{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,2,21]],"date-time":"2026-02-21T14:12:01Z","timestamp":1771683121988,"version":"3.50.1"},"reference-count":67,"publisher":"MDPI AG","issue":"17","license":[{"start":{"date-parts":[[2019,8,23]],"date-time":"2019-08-23T00:00:00Z","timestamp":1566518400000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"DOI":"10.13039\/501100013290","name":"National Key Research and Development Program of China Stem Cell and Translational Research","doi-asserted-by":"publisher","award":["2017YFC0803802"],"award-info":[{"award-number":["2017YFC0803802"]}],"id":[{"id":"10.13039\/501100013290","id-type":"DOI","asserted-by":"publisher"}]}],"content-domain":{"domain":["www.mdpi.com"],"crossmark-restriction":true},"short-container-title":["Remote Sensing"],"abstract":"<jats:p>Scene classification of high-resolution remote sensing images (HRRSI) is one of the most important means of land-cover classification. Deep learning techniques, especially the convolutional neural network (CNN) have been widely applied to the scene classification of HRRSI due to the advancement of graphic processing units (GPU). However, they tend to extract features from the whole images rather than discriminative regions. The visual attention mechanism can force the CNN to focus on discriminative regions, but it may suffer from the influence of intra-class diversity and repeated texture. Motivated by these problems, we propose an attention-based deep feature fusion (ADFF) framework that constitutes three parts, namely attention maps generated by Gradient-weighted Class Activation Mapping (Grad-CAM), a multiplicative fusion of deep features and the center-based cross-entropy loss function. First of all, we propose to make attention maps generated by Grad-CAM as an explicit input in order to force the network to concentrate on discriminative regions. Then, deep features derived from original images and attention maps are proposed to be fused by multiplicative fusion in order to consider both improved abilities to distinguish scenes of repeated texture and the salient regions. Finally, the center-based cross-entropy loss function that utilizes both the cross-entropy loss and center loss function is proposed to backpropagate fused features so as to reduce the effect of intra-class diversity on feature representations. The proposed ADFF architecture is tested on three benchmark datasets to show its performance in scene classification. The experiments confirm that the proposed method outperforms most competitive scene classification methods with an average overall accuracy of 94% under different training ratios.<\/jats:p>","DOI":"10.3390\/rs11171996","type":"journal-article","created":{"date-parts":[[2019,8,26]],"date-time":"2019-08-26T04:38:23Z","timestamp":1566794303000},"page":"1996","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":43,"title":["RETRACTED: Attention-Based Deep Feature Fusion for the Scene Classification of High-Resolution Remote Sensing Images"],"prefix":"10.3390","volume":"11","author":[{"ORCID":"https:\/\/orcid.org\/0000-0002-5006-0840","authenticated-orcid":false,"given":"Ruixi","family":"Zhu","sequence":"first","affiliation":[{"name":"School of Geodesy and Geomatics, Wuhan University, 129 Luoyu Road, Wuhan 430079, China"}]},{"given":"Li","family":"Yan","sequence":"additional","affiliation":[{"name":"School of Geodesy and Geomatics, Wuhan University, 129 Luoyu Road, Wuhan 430079, China"}]},{"given":"Nan","family":"Mo","sequence":"additional","affiliation":[{"name":"School of Geodesy and Geomatics, Wuhan University, 129 Luoyu Road, Wuhan 430079, China"}]},{"given":"Yi","family":"Liu","sequence":"additional","affiliation":[{"name":"School of Geodesy and Geomatics, Wuhan University, 129 Luoyu Road, Wuhan 430079, China"}]}],"member":"1968","published-online":{"date-parts":[[2019,8,23]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","first-page":"33","DOI":"10.1109\/TPAMI.2011.94","article-title":"Building development monitoring in multitemporal remotely sensed image pairs with stochastic birth-death dynamics","volume":"34","author":"Benedek","year":"2012","journal-title":"IEEE Trans. Pattern Anal. Mach. Intell."