconstruction error. Verification of the proposed algorithm was conducted using the Moving and Stationary Target Acquisition and Recognition (MSTAR) database which was acquired by synthetic aperture radar. Experiment results validated that the proposed approach was able to capture the local aspect characteristics of microwave images effectively, thereby improving the classification performance.<\/jats:p>","DOI":"10.3390\/s16091413","type":"journal-article","created":{"date-parts":[[2016,9,2]],"date-time":"2016-09-02T10:01:56Z","timestamp":1472810516000},"page":"1413","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":0,"title":["Aspect-Aided Dynamic Non-Negative Sparse Representation-Based Microwave Image Classification"],"prefix":"10.3390","volume":"16","author":[{"ORCID":"https:\/\/orcid.org\/0000-0003-0170-992X","authenticated-orcid":false,"given":"Xinzheng","family":"Zhang","sequence":"first","affiliation":[{"name":"College of Communication Engineering, Chongqing University, Chongqing 400044, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Qiuyue","family":"Yang","sequence":"additional","affiliation":[{"name":"College of Communication Engineering, Chongqing University, Chongqing 400044, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Miaomiao","family":"Liu","sequence":"additional","affiliation":[{"name":"College of Communication Engineering, Chongqing University, Chongqing 400044, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Yunjian","family":"Jia","sequence":"additional","affiliation":[{"name":"College of Communication Engineering, Chongqing University, Chongqing 400044, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Shujun","family":"Liu","sequence":"additional","affiliation":[{"name":"College of Communication Engineering, Chongqing University, Chongqing 400044, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Guojun","family":"Li","sequence":"additional","affiliation":[{"name":"Department of Communication Commanding, Chongqing Communication Institute, Chongqing 400035, China"}],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"1968","published-online":{"date-parts":[[2016,9,2]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","first-page":"294","DOI":"10.3390\/rs8040294","article-title":"Multiyear Arctic Ice Classification Using ASCAT and SSMIS","volume":"8","author":"Lindell","year":"2016","journal-title":"Remote Sens."},{"key":"ref_2","doi-asserted-by":"crossref","first-page":"4296","DOI":"10.1109\/JSTARS.2014.2321559","article-title":"CLOUDET: A Cloud Detection and Estimation Algorithm for Passive Microwave Imagers and Sounders Aided by Naive Bayes Classifier and Multilayer Perceptron","volume":"8","author":"Islam","year":"2015","journal-title":"IEEE J. Sel. Top. Appl. Earth Observ. Remote Sens."},{"key":"ref_3","first-page":"364","article-title":"Convolutional Neural Network with Data Augmentation for SAR Target Recognition","volume":"13","author":"Jun","year":"2016","journal-title":"IEEE Geosci. Remote Sens. Lett."},{"key":"ref_4","doi-asserted-by":"crossref","first-page":"1106","DOI":"10.1109\/LGRS.2013.2287295","article-title":"Dempster\u2013Shafer Fusion of Multiple Sparse Representation and Statistical Property for SAR Target Configuration Recognition","volume":"11","author":"Liu","year":"2014","journal-title":"IEEE Geosci. Remote Sens. Lett."},{"key":"ref_5","doi-asserted-by":"crossref","first-page":"256","DOI":"10.1049\/iet-rsn.2015.0024","article-title":"Synthetic Aperture Radar Target Configuration Recognition Using Locality-preserving Property and the Gamma Distribution","volume":"10","author":"Liu","year":"2016","journal-title":"IET Radar Sonar Navig."