{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,11,27]],"date-time":"2025-11-27T20:56:08Z","timestamp":1764276968652,"version":"build-2065373602"},"reference-count":79,"publisher":"MDPI AG","issue":"14","license":[{"start":{"date-parts":[[2019,7,17]],"date-time":"2019-07-17T00:00:00Z","timestamp":1563321600000},"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":["41371338"],"award-info":[{"award-number":["41371338"]}],"id":[{"id":"10.13039\/501100001809","id-type":"DOI","asserted-by":"publisher"}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Remote Sensing"],"abstract":"<jats:p>High spatial resolution remote sensing (HSRRS) images contain complex geometrical structures and spatial patterns, and thus HSRRS scene classification has become a significant challenge in the remote sensing community. In recent years, convolutional neural network (CNN)-based methods have attracted tremendous attention and obtained excellent performance in scene classification. However, traditional CNN-based methods focus on processing original red-green-blue (RGB) image-based features or CNN-based single-layer features to achieve the scene representation, and ignore that texture images or each layer of CNNs contain discriminating information. To address the above-mentioned drawbacks, a CaffeNet-based method termed CTFCNN is proposed to effectively explore the discriminating ability of a pre-trained CNN in this paper. At first, the pretrained CNN model is employed as a feature extractor to obtain convolutional features from multiple layers, fully connected (FC) features, and local binary pattern (LBP)-based FC features. Then, a new improved bag-of-view-word (iBoVW) coding method is developed to represent the discriminating information from each convolutional layer. Finally, weighted concatenation is employed to combine different features for classification. Experiments on the UC-Merced dataset and Aerial Image Dataset (AID) demonstrate that the proposed CTFCNN method performs significantly better than some state-of-the-art methods, and the overall accuracy can reach 98.44% and 94.91%, respectively. This indicates that the proposed framework can provide a discriminating description for HSRRS images.<\/jats:p>","DOI":"10.3390\/rs11141687","type":"journal-article","created":{"date-parts":[[2019,7,17]],"date-time":"2019-07-17T02:44:03Z","timestamp":1563331443000},"page":"1687","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":61,"title":["Combing Triple-Part Features of Convolutional Neural Networks for Scene Classification in Remote Sensing"],"prefix":"10.3390","volume":"11","author":[{"ORCID":"https:\/\/orcid.org\/0000-0002-7377-3077","authenticated-orcid":false,"given":"Hong","family":"Huang","sequence":"first","affiliation":[{"name":"Key Laboratory of Optoelectronic Technology and Systems of the Education Ministry of China, Chongqing University, Chongqing 400044, China"}]},{"given":"Kejie","family":"Xu","sequence":"additional","affiliation":[{"name":"Key Laboratory of Optoelectronic Technology and Systems of the Education Ministry of China, Chongqing University, Chongqing 400044, China"}]}],"member":"1968","published-online":{"date-parts":[[2019,7,17]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","first-page":"1865","DOI":"10.1109\/JPROC.2017.2675998","article-title":"Remote sensing image scene classification: Benchmark and state of the art","volume":"105","author":"Cheng","year":"2017","journal-title":"Proc. IEEE"},{"key":"ref_2","doi-asserted-by":"crossref","first-page":"197","DOI":"10.1016\/j.isprsjprs.2018.01.004","article-title":"PatternNet: A benchmark dataset for performance evaluation of remote sensing image retrieval","volume":"145","author":"Zhou","year":"2018","journal-title":"ISPRS J. Photogramm. Remote Sens."