},{"key":"ref_2","doi-asserted-by":"crossref","first-page":"145","DOI":"10.1016\/j.isprsjprs.2016.10.010","article-title":"MRF-based Segmentation and Unsupervised Classification for Building and Road Detection in Peri-urban Areas of High-resolution","volume":"122","author":"Grinias","year":"2016","journal-title":"ISPRS J. Photogramm. Remote Sens."},{"key":"ref_3","doi-asserted-by":"crossref","unstructured":"Yan, L., Zhu, R., Mo, N., and Liu, Y. (2017). Improved class-specific codebook with two-step classification for scene-level classification of high resolution remote sensing images. Remote Sens., 9.","DOI":"10.3390\/rs9030223"},{"key":"ref_4","doi-asserted-by":"crossref","unstructured":"Yu, Y., and Liu, F. (2018). Dense connectivity based two-stream deep feature fusion framework for aerial scene classification. Remote Sens., 10.","DOI":"10.3390\/rs10071158"},{"key":"ref_5","doi-asserted-by":"crossref","first-page":"3235","DOI":"10.1109\/JSTARS.2018.2859836","article-title":"TrAdaBoost based on improved particle swarm optimization for cross-domain scene classification with limited samples","volume":"99","author":"Yan","year":"2018","journal-title":"IEEE J. Sel. Top. Appl. Earth Obs. Remote Sens."},{"key":"ref_6","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_7","doi-asserted-by":"crossref","unstructured":"Qi, K., Guan, Q., and Yang, C. (2018). Concentric Circle Pooling in Deep Convolutional Networks for Remote Sensing Scene Classification. Remote Sens., 10.","DOI":"10.3390\/rs10060934"},{"key":"ref_8","doi-asserted-by":"crossref","first-page":"4178","DOI":"10.1109\/TGRS.2018.2828314","article-title":"Scene capture and selected codebook-based refined fuzzy classification of large high-resolution images","volume":"56","author":"Yan","year":"2018","journal-title":"IEEE Trans. Geosci. Remote Sens."},{"key":"ref_9","doi-asserted-by":"crossref","first-page":"2811","DOI":"10.1109\/TGRS.2017.2783902","article-title":"When deep learning meets metric learning: Remote sensing image scene classification via learning discriminative CNNs","volume":"56","author":"Cheng","year":"2018","journal-title":"IEEE Trans. Geosci. Remote Sens."},{"key":"ref_10","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_11","unstructured":"Castelluccio, M., Poggi, G., Sansone, C., and Verdoliva, L. (2015). Land use classification in remote sensing images by convolutional neural networks. arXiv."},{"key":"ref_12","doi-asserted-by":"crossref","first-page":"439","DOI":"10.1109\/TGRS.2013.2241444","article-title":"Unsupervised feature learning for aerial scene classification","volume":"52","author":"Cheriyadat","year":"2013","journal-title":"IEEE Trans. Geosci. Remote Sens."},{"key":"ref_13","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_14","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_15","doi-asserted-by":"crossref","first-page":"17","DOI":"10.1080\/135062800394667","article-title":"The dynamic representation of scenes","volume":"7","author":"Rensink","year":"2000","journal-title":"Vis. Cogn."},{"key":"ref_16","doi-asserted-by":"crossref","unstructured":"Ma, W., Yang, Q., Wu, Y., Zhao, W., and Zhang, X. (2019). Double-Branch Multi-Attention Mechanism Network for Hyperspectral Image Classification. Remote Sens., 11.","DOI":"10.3390\/rs11111307"},{"key":"ref_17","doi-asserted-by":"crossref","unstructured":"Xu, R., Tao, Y., Lu, Z., and Zhong, Y. (2018). Attention-Mechanism-Containing Neural Networks for High-Resolution Remote Sensing Image Classification. Remote Sens., 10.","DOI":"10.3390\/rs10101602"},{"key":"ref_18","doi-asserted-by":"crossref","unstructured":"Fang, B., Li, Y., Zhang, H., and Chan, J. (2019). Hyperspectral Images Classification Based on Dense Convolutional Networks with Spectral-Wise Attention Mechanism. Remote Sens., 11.","DOI":"10.3390\/rs11020159"},{"key":"ref_19","doi-asserted-by":"crossref","unstructured":"Mei, X., Pan, E., Ma, Y., Dai, X., Huang, J., Fan, F., Du, Q., Zheng, H., and Ma, J. (2019). Spectral-Spatial Attention Networks for Hyperspectral Image Classification. Remote Sens., 11.","DOI":"10.3390\/rs11080963"},{"key":"ref_20","doi-asserted-by":"crossref","first-page":"188","DOI":"10.1016\/j.isprsjprs.2019.01.015","article-title":"Recurrently exploring class-wise attention in a hybrid convolutional and bidirectional LSTM network for multi-label aerial image classification","volume":"149","author":"Hua","year":"2019","journal-title":"ISPRS J. Photogramm. Remote Sens."