},{"key":"ref_6","first-page":"11","article-title":"Performance of a High-resolution Polarimetric SAR Automatic Target Recognition System","volume":"6","author":"Novak","year":"1993","journal-title":"Lincoln Lab J."},{"key":"ref_7","doi-asserted-by":"crossref","first-page":"122","DOI":"10.1117\/1.JRS.10.016025","article-title":"Three-dimensional Electromagnetic Model-based Scattering Center Matching Method for Synthetic Aperture Radar Automatic Target Recognition by Combining Spatial and Attributed Information","volume":"10","author":"Ma","year":"2016","journal-title":"J. Appl. Remote Sens."},{"key":"ref_8","doi-asserted-by":"crossref","first-page":"222","DOI":"10.1109\/LGRS.2015.2506659","article-title":"SAR Target Configuration Recognition Using Tensor Global and Local Discriminant Embedding","volume":"13","author":"Huang","year":"2016","journal-title":"IEEE Geosci. Remote Sens. Lett."},{"key":"ref_9","doi-asserted-by":"crossref","first-page":"1376","DOI":"10.1049\/iet-rsn.2014.0407","article-title":"Target Recognition in Synthetic Aperture Radar Images via Non-negative Matrix Factorisation","volume":"9","author":"Cui","year":"2015","journal-title":"IET Radar Sonar Navig."},{"key":"ref_10","doi-asserted-by":"crossref","first-page":"210","DOI":"10.1109\/TPAMI.2008.79","article-title":"Robust Face Recognition via Sparse Representation","volume":"31","author":"Wright","year":"2009","journal-title":"IEEE Trans. Patt. Anal. Mach. Intell."},{"key":"ref_11","doi-asserted-by":"crossref","first-page":"237","DOI":"10.1109\/LSP.2015.2509480","article-title":"Supervised Monaural Speech Enhancement Using Complementary Joint Sparse Representations","volume":"23","author":"Luo","year":"2016","journal-title":"IEEE Signal Process. Lett."},{"key":"ref_12","doi-asserted-by":"crossref","first-page":"641","DOI":"10.1109\/LGRS.2016.2532380","article-title":"Hyperspectral Image Classification with Robust Sparse Representation","volume":"13","author":"Li","year":"2016","journal-title":"IEEE Geosci. Remote Sens. Lett."},{"key":"ref_13","doi-asserted-by":"crossref","first-page":"2605","DOI":"10.1109\/TIM.2015.2427893","article-title":"Face Recognition by Exploiting Local Gabor Features with Multitask Adaptive Sparse Representation","volume":"64","author":"Fang","year":"2015","journal-title":"IEEE Trans. Instrum. Measur."},{"key":"ref_14","doi-asserted-by":"crossref","first-page":"1624","DOI":"10.1109\/TSP.2011.2179539","article-title":"Kernel Sparse Representation-Based Classifier","volume":"60","author":"Zhang","year":"2012","journal-title":"IEEE Trans. Signal Process."},{"key":"ref_15","doi-asserted-by":"crossref","first-page":"41","DOI":"10.1016\/j.neucom.2013.02.012","article-title":"Classification Approach Based on Non-Negative Least Squares","volume":"118","author":"Li","year":"2013","journal-title":"Neurocomput"},{"key":"ref_16","doi-asserted-by":"crossref","first-page":"447","DOI":"10.1109\/TCBB.2013.30","article-title":"Nonnegative Least-Squares Methods for the Classification of High-Dimensional Biological Data","volume":"10","author":"Li","year":"2013","journal-title":"IEEE\/ACM Trans. Comput. Biol. Bioinform."},{"key":"ref_17","doi-asserted-by":"crossref","first-page":"2481","DOI":"10.1109\/TAES.2012.6237604","article-title":"Multi-view Automatic Target Recognition Using Joint Sparse Representation","volume":"48","author":"Zhang","year":"2012","journal-title":"IEEE Trans. Aerosp. Electron. Syst."},{"key":"ref_18","doi-asserted-by":"crossref","first-page":"1","DOI":"10.1186\/1687-6180-2014-87","article-title":"SAR Target Recognition Based on Improved Joint Sparse Representation","volume":"2014","author":"Cheng","year":"2014","journal-title":"EURASIP J. Adv. Signal Process."