},{"key":"ref_3","doi-asserted-by":"crossref","first-page":"1793","DOI":"10.1109\/TGRS.2015.2488681","article-title":"Scene Classification via a Gradient Boosting Random Convolutional Network Framework","volume":"54","author":"Zhang","year":"2016","journal-title":"IEEE Trans. Geosci. Remote Sens."},{"key":"ref_4","doi-asserted-by":"crossref","first-page":"1155","DOI":"10.1109\/TGRS.2018.2864987","article-title":"Scene Classification with Recurrent Attention of VHR Remote Sensing Images","volume":"57","author":"Wang","year":"2019","journal-title":"IEEE Trans. Geosci. Remote Sens."},{"key":"ref_5","doi-asserted-by":"crossref","unstructured":"Pham, M.T., Mercier, G., Regniers, O., and Michel, J. (2016). Texture Retrieval from VHR Optical Remote Sensed Images Using the Local Extrema Descriptor with Application to Vineyard Parcel Detection. Remote Sens., 8.","DOI":"10.3390\/rs8050368"},{"key":"ref_6","doi-asserted-by":"crossref","first-page":"1343","DOI":"10.1080\/01431161.2017.1399472","article-title":"Visual descriptors for content-based retrieval of remote-sensing images","volume":"39","author":"Napoletano","year":"2018","journal-title":"Int. J. Remote Sens."},{"key":"ref_7","doi-asserted-by":"crossref","first-page":"818","DOI":"10.1109\/TGRS.2012.2205158","article-title":"Geographic image retrieval using local invariant features","volume":"51","author":"Yang","year":"2013","journal-title":"IEEE Trans. Geosci. Remote Sens."},{"key":"ref_8","doi-asserted-by":"crossref","first-page":"474","DOI":"10.1109\/LGRS.2018.2795531","article-title":"Fully Convolutional Networks for Semantic Segmentation of Very High Resolution Remotely Sensed Images Combined With DSM","volume":"15","author":"Sun","year":"2018","journal-title":"IEEE Geosci. Remote Sens. Lett."},{"key":"ref_9","doi-asserted-by":"crossref","unstructured":"Hu, T.Y., Yang, J., Li, X.C., and Gong, P. (2016). Mapping Urban Land Use by Using Landsat Images and Open Social Data. Remote Sens., 8.","DOI":"10.3390\/rs8020151"},{"key":"ref_10","doi-asserted-by":"crossref","first-page":"73","DOI":"10.1016\/j.rse.2018.04.050","article-title":"Urban land-use mapping using a deep convolutional neural network with high spatial resolution multispectral remote sensing imagery","volume":"214","author":"Huang","year":"2018","journal-title":"Remote Sens. Environ."},{"key":"ref_11","doi-asserted-by":"crossref","first-page":"281","DOI":"10.1016\/j.isprsjprs.2018.02.014","article-title":"Multi-class geospatial object detection based on a position-sensitive balancing framework for high spatial resolution remote sensing imagery","volume":"138","author":"Zhong","year":"2018","journal-title":"ISPRS J. Photogramm. Remote Sens."},{"key":"ref_12","doi-asserted-by":"crossref","first-page":"3","DOI":"10.1016\/j.isprsjprs.2018.04.003","article-title":"Multi-scale object detection in remote sensing imagery with convolutional neural networks","volume":"145","author":"Deng","year":"2018","journal-title":"ISPRS J. Photogramm. Remote Sens."},{"key":"ref_13","doi-asserted-by":"crossref","first-page":"119","DOI":"10.1016\/j.isprsjprs.2014.10.002","article-title":"Multi-class geospatial object detection and geographic image classification based on collection of part detectors","volume":"98","author":"Cheng","year":"2014","journal-title":"ISPRS J. Photogramm. Remote Sens."},{"key":"ref_14","doi-asserted-by":"crossref","unstructured":"Manfreda, S., McCabe, M.F., Miller, P.E., Lucas, R., Pajuelo Madrigal, V., Mallinis, G., Ben Dor, E., Helman, D., Estes, L., and Ciraolo, G. (2018). On the use of unmanned aerial systems for environmental monitoring. Remote Sens., 10.","DOI":"10.20944\/preprints201803.0097.v1"},{"key":"ref_15","doi-asserted-by":"crossref","first-page":"36","DOI":"10.1016\/j.neucom.2019.05.024","article-title":"Graph convolutional network for multi-label VHR remote sensing scene recognition","volume":"357","author":"Khan","year":"2019","journal-title":"Neurocomputing"},{"key":"ref_16","doi-asserted-by":"crossref","first-page":"6899","DOI":"10.1109\/TGRS.2018.2845668","article-title":"Remote Sensing Scene Classification Using Multilayer Stacked Covariance Pooling","volume":"51","author":"He","year":"2018","journal-title":"IEEE Trans. Geosci. Remote Sens."