},{"key":"ref_21","doi-asserted-by":"crossref","first-page":"313","DOI":"10.1016\/j.isprsjprs.2019.03.014","article-title":"Deep built-structure counting in satellite imagery using attention based re-weighting","volume":"151","author":"Shakeel","year":"2019","journal-title":"ISPRS J. Photogramm. Remote Sens."},{"key":"ref_22","doi-asserted-by":"crossref","first-page":"3840","DOI":"10.1109\/TGRS.2018.2888618","article-title":"Cross-Domain Distance Metric Learning Framework with Limited Target Samples for Scene Classification of Aerial Images","volume":"57","author":"Yan","year":"2019","journal-title":"IEEE Trans. Geosci. Remote Sens."},{"key":"ref_23","doi-asserted-by":"crossref","first-page":"962","DOI":"10.1109\/JSTARS.2018.2795753","article-title":"Domain-adapted convolutional networks for satellite image classification: A large-scale interactive learning workflow","volume":"11","author":"Lunga","year":"2018","journal-title":"IEEE J. Sel. Top. Appl. Earth Obs. Remote Sens."},{"key":"ref_24","doi-asserted-by":"crossref","first-page":"2175","DOI":"10.1109\/TGRS.2014.2357078","article-title":"Saliency-guided unsupervised feature learning for scene classification","volume":"53","author":"Zhang","year":"2014","journal-title":"IEEE Trans. Geosci. Remote Sens."},{"key":"ref_25","doi-asserted-by":"crossref","unstructured":"Cheng, G., Han, J., Guo, L., and Liu, T. (2015, January 7\u201312). Learning coarse-to-fine sparselets for efficient object detection and scene classification. Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, Boston, MA, USA.","DOI":"10.1109\/CVPR.2015.7298721"},{"key":"ref_26","doi-asserted-by":"crossref","first-page":"4238","DOI":"10.1109\/TGRS.2015.2393857","article-title":"Effective and efficient midlevel visual elements-oriented land-use classification using VHR remote sensing images","volume":"53","author":"Cheng","year":"2015","journal-title":"IEEE Trans. Geosci. Remote Sens."},{"key":"ref_27","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_28","doi-asserted-by":"crossref","first-page":"745","DOI":"10.1007\/s11760-015-0804-2","article-title":"Land-use scene classification using multi-scale completed local binary patterns","volume":"10","author":"Chen","year":"2016","journal-title":"Signal Image Video Process."},{"key":"ref_29","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":"2016","journal-title":"Inf. Sci."},{"key":"ref_30","doi-asserted-by":"crossref","unstructured":"Liu, B.D., Xie, W.Y., Meng, J., Li, Y., and Wang, Y. (2018). Hybrid collaborative representation for remote-sensing image scene classification. Remote Sens., 10.","DOI":"10.3390\/rs10121934"},{"key":"ref_31","doi-asserted-by":"crossref","unstructured":"Liu, B.D., Meng, J., Xie, W.Y., Sao, S., Li, Y., and Wang, Y. (2019). Weighted Spatial Pyramid Matching Collaborative Representation for Remote-Sensing-Image Scene Classification. Remote Sens., 11.","DOI":"10.3390\/rs11050518"},{"key":"ref_32","doi-asserted-by":"crossref","first-page":"2250","DOI":"10.1109\/TGRS.2016.2640186","article-title":"Unsupervised feature learning for land-use scene recognition","volume":"55","author":"Fan","year":"2017","journal-title":"IEEE Trans. Geosci. Remote Sens."},{"key":"ref_33","doi-asserted-by":"crossref","first-page":"1131","DOI":"10.1109\/JSTARS.2017.2755639","article-title":"GPU parallel implementation of spatially adaptive hyperspectral image classification","volume":"11","author":"Wu","year":"2017","journal-title":"IEEE J. Sel. Top. Appl. Earth Obs. Remote Sens."},{"key":"ref_34","doi-asserted-by":"crossref","first-page":"2270","DOI":"10.1109\/JSTARS.2016.2542193","article-title":"Parallel and distributed dimensionality reduction of hyperspectral data on cloud computing architectures","volume":"9","author":"Wu","year":"2016","journal-title":"IEEE J. Sel. Top. Appl. Earth Obs. Remote Sens."