},{"key":"ref_19","doi-asserted-by":"crossref","first-page":"3316","DOI":"10.1109\/JSTARS.2015.2436694","article-title":"SAR Target Recognition via Joint Sparse Representation of Monogenic Signal","volume":"8","author":"Dong","year":"2015","journal-title":"IEEE J. Sel. Top. Appl. Earth Observ. Remote Sens."},{"key":"ref_20","doi-asserted-by":"crossref","first-page":"1308","DOI":"10.1109\/JSTARS.2015.2513481","article-title":"SAR Target Recognition via Sparse Representation of Monogenic Signal on Grassmann Manifolds","volume":"9","author":"Dong","year":"2016","journal-title":"IEEE J. Sel. Top. Appl. Earth Observ. Remote Sens."},{"key":"ref_21","doi-asserted-by":"crossref","first-page":"1562","DOI":"10.1109\/LGRS.2013.2262073","article-title":"Ship Classification in TerraSAR-X Images with Feature Space Based Sparse Representation","volume":"10","author":"Xing","year":"2013","journal-title":"IEEE Geosci. Remote Sens. Lett."},{"key":"ref_22","doi-asserted-by":"crossref","first-page":"97","DOI":"10.1016\/j.neucom.2013.01.033","article-title":"Decision Fusion of Sparse Representation and Support Vector Machine for SAR Image Target Recognition","volume":"113","author":"Liu","year":"2013","journal-title":"Neurocomputing"},{"key":"ref_23","doi-asserted-by":"crossref","first-page":"27","DOI":"10.1016\/j.sigpro.2015.12.018","article-title":"Automatic Target Recognition with Joint Sparse Representation of Heterogeneous Multi-view SAR Images over a Locally Adaptive Dictionary","volume":"126","author":"Cao","year":"2016","journal-title":"Signal Process."},{"key":"ref_24","doi-asserted-by":"crossref","first-page":"33","DOI":"10.1109\/LGRS.2015.2493242","article-title":"SAR Image Classification via Hierarchical Sparse Representation and Multisize Patch Features","volume":"13","author":"Hou","year":"2016","journal-title":"IEEE Trans. Geosci. Remote Sens."},{"key":"ref_25","doi-asserted-by":"crossref","first-page":"7158","DOI":"10.3390\/rs6087158","article-title":"Polarimetric Contextual Classification of PolSAR Images Using Sparse Representation and Superpixels","volume":"6","author":"Feng","year":"2014","journal-title":"Remote Sens."},{"key":"ref_26","doi-asserted-by":"crossref","first-page":"3923","DOI":"10.1109\/JSTARS.2014.2359459","article-title":"Fully Polarimetric SAR Image Classification via Sparse Representation and Polarimetric Features","volume":"8","author":"Zhang","year":"2015","journal-title":"IEEE J. Sel. Top. Appl. Earth Observ. Remote Sens."},{"key":"ref_27","doi-asserted-by":"crossref","first-page":"8469","DOI":"10.3390\/rs70708469","article-title":"Multi-Frequency Polarimetric SAR Classification Based on Riemannian Manifold and Simultaneous Sparse Representation","volume":"7","author":"Yang","year":"2015","journal-title":"Remote Sens."},{"key":"ref_28","first-page":"227","article-title":"PolSAR Image Classification via D-KSVD and NSCT-Domain Features Extraction","volume":"13","author":"Xie","year":"2016","journal-title":"IEEE Signal Process. Lett."},{"key":"ref_29","unstructured":"Principe, J., Zhao, Q., and Xu, D. (November, January 30). A Novel ATR Classifier Exploiting Pose Information. Proceedings of the Image Understanding Workshop, Monterey, CA, USA."},{"key":"ref_30","doi-asserted-by":"crossref","unstructured":"Shafiee, S., Kamangar, F., and Ghandehari, L. (2014, January 6\u20138). Cluster-Based Multi-task Sparse Representation for Efficient Face Recognition. Proceedings of the 2014 IEEE Southwest Symposium on Image Analysis and Interpretation, San Diego, CA, USA.","DOI":"10.1109\/SSIAI.2014.6806045"},{"key":"ref_31","doi-asserted-by":"crossref","unstructured":"Hu, H., Lin, Z., Feng, J., and Zhou, J. (2014, January 23\u201328). Smooth Representation Clustering. Proceedings of the 2014 IEEE Conference on Computer Vision and Pattern Recognition, Columbus, OH, USA.","DOI":"10.1109\/CVPR.2014.484"},{"key":"ref_32","doi-asserted-by":"crossref","first-page":"395","DOI":"10.1007\/s11222-007-9033-z","article-title":"A Tutorial on Spectral Clustering","volume":"17","year":"2007","journal-title":"Stat. Comput."