},{"key":"ref_17","doi-asserted-by":"crossref","first-page":"11215","DOI":"10.1109\/ACCESS.2018.2798799","article-title":"Exploiting Convolutional Neural Networks with Deeply Local Description for Remote Sensing Image Classification","volume":"6","author":"Liu","year":"2018","journal-title":"IEEE Access"},{"key":"ref_18","doi-asserted-by":"crossref","unstructured":"Jin, P., Xia, G.S., Hu, F., Lu, Q.K., and Zhang, L.P. (2018, January 22\u201327). AID++: An Updated Version of AID on Scene Classification. Proceedings of the 2018 IEEE International Geoscience and Remote Sensing Symposium (IGARSS), Valencia, Spain.","DOI":"10.1109\/IGARSS.2018.8518882"},{"key":"ref_19","doi-asserted-by":"crossref","unstructured":"Hu, F., Xia, G.S., Yang, W., and Zhang, L.P. (2018, January 22\u201327). Recent advances and opportunities in scene classification of aerial images with deep models. Proceedings of the 2018 IEEE International Geoscience and Remote Sensing Symposium (IGARSS), Valencia, Spain.","DOI":"10.1109\/IGARSS.2018.8518336"},{"key":"ref_20","doi-asserted-by":"crossref","first-page":"1349","DOI":"10.1109\/TGRS.2015.2478379","article-title":"Unsupervised Deep Feature Extraction for Remote Sensing Image Classification","volume":"54","author":"Romero","year":"2016","journal-title":"IEEE Trans. Geosci. Remote Sens."},{"key":"ref_21","doi-asserted-by":"crossref","unstructured":"Yu, Y.L., and Liu, F.X. (2018). Dense connectivity based two-stream deep feature fusion framework for aerial scene classification. Remote Sens., 10.","DOI":"10.3390\/rs10071158"},{"key":"ref_22","doi-asserted-by":"crossref","first-page":"3965","DOI":"10.1109\/TGRS.2017.2685945","article-title":"AID: A benchmark data set for performance evaluation of aerial scene classification","volume":"55","author":"Xia","year":"2017","journal-title":"IEEE Trans. Geosci. Remote Sens."},{"key":"ref_23","doi-asserted-by":"crossref","first-page":"1899","DOI":"10.1109\/JSTARS.2012.2228254","article-title":"Indexing of remote sensing images with different resolutions by multiple features","volume":"6","author":"Luo","year":"2013","journal-title":"IEEE J. Sel. Top. Appl. Earth Obs. Remote Sens."},{"key":"ref_24","doi-asserted-by":"crossref","unstructured":"Yang, Y., and Newsam, S. (2008, January 12\u201315). Comparing SIFT descriptors and Gabor texture features for classification of remote sensed imagery. Proceedings of the 15th IEEE International Conference on Image Processing (ICIP 2008), San Diego, CA, USA.","DOI":"10.1109\/ICIP.2008.4712139"},{"key":"ref_25","doi-asserted-by":"crossref","unstructured":"Yang, Y., and Newsam, S. (2010, January 2\u20135). Bag-of-visual-words and spatial extensions for land-use classification. Proceedings of the 18th SIGSPATIAL International Conference on Advances in Geographic Information Systems, San Jose, CA, USA.","DOI":"10.1145\/1869790.1869829"},{"key":"ref_26","doi-asserted-by":"crossref","first-page":"747","DOI":"10.1109\/LGRS.2015.2513443","article-title":"Bag-of-visual-words scene classifier with local and global features for high spatial resolution remote sensing imagery","volume":"13","author":"Zhu","year":"2016","journal-title":"IEEE Geosci. Remote Sens. Lett."},{"key":"ref_27","doi-asserted-by":"crossref","first-page":"4620","DOI":"10.1109\/JSTARS.2014.2339842","article-title":"Land-use scene classification using a concentric circle-structured multiscale bag-of-visual-words model","volume":"7","author":"Zhao","year":"2014","journal-title":"IEEE J. Sel. Top. Appl. Earth Obs. Remote Sens."