{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,9,28]],"date-time":"2026-09-28T17:19:08Z","timestamp":1790615948024,"version":"4.1.0"},"reference-count":55,"publisher":"MDPI AG","issue":"1","license":[{"start":{"date-parts":[[2019,12,23]],"date-time":"2019-12-23T00:00:00Z","timestamp":1577059200000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"DOI":"10.13039\/501100012166","name":"National Key Research and Development Program of China","doi-asserted-by":"publisher","award":["2018YFB0505000"],"award-info":[{"award-number":["2018YFB0505000"]}],"id":[{"id":"10.13039\/501100012166","id-type":"DOI","asserted-by":"publisher"}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Remote Sensing"],"abstract":"<jats:p>Land-cover information is significant for land-use planning, urban management, and environment monitoring. This paper presented a novel extended topology-preserving segmentation (ETPS)-based multi-scale and multi-feature method using the convolutional neural network (EMMCNN) for high spatial resolution (HSR) image land-cover classification. The EMMCNN first segmented the images into superpixels using the ETPS algorithm with false-color composition and enhancement and built parallel convolutional neural networks (CNNs) with dense connections for superpixel multi-scale deep feature learning. Then, the multi-resolution segmentation (MRS) object hand-delineated features were extracted and mapped to superpixels for complementary multi-segmentation and multi-type representation. Finally, a hybrid network was designed to consist of 1-dimension CNN and multi-layer perception (MLP) with channel-wise stacking and attention-based weighting for adaptive feature fusion and comprehensive classification. Experimental results on four real HSR GaoFen-2 datasets demonstrated the superiority of the proposed EMMCNN over several well-known classification methods in terms of accuracy and consistency, with overall accuracy averagely improved by 1.74% to 19.35% for testing images and 1.06% to 8.78% for validating images. It was found that the solution combining an appropriate number of larger scales and multi-type features is recommended for better performance. Efficient superpixel segmentation, networks with strong learning ability, optimized multi-scale and multi-feature solution, and adaptive attention-based feature fusion were key points for improving HSR image land-cover classification in this study.<\/jats:p>","DOI":"10.3390\/rs12010066","type":"journal-article","created":{"date-parts":[[2019,12,24]],"date-time":"2019-12-24T05:56:15Z","timestamp":1577166975000},"page":"66","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":26,"title":["EMMCNN: An ETPS-Based Multi-Scale and Multi-Feature Method Using CNN for High Spatial Resolution Image Land-Cover Classification"],"prefix":"10.3390","volume":"12","author":[{"given":"Shuyu","family":"Zhang","sequence":"first","affiliation":[{"name":"School of Earth Sciences, Zhejiang University, Hangzhou 310027, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Chuanrong","family":"Li","sequence":"additional","affiliation":[{"name":"Key Laboratory of Quantitative Remote Sensing Information Technology, Academy of Opto-Electronics, Chinese Academy of Sciences, Beijing 100094, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Shi","family":"Qiu","sequence":"additional","affiliation":[{"name":"Key Laboratory of Quantitative Remote Sensing Information Technology, Academy of Opto-Electronics, Chinese Academy of Sciences, Beijing 100094, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Caixia","family":"Gao","sequence":"additional","affiliation":[{"name":"Key Laboratory of Quantitative Remote Sensing Information Technology, Academy of Opto-Electronics, Chinese Academy of Sciences, Beijing 100094, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-1475-8480","authenticated-orcid":false,"given":"Feng","family":"Zhang","sequence":"additional","affiliation":[{"name":"School of Earth Sciences, Zhejiang University, Hangzhou 310027, China"},{"name":"Zhejiang Provincial Key Laboratory of Geographic Information