},{"key":"ref_33","doi-asserted-by":"crossref","unstructured":"Yang, M., Zhang, L., Yang, J., and Zhang, D. (2011, January 20\u201325). Robust Sparse Coding for Face Recognition. Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, Colorado Springs, CO, USA.","DOI":"10.1109\/CVPR.2011.5995393"},{"key":"ref_34","doi-asserted-by":"crossref","first-page":"797","DOI":"10.1002\/cpa.20132","article-title":"For Most Large Underdetermined Systems of Linear Equations the Minimal \u2113 1-Norm Solution Is Also the Sparsest Solution","volume":"59","author":"Donoho","year":"2006","journal-title":"Commun. Pure Appl. Math."},{"key":"ref_35","unstructured":"Sharon, Y., Wright, J., and Ma, Y. (2007). Computation and Relaxation of Conditions for Equivalence between \u21131 and l \u21130 Minimization, University of Illinois. CSL Technical Report UILU-ENG-07-2208."},{"key":"ref_36","doi-asserted-by":"crossref","first-page":"2197","DOI":"10.1073\/pnas.0437847100","article-title":"Optimal Sparse Representation in General (Nonorthogonal) Dictionaries via \u21131 Minimization","volume":"100","author":"Donoho","year":"2003","journal-title":"Proc. Nat. Acad. Sci. USA"},{"key":"ref_37","doi-asserted-by":"crossref","first-page":"267","DOI":"10.1111\/j.2517-6161.1996.tb02080.x","article-title":"Regression Shrinkage and Selection via the LASSO","volume":"58","author":"Tibshirani","year":"1996","journal-title":"J. R. Stat. Soc. B"},{"key":"ref_38","doi-asserted-by":"crossref","first-page":"1207","DOI":"10.1002\/cpa.20124","article-title":"Stable Signal Recovery from Incomplete and Inaccurate Measurements","volume":"59","author":"Candes","year":"2006","journal-title":"Commun. Pure Appl. Math."},{"key":"ref_39","doi-asserted-by":"crossref","first-page":"907","DOI":"10.1002\/cpa.20131","article-title":"For Most Large Underdetermined Systems of Linear Equations the Minimal \u2113 1-Norm near Solution Approximates the Sparest Solution","volume":"59","author":"Donoho","year":"2006","journal-title":"Commun. Pure Appl. Math."},{"key":"ref_40","doi-asserted-by":"crossref","unstructured":"Boyd, S., and Vandenberghe, L. (2004). Convex Optimization, Cambridge University Press.","DOI":"10.1017\/CBO9780511804441"},{"key":"ref_41","doi-asserted-by":"crossref","unstructured":"Yang, A.Y., Sastry, S.S., Ganesh, A., and Ma, Y. (2010, January 26\u201329). Fast \u2113 1-minimization Algorithms and an Application in Robust Face Recognition: A review. Proceedings of the 17th IEEE International Conference on Image Processing, Hong Kong, China.","DOI":"10.1109\/ICIP.2010.5651522"}],"container-title":["Sensors"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/www.mdpi.com\/1424-8220\/16\/9\/1413\/pdf","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,10,11]],"date-time":"2025-10-11T19:30:00Z","timestamp":1760211000000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.mdpi.com\/1424-8220\/16\/9\/1413"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2016,9,2]]},"references-count":41,"journal-issue":{"issue":"9","published-online":{"date-parts":[[2016,9]]}},"alternative-id":["s16091413"],"URL":"https:\/\/doi.org\/10.3390\/s16091413","relation":{},"ISSN":["1424-8220"],"issn-type":[{"type":"electronic","value":"1424-8220"}],"subject":[],"published":{"date-parts":[[2016,9,2]]}}}