},{"key":"ref_28","doi-asserted-by":"crossref","unstructured":"Wu, H., Liu, B.Z., Su, W.H., Zhang, W., and Sun, J.G. (2016). Hierarchical coding vectors for scene level land-use classification. Remote Sens., 8.","DOI":"10.3390\/rs8050436"},{"key":"ref_29","doi-asserted-by":"crossref","first-page":"2403","DOI":"10.1109\/LGRS.2015.2478966","article-title":"Land-Use Scene Classification in High-Resolution Remote Sensing Images Using Improved Correlatons","volume":"12","author":"Qi","year":"2015","journal-title":"Remote Sens. Lett."},{"key":"ref_30","doi-asserted-by":"crossref","unstructured":"Perronnin, F., S\u00e1nchez, J., and Mensink, T. (2010, January 5\u201311). Improving the Fisher Kernel for Large-Scale Image Classification. Proceedings of the European Conference on Computer Vision, Crete, Greece.","DOI":"10.1007\/978-3-642-15561-1_11"},{"key":"ref_31","doi-asserted-by":"crossref","first-page":"4104","DOI":"10.1109\/JSTARS.2017.2705419","article-title":"Aggregating rich hierarchical features for scene classification in remote sensing imagery","volume":"10","author":"Wang","year":"2017","journal-title":"IEEE J. Sel. Top. Appl. Earth Obs. Remote Sens."},{"key":"ref_32","doi-asserted-by":"crossref","first-page":"5148","DOI":"10.1109\/TGRS.2017.2702596","article-title":"Remote Sensing Scene Classification by Unsupervised Representation Learning","volume":"55","author":"Lu","year":"2017","journal-title":"IEEE Trans. Geosci. Remote Sens."},{"key":"ref_33","doi-asserted-by":"crossref","first-page":"209","DOI":"10.1016\/j.ins.2016.02.021","article-title":"Scene classification using local and global features with collaborative representation fusion","volume":"348","author":"Zou","year":"2018","journal-title":"Inf. Sci."},{"key":"ref_34","doi-asserted-by":"crossref","first-page":"4982","DOI":"10.1109\/JSTARS.2018.2881342","article-title":"Remote Sensing Image Fusion Using Hierarchical Multimodal Probabilistic Latent Semantic Analysis","volume":"11","author":"Haut","year":"2018","journal-title":"IEEE J. Sel. Top. Appl. Earth Obs. Remote Sens."},{"key":"ref_35","doi-asserted-by":"crossref","first-page":"6207","DOI":"10.1109\/TGRS.2015.2435801","article-title":"Scene Classification Based on the Multifeature Fusion Probabilistic Topic Model for High Spatial Resolution Remote Sensing Imagery","volume":"53","author":"Zhong","year":"2015","journal-title":"IEEE Trans. Geosci. Remote Sens."},{"key":"ref_36","doi-asserted-by":"crossref","first-page":"042609","DOI":"10.1117\/1.JRS.11.042609","article-title":"Comprehensive survey of deep learning in remote sensing: Theories, tools, and challenges for the community","volume":"11","author":"Ball","year":"2017","journal-title":"J. Appl. Remote Sens."},{"key":"ref_37","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_38","doi-asserted-by":"crossref","first-page":"749","DOI":"10.1080\/15481603.2018.1564499","article-title":"Semantic segmentation of high spatial resolution images with deep neural networks","volume":"56","author":"Yang","year":"2019","journal-title":"GISci. Remote Sens."},{"key":"ref_39","doi-asserted-by":"crossref","first-page":"3","DOI":"10.1109\/TGRS.2018.2849692","article-title":"GETNET: A General End-to-End 2-D CNN Framework for Hyperspectral Image Change Detection","volume":"57","author":"Wang","year":"2019","journal-title":"IEEE Trans. Geosci. Remote Sens."},{"key":"ref_40","doi-asserted-by":"crossref","first-page":"1205","DOI":"10.1109\/TGRS.2018.2865197","article-title":"Hyperspectral Image Denoising Employing a Spatial-Spectral Deep Residual Convolutional Neural Network","volume":"57","author":"Yuan","year":"2019","journal-title":"IEEE Trans. Geosci. Remote Sens."