Science, Hangzhou 310028, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Zhenhong","family":"Du","sequence":"additional","affiliation":[{"name":"School of Earth Sciences, Zhejiang University, Hangzhou 310027, China"},{"name":"Zhejiang Provincial Key Laboratory of Geographic Information Science, Hangzhou 310028, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Renyi","family":"Liu","sequence":"additional","affiliation":[{"name":"School of Earth Sciences, Zhejiang University, Hangzhou 310027, China"},{"name":"Zhejiang Provincial Key Laboratory of Geographic Information Science, Hangzhou 310028, China"}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"1968","published-online":{"date-parts":[[2019,12,23]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","first-page":"277","DOI":"10.1016\/j.isprsjprs.2017.06.001","article-title":"A review of supervised object-based land-cover image classification","volume":"130","author":"Ma","year":"2017","journal-title":"ISPRS J. Photogramm. Remote Sens."},{"key":"ref_2","doi-asserted-by":"crossref","first-page":"48","DOI":"10.1016\/j.isprsjprs.2017.08.011","article-title":"Contextually guided very-high-resolution imagery classification with semantic segments","volume":"132","author":"Zhao","year":"2017","journal-title":"ISPRS J. Photogramm. Remote Sens."},{"key":"ref_3","doi-asserted-by":"crossref","first-page":"506","DOI":"10.1080\/01431161.2018.1513666","article-title":"Very high resolution remote sensing image classification with SEEDS-CNN and scale effect analysis for superpixel CNN classification","volume":"40","author":"Lv","year":"2019","journal-title":"Int. J. Remote Sens."},{"key":"ref_4","doi-asserted-by":"crossref","first-page":"685","DOI":"10.1016\/j.patcog.2011.07.017","article-title":"Extraction of complex patterns from multiresolution remote sensing images: A hierarchical top-down methodology","volume":"45","author":"Kurtz","year":"2012","journal-title":"Pattern Recognit."},{"key":"ref_5","doi-asserted-by":"crossref","first-page":"3386","DOI":"10.1109\/JSTARS.2017.2680324","article-title":"Object-based convolutional neural network for high-resolution imagery classification","volume":"10","author":"Zhao","year":"2017","journal-title":"IEEE J. Sel. Top. Appl. Earth Obs. Remote Sens."},{"key":"ref_6","doi-asserted-by":"crossref","first-page":"133","DOI":"10.1016\/j.isprsjprs.2017.07.014","article-title":"A hybrid MLP-CNN classifier for very fine resolution remotely sensed image classification","volume":"140","author":"Zhang","year":"2018","journal-title":"ISPRS J. Photogramm. Remote Sens."},{"key":"ref_7","doi-asserted-by":"crossref","first-page":"2321","DOI":"10.3390\/rs3112321","article-title":"Terrestrial remotely sensed imagery in support of public health: New avenues of research using object-based image analysis","volume":"3","author":"Kelly","year":"2011","journal-title":"Remote Sens."},{"key":"ref_8","doi-asserted-by":"crossref","first-page":"1145","DOI":"10.1016\/j.rse.2010.12.017","article-title":"Per-pixel vs. object-based classification of urban land cover extraction using high spatial resolution imagery","volume":"115","author":"Myint","year":"2011","journal-title":"Remote Sens. Environ."},{"key":"ref_9","doi-asserted-by":"crossref","first-page":"180","DOI":"10.1016\/j.isprsjprs.2013.09.014","article-title":"Geographic object-based image analysis-towards a new paradigm","volume":"87","author":"Blaschke","year":"2014","journal-title":"ISPRS J. Photogramm. Remote Sens."},{"key":"ref_10","first-page":"87","article-title":"A systematic comparison of different object-based classification techniques using high spatial resolution imagery in agricultural environments","volume":"49","author":"Li","year":"2016","journal-title":"Int. J. Appl. Earth Obs. Geoinf."},{"key":"ref_11","doi-asserted-by":"crossref","first-page":"159","DOI":"10.1080\/15481603.2018.1426092","article-title":"Geographic object-based image analysis (GEOBIA): Emerging trends and future opportunities","volume":"55","author":"Chen","year":"2018","journal-title":"GIScience Remote Sens."