},{"key":"ref_35","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_36","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_37","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_38","doi-asserted-by":"crossref","unstructured":"Chen, G., Zhang, X., Tan, X., Chen, Y., Dai, F., Zhu, K., Gong, Y., and Wang, Q. (2018). Training small networks for scene classification of remote sensing images via knowledge distillation. Remote Sens., 10.","DOI":"10.3390\/rs10050719"},{"key":"ref_39","doi-asserted-by":"crossref","unstructured":"Huang, H., and Xu, K. (2019). Combing Triple-Part Features of Convolutional Neural Networks for Scene Classification in Remote Sensing. Remote Sens., 11.","DOI":"10.3390\/rs11141687"},{"key":"ref_40","doi-asserted-by":"crossref","unstructured":"Zhang, H., Zhang, J., and Xu, F. (2015, January 27\u201330). Land use and land cover classification base on image saliency map cooperated coding. Proceedings of the 2015 IEEE International Conference on Image Processing (ICIP), Quebec City, QC, Canada.","DOI":"10.1109\/ICIP.2015.7351276"},{"key":"ref_41","doi-asserted-by":"crossref","unstructured":"Zhou, B., Khosla, A., Lapedriza, A., Oliva, A., and Tarralba, A. (2016, January 27\u201330). Learning deep features for discriminative localization. Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, Las Vegas, NV, USA.","DOI":"10.1109\/CVPR.2016.319"},{"key":"ref_42","unstructured":"Selvaraju, R.R., Cogswell, M., Das, A., Vedantam, R., Parikh, D., and Batra, D. Grad-cam: Visual explanations from deep networks via gradient-based localization. Proceedings of the IEEE International Conference on Computer Vision."},{"key":"ref_43","doi-asserted-by":"crossref","unstructured":"Chattopadhay, A., Sarkar, A., Howlader, P., and Balasubramanian, V. (2018, January 12\u201315). Grad-cam++: Generalized gradient-based visual explanations for deep convolutional networks. Proceedings of the IEEE Winter Conference on Applications of Computer Vision (WACV), Lake Tahoe, NV, USA.","DOI":"10.1109\/WACV.2018.00097"},{"key":"ref_44","doi-asserted-by":"crossref","unstructured":"Feichtenhofer, C., Pinz, A., and Zisserman, A. (2016, January 27\u201330). Convolutional two-stream network fusion for video action recognition. Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, Las Vegas, NV, USA.","DOI":"10.1109\/CVPR.2016.213"},{"key":"ref_45","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_46","doi-asserted-by":"crossref","first-page":"73","DOI":"10.1016\/j.isprsjprs.2016.03.004","article-title":"A spectral\u2013structural bag-of-features scene classifier for very high spatial resolution remote sensing imagery","volume":"116","author":"Zhao","year":"2016","journal-title":"ISPRS J. Photogramm. Remote Sens."},{"key":"ref_47","doi-asserted-by":"crossref","unstructured":"Chowdhury, A.R., Lin, T.Y., Maji, S., and Learned-Miller, E. (2016, January 7\u201310). One-to-many face recognition with bilinear cnns. Proceedings of the IEEE Winter Conference on Applications of Computer Vision (WACV), Lake Placid, NY, USA.","DOI":"10.1109\/WACV.2016.7477593"},{"key":"ref_48","doi-asserted-by":"crossref","first-page":"3137","DOI":"10.1109\/TMM.2018.2823900","article-title":"Modeling multimodal clues in a hybrid deep learning framework for video classification","volume":"20","author":"Jiang","year":"2018","journal-title":"IEEE Trans. Multimed."},{"key":"ref_49","doi-asserted-by":"crossref","unstructured":"Bodla, N., Zheng, J., Xu, H., Chen, J., Castillo, C., and Chellappa, R. (2017, January 24\u201331). Deep heterogeneous feature fusion for template-based face recognition. Proceedings of the IEEE Winter Conference on Applications of Computer Vision (WACV), Santa Rosa, CA, USA.","DOI":"10.1109\/WACV.2017.71"},{"key":"ref_50","unstructured":"He, K., Zhang, X., Ren, S., and Sun, J. (July, January 26). Deep residual learning for image recognition. Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, Las Vegas, NV, USA."