},{"key":"ref_41","doi-asserted-by":"crossref","unstructured":"Jian, L., Gao, F.H., Ren, P., Song, Y.Q., and Luo, S.H. (2018). A Noise-Resilient Online Learning Algorithm for Scene Classification. Remote Sens., 10.","DOI":"10.3390\/rs10111836"},{"key":"ref_42","doi-asserted-by":"crossref","first-page":"1451","DOI":"10.1109\/LGRS.2018.2839092","article-title":"Enhanced Fusion of Deep Neural Networks for Classification of Benchmark High-Resolution Image Data Sets","volume":"15","author":"Scott","year":"2018","journal-title":"IEEE Geosci. Remote Sens. Lett."},{"key":"ref_43","doi-asserted-by":"crossref","unstructured":"Zhang, W., Tang, P., and Zhao, L.J. (2019). Remote Sensing Image Scene Classification Using CNN-CapsNet. Remote Sens., 11.","DOI":"10.3390\/rs11050494"},{"key":"ref_44","doi-asserted-by":"crossref","unstructured":"Chen, J.B., Wang, C.Y., Ma, Z., Chen, J.S., He, D.X., and Ackland, S. (2018). Remote Sensing Scene Classification Based on Convolutional Neural Networks Pre-Trained Using Attention-Guided Sparse Filters. Remote Sens., 10.","DOI":"10.3390\/rs10020290"},{"key":"ref_45","doi-asserted-by":"crossref","first-page":"2494","DOI":"10.1109\/TGRS.2018.2873966","article-title":"Scene Classification Using Hierarchical Wasserstein CNN","volume":"57","author":"Liu","year":"2019","journal-title":"IEEE Trans. Geosci. Remote Sens."},{"key":"ref_46","doi-asserted-by":"crossref","first-page":"4441","DOI":"10.1109\/TGRS.2017.2692281","article-title":"Domain Adaptation Network for Cross-Scene Classification","volume":"55","author":"Othman","year":"2017","journal-title":"IEEE Trans. Geosci. Remote Sens."},{"key":"ref_47","doi-asserted-by":"crossref","first-page":"220","DOI":"10.1109\/JSTARS.2017.2761800","article-title":"Scene Classification via Triplet Networks","volume":"11","author":"Liu","year":"2018","journal-title":"IEEE J. Sel. Top. Appl. Earth Obs. Remote Sens."},{"key":"ref_48","doi-asserted-by":"crossref","first-page":"2321","DOI":"10.1109\/LGRS.2015.2475299","article-title":"Deep Learning Based Feature Selection for Remote Sensing Scene Classification","volume":"12","author":"Zou","year":"2015","journal-title":"IEEE Geosci. Remote Sens. Lett."},{"key":"ref_49","doi-asserted-by":"crossref","unstructured":"Penatti, O.A., Nogueira, K., and dos Santos, J.A. (2015, January 7). Do deep features generalize from everyday objects to remote sensing and aerial scenes domains?. Proceedings of the 2015 IEEE Conference on Computer Vision and Pattern Recognition Workshops (CVPRW), Boston, MA, USA.","DOI":"10.1109\/CVPRW.2015.7301382"},{"key":"ref_50","doi-asserted-by":"crossref","first-page":"14680","DOI":"10.3390\/rs71114680","article-title":"Transferring Deep Convolutional Neural Networks for the Scene Classification of High-Resolution Remote Sensing Imagery","volume":"7","author":"Hu","year":"2015","journal-title":"Remote Sens."},{"key":"ref_51","doi-asserted-by":"crossref","unstructured":"Liu, B.D., Jie, M., Xie, W.Y., Shao, S., Li, Y., and Wang, Y.J. (2019). Weighted Spatial Pyramid Matching Collaborative Representation for Remote-Sensing-Image Scene Classification. Remote Sens., 11.","DOI":"10.3390\/rs11050518"},{"key":"ref_52","doi-asserted-by":"crossref","unstructured":"Liu, B.D., Xie, W.Y., Meng, J., Li, Y., and Wang, Y.J. (2018). Hybrid collaborative representation for remote-sensing image scene classification. Remote Sens., 10.","DOI":"10.3390\/rs10121934"},{"key":"ref_53","doi-asserted-by":"crossref","first-page":"32","DOI":"10.1016\/j.patcog.2018.12.019","article-title":"Dictionaries of deep features for land-use scene classification of very high spatial resolution images","volume":"89","author":"Flores","year":"2019","journal-title":"Pattern Recognit."