},{"key":"ref_12","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_13","doi-asserted-by":"crossref","first-page":"57","DOI":"10.1016\/j.rse.2018.06.034","article-title":"An object-based convolutional neural network (OCNN) for urban land use classification","volume":"216","author":"Zhang","year":"2018","journal-title":"Remote Sens. Environ."},{"key":"ref_14","doi-asserted-by":"crossref","first-page":"5861","DOI":"10.1109\/TGRS.2015.2423688","article-title":"Superpixel-based graphical model for remote sensing image mapping","volume":"53","author":"Zhang","year":"2015","journal-title":"IEEE Trans. Geosci. Remote Sens."},{"key":"ref_15","doi-asserted-by":"crossref","first-page":"6663","DOI":"10.1109\/TGRS.2015.2445767","article-title":"Classification of hyperspectral images by exploiting spectral--spatial information of superpixel via multiple kernels","volume":"53","author":"Fang","year":"2015","journal-title":"IEEE Trans. Geosci. Remote Sens."},{"key":"ref_16","doi-asserted-by":"crossref","first-page":"4186","DOI":"10.1109\/TGRS.2015.2392755","article-title":"Spectral-spatial classification of hyperspectral images with a superpixel-based discriminative sparse model","volume":"53","author":"Fang","year":"2015","journal-title":"IEEE Trans. Geosci. Remote Sens."},{"key":"ref_17","doi-asserted-by":"crossref","first-page":"4141","DOI":"10.1109\/TGRS.2017.2689018","article-title":"Superpixel-based multiple local CNN for panchromatic and multispectral image classification","volume":"55","author":"Zhao","year":"2017","journal-title":"IEEE Trans. Geosci. Remote Sens."},{"key":"ref_18","doi-asserted-by":"crossref","first-page":"618","DOI":"10.1109\/LGRS.2018.2879492","article-title":"Water body extraction from very high-resolution remote sensing imagery using deep U-Net and a superpixel-based conditional random field model","volume":"16","author":"Feng","year":"2018","journal-title":"IEEE Geosci. Remote Sens. Lett."},{"key":"ref_19","doi-asserted-by":"crossref","first-page":"1357","DOI":"10.1109\/LGRS.2015.2402391","article-title":"A comparative study of bag-of-words and bag-of-topics models of EO image patches","volume":"12","author":"Bahmanyar","year":"2015","journal-title":"IEEE Geosci. Remote Sens. Lett."},{"key":"ref_20","doi-asserted-by":"crossref","first-page":"73","DOI":"10.1016\/j.isprsjprs.2016.03.004","article-title":"A spectral-structural 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_21","unstructured":"Tong, X.-Y., Xia, G.-S., Lu, Q., Shen, H., Li, S., You, S., and Zhang, L. (2018). Learning transferable deep models for land-use classification with high-resolution remote sensing images. arXiv."},{"key":"ref_22","doi-asserted-by":"crossref","first-page":"3","DOI":"10.1016\/j.isprsjprs.2018.06.005","article-title":"Developing a multi-filter convolutional neural network for semantic segmentation using high-resolution aerial imagery and LiDAR data","volume":"143","author":"Sun","year":"2018","journal-title":"ISPRS J. Photogramm. Remote Sens."},{"key":"ref_23","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":"2018","journal-title":"IEEE Trans. Geosci. Remote Sens."},{"key":"ref_24","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":"2015","journal-title":"IEEE Trans. Geosci. Remote Sens."},{"key":"ref_25","doi-asserted-by":"crossref","first-page":"3322","DOI":"10.1109\/TGRS.2017.2669341","article-title":"Automatic road detection and centerline extraction via cascaded end-to-end convolutional neural network","volume":"55","author":"Cheng","year":"2017","journal-title":"IEEE Trans. Geosci. Remote Sens."},{"key":"ref_26","doi-asserted-by":"crossref","first-page":"22","DOI":"10.1109\/MGRS.2016.2540798","article-title":"Deep learning for remote sensing data: A technical tutorial on the state of the art","volume":"4","author":"Zhang","year":"2016","journal-title":"IEEE Geosci. Remote Sens. Mag."},{"key":"ref_27","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_28","doi-asserted-by":"crossref","first-page":"166","DOI":"10.1016\/j.isprsjprs.2019.04.015","article-title":"Deep learning in remote sensing applications: A meta-analysis and review","volume":"152","author":"Ma","year":"2019","journal-title":"ISPRS J. Photogramm. Remote Sens."