},{"key":"ref_51","doi-asserted-by":"crossref","unstructured":"Ba, R., Chen, C., Yuan, J., Song, W., and Lo, S. (2019). SmokeNet: Satellite Smoke Scene Detection Using Convolutional Neural Network with Spatial and Channel-Wise Attention. Remote Sens., 11.","DOI":"10.3390\/rs11141702"},{"key":"ref_52","doi-asserted-by":"crossref","unstructured":"Gong, Z., Zhong, P., Hu, W., and Hua, Y. (2019). Joint learning of the center points and deep metrics for land-use classification in remote sensing. Remote Sens., 11.","DOI":"10.3390\/rs11010076"},{"key":"ref_53","doi-asserted-by":"crossref","unstructured":"Wen, Y., Zhang, K., Li, Z., and Qiao, Y. (2016). A discriminative feature learning approach for deep face recognition. European Conference on Computer Vision, Springer.","DOI":"10.1007\/978-3-319-46478-7_31"},{"key":"ref_54","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_55","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_56","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_57","doi-asserted-by":"crossref","unstructured":"Liu, N., Lu, X., Wan, L., Huo, H., and Fang, T. (2018). Improving the separability of deep features with discriminative convolution filters for RSI classification. ISPRS Int. J. Geo Inf., 7.","DOI":"10.3390\/ijgi7030095"},{"key":"ref_58","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_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","unstructured":"Szegedy, C., Liu, W., Jia, Y., Sermanet, P., Reed, S., Anguelov, D., Erhan, D., Vanhoucke, V., and Rabinovich, A. (2015, January 7\u201312). Going deeper with convolutions. Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, Boston, MA, USA.","DOI":"10.1109\/CVPR.2015.7298594"},{"key":"ref_61","unstructured":"Simonyan, K., and Zisserman, A. (2014). Very deep convolutional networks for large-scale image recognition. arXiv."},{"key":"ref_62","doi-asserted-by":"crossref","unstructured":"Al Rahhal, M., Bazi, Y., Abdullah, T., Mekhalfi, M., AlHichri, H., and Zuair, M. (2018). Learning a Multi-Branch Neural Network from Multiple Sources for Knowledge Adaptation in Remote Sensing Imagery. Remote Sens., 10.","DOI":"10.3390\/rs10121890"},{"key":"ref_63","doi-asserted-by":"crossref","unstructured":"Hoffer, E., and Ailon, N. (2015). Deep metric learning using triplet network. International Workshop on Similarity-Based Pattern Recognition, Springer.","DOI":"10.1007\/978-3-319-24261-3_7"},{"key":"ref_64","doi-asserted-by":"crossref","unstructured":"Minetto, R., Segundo, M.P., and Sarkar, S. (2019). Hydra: An ensemble of convolutional neural networks for geospatial land classification. IEEE Trans. Geosci. Remote Sens.","DOI":"10.1109\/TGRS.2019.2906883"},{"key":"ref_65","doi-asserted-by":"crossref","unstructured":"Huang, G., Liu, Z., van der Maaten, L., and Weinberger, K.Q. (2017, January 21\u201326). Densely connected convolutional networks. Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (CVPR), Honolulu, HI, USA.","DOI":"10.1109\/CVPR.2017.243"},{"key":"ref_66","doi-asserted-by":"crossref","first-page":"1735","DOI":"10.1109\/LGRS.2017.2731997","article-title":"Remote sensing image scene classification using bag of convolutional features","volume":"14","author":"Cheng","year":"2017","journal-title":"IEEE Geosci. Remote Sens. Lett."},{"key":"ref_67","doi-asserted-by":"crossref","unstructured":"Yan, L., Zhu, R., Liu, Y., and Mo, N. (2018). Color-Boosted Saliency-Guided Rotation Invariant Bag of Visual Words Representation with Parameter Transfer for Cross-Domain Scene-Level Classification. Remote Sens., 10.","DOI":"10.3390\/rs10040610"}],"updated-by":[{"DOI":"10.3390\/rs12040742","type":"retraction","label":"Retraction","source":"retraction-watch","updated":{"date-parts":[[2020,2,24]],"date-time":"2020-02-24T00:00:00Z","timestamp":1582502400000},"record-id":"44039"},{"DOI":"10.3390\/rs12040742","type":"retraction","label":"Retraction","source":"publisher","updated":{"date-parts":[[2019,8,23]],"date-time":"2019-08-23T00:00:00Z","timestamp":1566518400000}}],"container-title":["Remote Sensing"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/www.mdpi.com\/2072-4292\/11\/17\/1996\/pdf","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,8,3]],"date-time":"2025-08-03T22:17:34Z","timestamp":1754259454000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.mdpi.com\/2072-4292\/11\/17\/1996"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2019,8,23]]},"references-count":67,"journal-issue":{"issue":"17","published-online":{"date-parts":[[2019,9]]}},"alternative-id":["rs11171996"],"URL":"https:\/\/doi.org\/10.3390\/rs11171996","relation":{"retraction":[{"id-type":"doi","id":"10.3390\/rs12040742","asserted-by":"object"}]},"ISSN":["2072-4292"],"issn-type":[{"value":"2072-4292","type":"electronic"}],"subject":[],"published":{"date-parts":[[2019,8,23]]}}}