},{"key":"ref_54","doi-asserted-by":"crossref","first-page":"5653","DOI":"10.1109\/TGRS.2017.2711275","article-title":"Integrating Multilayer Features of Convolutional Neural Networks for Remote Sensing Scene Classification","volume":"55","author":"Li","year":"2017","journal-title":"IEEE Trans. Geosci. Remote Sens."},{"key":"ref_55","doi-asserted-by":"crossref","first-page":"4775","DOI":"10.1109\/TGRS.2017.2700322","article-title":"Deep Feature Fusion for VHR remote sensing scene classification","volume":"55","author":"Chaib","year":"2017","journal-title":"IEEE Trans. Geosci. Remote Sens."},{"key":"ref_56","doi-asserted-by":"crossref","first-page":"74","DOI":"10.1016\/j.isprsjprs.2018.01.023","article-title":"Binary patterns encoded convolutional neural networks for texture recognition and remote sensing scene classification","volume":"138","author":"Anwer","year":"2018","journal-title":"ISPRS J. Photogramm. Remote Sens."},{"key":"ref_57","doi-asserted-by":"crossref","first-page":"354","DOI":"10.1016\/j.patcog.2017.10.013","article-title":"Recent Advances in Convolutional Neural Networks","volume":"77","author":"Gu","year":"2018","journal-title":"Pattern Recognit."},{"key":"ref_58","doi-asserted-by":"crossref","first-page":"307","DOI":"10.1016\/j.ins.2018.06.022","article-title":"Reinforcement learning for robust adaptive control of partially unknown nonlinear systems subject to unmatched uncertainties","volume":"463","author":"Yang","year":"2018","journal-title":"Inf. Sci."},{"key":"ref_59","doi-asserted-by":"crossref","unstructured":"Jia, Y., Shelhamer, E., Donahue, J., Karayev, S., Long, J., Girshick, R., Guadarrama, S., and Darrell, T. (2014, January 3\u20137). Caffe: Convolutional architecture for fast feature embedding. Proceedings of the 22nd ACM International Conference on Multimedia, Orlando, FL, USA.","DOI":"10.1145\/2647868.2654889"},{"key":"ref_60","doi-asserted-by":"crossref","first-page":"354","DOI":"10.1109\/LGRS.2016.2643000","article-title":"Learning and Transferring Convolutional Neural Network Knowledge to Ocean Front Recognition","volume":"14","author":"Lima","year":"2017","journal-title":"IEEE Geosci. Remote Sens. Lett."},{"key":"ref_61","doi-asserted-by":"crossref","first-page":"016520","DOI":"10.1117\/1.JRS.13.016520","article-title":"Hierarchical feature coding model for high-resolution satellite scene classification","volume":"13","author":"Zhao","year":"2019","journal-title":"J. Appl. Remote Sens."},{"key":"ref_62","doi-asserted-by":"crossref","first-page":"2037","DOI":"10.1109\/TPAMI.2006.244","article-title":"Face description with local binary patterns: Application to face recognition","volume":"28","author":"Ahonen","year":"2006","journal-title":"IEEE Trans. Pattern Anal. Mach. Intell."},{"key":"ref_63","doi-asserted-by":"crossref","first-page":"3681","DOI":"10.1109\/TGRS.2014.2381602","article-title":"Local Binary Patterns and Extreme Learning Machine for Hyperspectral Imagery Classification","volume":"53","author":"Li","year":"2015","journal-title":"IEEE Trans. Geosci. Remote Sens."},{"key":"ref_64","doi-asserted-by":"crossref","unstructured":"Huang, H., Li, Z.Y., and Pan, Y.S. (2019). Multi-Feature Manifold Discriminant Analysis for Hyperspectral Image Classification. Remote Sens., 11.","DOI":"10.3390\/rs11060651"},{"key":"ref_65","doi-asserted-by":"crossref","first-page":"602","DOI":"10.1109\/LGRS.2017.2664118","article-title":"Ship Detection From Optical Satellite Images Based on Saliency Segmentation and Structure-LBP Feature","volume":"14","author":"Yang","year":"2017","journal-title":"IEEE Geosci. Remote Sens. Lett."},{"key":"ref_66","first-page":"1871","article-title":"LIBLINEAR: A library for large linear classification","volume":"9","author":"Fan","year":"2008","journal-title":"J. Mach. Learn. Res."