},{"key":"ref_29","unstructured":"Zhou, T., Miao, Z., and Zhang, J. (2018, January 12\u201316). Combining CNN with hand-crafted features for image classification. Proceedings of the 2018 14th IEEE International Conference on Signal Processing (ICSP), Beijing, China."},{"key":"ref_30","doi-asserted-by":"crossref","unstructured":"Cao, Q., Zhong, Y., Ma, A., and Zhang, L. (2018, January 22\u201327). Urban land use\/land cover classification based on feature fusion fusing hyperspectral image and lidar data. Proceedings of the IGARSS 2018-2018 IEEE International Geoscience and Remote Sensing Symposium, Valencia, Spain.","DOI":"10.1109\/IGARSS.2018.8517361"},{"key":"ref_31","doi-asserted-by":"crossref","first-page":"155","DOI":"10.1016\/j.isprsjprs.2016.01.004","article-title":"Learning multiscale and deep representations for classifying remotely sensed imagery","volume":"113","author":"Zhao","year":"2016","journal-title":"ISPRS J. Photogramm. Remote Sens."},{"key":"ref_32","doi-asserted-by":"crossref","first-page":"3368","DOI":"10.1080\/2150704X.2015.1062157","article-title":"On combining multiscale deep learning features for the classification of hyperspectral remote sensing imagery","volume":"36","author":"Zhao","year":"2015","journal-title":"Int. J. Remote Sens."},{"key":"ref_33","doi-asserted-by":"crossref","unstructured":"L\u00e4ngkvist, M., Kiselev, A., Alirezaie, M., and Loutfi, A. (2016). Classification and segmentation of satellite orthoimagery using convolutional neural networks. Remote Sens., 8.","DOI":"10.3390\/rs8040329"},{"key":"ref_34","doi-asserted-by":"crossref","first-page":"2911","DOI":"10.1109\/JSTARS.2018.2836671","article-title":"Deep multiscale spectral-spatial feature fusion for hyperspectral images classification","volume":"11","author":"Liang","year":"2018","journal-title":"IEEE J. Sel. Top. Appl. Earth Obs. Remote Sens."},{"key":"ref_35","doi-asserted-by":"crossref","unstructured":"Tian, Q., Wan, S., Jin, P., Xu, J., Zou, C., and Li, X. (2018, January 21\u201322). A novel feature fusion with self-adaptive weight method based on deep learning for image classification. Proceedings of the Pacific Rim Conference on Multimedia, Hefei, China.","DOI":"10.1007\/978-3-030-00776-8_39"},{"key":"ref_36","doi-asserted-by":"crossref","unstructured":"Zhang, Y., Huynh, C.P., and Ngan, K.N. (2019). Feature fusion with predictive weighting for spectral image classification and segmentation. IEEE Trans. Geosci. Remote Sens., 6792\u20136807.","DOI":"10.1109\/TGRS.2019.2908679"},{"key":"ref_37","doi-asserted-by":"crossref","first-page":"6712","DOI":"10.1109\/TGRS.2018.2841823","article-title":"Exploring hierarchical convolutional features for hyperspectral image classification","volume":"56","author":"Cheng","year":"2018","journal-title":"IEEE Trans. Geosci. Remote Sens."},{"key":"ref_38","doi-asserted-by":"crossref","first-page":"3459","DOI":"10.1080\/01431161.2015.1055607","article-title":"Hyperspectral classification via deep networks and superpixel segmentation","volume":"36","author":"Liu","year":"2015","journal-title":"Int. J. Remote Sens."},{"key":"ref_39","doi-asserted-by":"crossref","first-page":"1","DOI":"10.1016\/j.cviu.2017.03.007","article-title":"Superpixels: An evaluation of the state-of-the-art","volume":"166","author":"Stutz","year":"2018","journal-title":"Comput. Vis. Image Underst."},{"key":"ref_40","doi-asserted-by":"crossref","unstructured":"Yamaguchi, K., McAllester, D., and Urtasun, R. (2014, January 6\u201312). Efficient joint segmentation, occlusion labeling, stereo and flow estimation. Proceedings of the European Conference on Computer Vision, Zurich, Switzerland.","DOI":"10.1007\/978-3-319-10602-1_49"},{"key":"ref_41","doi-asserted-by":"crossref","unstructured":"Yao, J., Boben, M., Fidler, S., and Urtasun, R. (2015, January 8\u201310). Real-time coarse-to-fine topologically preserving segmentation. Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, Boston, America.","DOI":"10.1109\/CVPR.2015.7298913"},{"key":"ref_42","doi-asserted-by":"crossref","first-page":"2278","DOI":"10.1109\/5.726791","article-title":"Gradient-based learning applied to document recognition","volume":"86","author":"LeCun","year":"1998","journal-title":"Proc. IEEE"},{"key":"ref_43","unstructured":"Krizhevsky, A., Sutskever, I., and Hinton, G.E. (2012). Imagenet classification with deep convolutional neural networks. Proceedings of the Advances in Neural Information Processing Systems, Neural Information Processing Systems Foundation, Inc."},{"key":"ref_44","unstructured":"Simonyan, K., and Zisserman, A. (2014). Very deep convolutional networks for large-scale image recognition. arXiv."},{"key":"ref_45","doi-asserted-by":"crossref","unstructured":"He, K., Zhang, X., Ren, S., and Sun, J. (2016, January 27\u201330). Deep residual learning for image recognition. Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, Las Vegas, America.","DOI":"10.1109\/CVPR.2016.90"},{"key":"ref_46","doi-asserted-by":"crossref","unstructured":"Huang, G., Liu, Z., Van Der Maaten, L., and Weinberger, K.Q. (2017, January 22\u201325). Densely connected convolutional networks. Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, Honolulu, America.","DOI":"10.1109\/CVPR.2017.243"},{"key":"ref_47","doi-asserted-by":"crossref","unstructured":"Han, X., Zhong, Y., Cao, L., and Zhang, L. (2017). Pre-trained Alexnet architecture with pyramid pooling and supervision for high spatial resolution remote sensing image scene classification. Remote Sens., 9.","DOI":"10.3390\/rs9080848"},{"key":"ref_48","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_49","doi-asserted-by":"crossref","unstructured":"Xu, Y., Xie, Z., Feng, Y., and Chen, Z. (2018). Road extraction from high-resolution remote sensing imagery using deep learning. Remote Sens., 10.","DOI":"10.3390\/rs10091461"},{"key":"ref_50","doi-asserted-by":"crossref","unstructured":"Tao, Y., Xu, M., Lu, Z., and Zhong, Y. (2018). DenseNet-based depth-width double reinforced deep learning neural network for high-resolution remote sensing image per-pixel classification. Remote Sens., 10.","DOI":"10.3390\/rs10050779"},{"key":"ref_51","doi-asserted-by":"crossref","unstructured":"Mahdianpari, M., Salehi, B., Rezaee, M., Mohammadimanesh, F., and Zhang, Y. (2018). Very deep convolutional neural networks for complex land cover mapping using multispectral remote sensing imagery. Remote Sens., 10.","DOI":"10.3390\/rs10071119"},{"key":"ref_52","unstructured":"Ioffe, S., and Szegedy, C. (2015). Batch normalization: Accelerating deep network training by reducing internal covariate shift. arXiv."},{"key":"ref_53","doi-asserted-by":"crossref","first-page":"239","DOI":"10.1016\/j.isprsjprs.2003.10.002","article-title":"Multi-resolution, object-oriented fuzzy analysis of remote sensing data for GIS-ready information","volume":"58","author":"Benz","year":"2004","journal-title":"ISPRS J. Photogramm. Remote Sens."},{"key":"ref_54","unstructured":"Kingma, D.P., and Ba, J. (2014). Adam: A method for stochastic optimization. arXiv."},{"key":"ref_55","doi-asserted-by":"crossref","unstructured":"Paisitkriangkrai, S., Sherrah, J., Janney, P., and Hengel, V.-D. (2015, January 11\u201312). Others Effective semantic pixel labelling with convolutional networks and conditional random fields. Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition Workshops, Boston, America.","DOI":"10.1109\/CVPRW.2015.7301381"}],"container-title":["Remote Sensing"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/www.mdpi.com\/2072-4292\/12\/1\/66\/pdf","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,10,11]],"date-time":"2025-10-11T13:44:55Z","timestamp":1760190295000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.mdpi.com\/2072-4292\/12\/1\/66"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2019,12,23]]},"references-count":55,"journal-issue":{"issue":"1","published-online":{"date-parts":[[2020,1]]}},"alternative-id":["rs12010066"],"URL":"https:\/\/doi.org\/10.3390\/rs12010066","relation":{},"ISSN":["2072-4292"],"issn-type":[{"value":"2072-4292","type":"electronic"}],"subject":[],"published":{"date-parts":[[2019,12,23]]}}}