},{"key":"ref_67","doi-asserted-by":"crossref","unstructured":"Vedaldi, A., and Lenc, K. (2015, January 26\u201330). Matconvnet: Convolutional neural networks for matlab. Proceedings of the 23rd ACM International Conference on Multimedia, Brisbane, Australia.","DOI":"10.1145\/2733373.2807412"},{"key":"ref_68","doi-asserted-by":"crossref","unstructured":"Levi, G., and Hassner, T. (2015, January 9\u201313). Emotion recognition in the wild via convolutional neural networks and mapped binary patterns. Proceedings of the 2015 ACM on International Conference on Multimodal Interaction, Seattle, WA, USA.","DOI":"10.1145\/2818346.2830587"},{"key":"ref_69","doi-asserted-by":"crossref","unstructured":"Negrel, R., Picard, D., and Gosselin, P.H. (2014, January 18\u201320). Evaluation of second-order visual features for land-use classification. Proceedings of the 2014 12th International Workshop on Content-Based Multimedia Indexing (CBMI 2014), Klagenfurt, Austria.","DOI":"10.1109\/CBMI.2014.6849835"},{"key":"ref_70","doi-asserted-by":"crossref","unstructured":"Huang, L.H., Chen, C., Li, W., and Du, Q. (2016). Remote sensing image scene classification using multi-scale completed local binary patterns and fisher vectors. Remote Sens., 8.","DOI":"10.3390\/rs8060483"},{"key":"ref_71","doi-asserted-by":"crossref","first-page":"539","DOI":"10.1016\/j.patcog.2016.07.001","article-title":"Towards better exploiting convolutional neural networks for remote sensing scene classification","volume":"61","author":"Nogueira","year":"2017","journal-title":"Pattern Recognit."},{"key":"ref_72","doi-asserted-by":"crossref","unstructured":"Ji, W.J., Li, X.L., and Lu, X.Q. (2017, January 11\u201314). Bidirectional Adaptive Feature Fusion for Remote Sensing Scene Classification. Proceedings of the Second CCF Chinese Conference (CCCV 2017), Tianjin, China.","DOI":"10.1007\/978-981-10-7302-1_40"},{"key":"ref_73","doi-asserted-by":"crossref","first-page":"704","DOI":"10.1109\/LGRS.2017.2672643","article-title":"Land-use classification via extreme learning classifier based on deep convolutional features","volume":"14","author":"Weng","year":"2017","journal-title":"IEEE Geosci. Remote Sens. Lett."},{"key":"ref_74","doi-asserted-by":"crossref","first-page":"2889","DOI":"10.1109\/JSTARS.2017.2683799","article-title":"Fusing local and global features for high-resolution scene classification","volume":"10","author":"Bian","year":"2017","journal-title":"IEEE J. Sel. Top. Appl. Earth Obs. Remote Sens."},{"key":"ref_75","doi-asserted-by":"crossref","unstructured":"Qi, K.L., Yang, C., Guan, Q.F., Wu, H.Y., and Gong, J.Y. (2017). A Multiscale Deeply Described Correlatons-Based Model for Land-Use Scene Classification. Remote Sens., 9.","DOI":"10.3390\/rs9090917"},{"key":"ref_76","doi-asserted-by":"crossref","first-page":"015010","DOI":"10.1117\/1.JRS.12.015010","article-title":"Multiscale deep features learning for land-use scene recognition","volume":"12","author":"Yuan","year":"2018","journal-title":"J. Appl. Remote Sens."},{"key":"ref_77","doi-asserted-by":"crossref","first-page":"117","DOI":"10.1109\/TGRS.2017.2743243","article-title":"Learning Multiscale Deep Features for High-Resolution Satellite Image Scene Classification","volume":"56","author":"Liu","year":"2018","journal-title":"IEEE Trans. Geosci. Remote Sens."},{"key":"ref_78","doi-asserted-by":"crossref","first-page":"183","DOI":"10.1109\/LGRS.2017.2779469","article-title":"Scene Classification Based on Two-Stage Deep Feature Fusion","volume":"15","author":"Liu","year":"2018","journal-title":"IEEE Geosci. Remote Sens. Lett."},{"key":"ref_79","doi-asserted-by":"crossref","first-page":"287","DOI":"10.1109\/LGRS.2017.2786241","article-title":"Aerial Scene Classification via Multilevel Fusion Based on Deep Convolutional Neural Networks","volume":"15","author":"Yu","year":"2018","journal-title":"IEEE Geosci. Remote Sens. 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