{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,4,1]],"date-time":"2026-04-01T18:28:07Z","timestamp":1775068087518,"version":"3.50.1"},"reference-count":49,"publisher":"MDPI AG","issue":"5","license":[{"start":{"date-parts":[[2018,5,18]],"date-time":"2018-05-18T00:00:00Z","timestamp":1526601600000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Remote Sensing"],"abstract":"<jats:p>Deep neural networks (DNNs) face many problems in the very high resolution remote sensing (VHRRS) per-pixel classification field. Among the problems is the fact that as the depth of the network increases, gradient disappearance influences classification accuracy and the corresponding increasing number of parameters to be learned increases the possibility of overfitting, especially when only a small amount of VHRRS labeled samples are acquired for training. Further, the hidden layers in DNNs are not transparent enough, which results in extracted features not being sufficiently discriminative and significant amounts of redundancy. This paper proposes a novel depth-width-reinforced DNN that solves these problems to produce better per-pixel classification results in VHRRS. In the proposed method, densely connected neural networks and internal classifiers are combined to build a deeper network and balance the network depth and performance. This strengthens the gradients, decreases negative effects from gradient disappearance as the network depth increases and enhances the transparency of hidden layers, making extracted features more discriminative and reducing the risk of overfitting. In addition, the proposed method uses multi-scale filters to create a wider neural network. The depth of the filters from each scale is controlled to decrease redundancy and the multi-scale filters enable utilization of joint spatio-spectral information and diverse local spatial structure simultaneously. Furthermore, the concept of network in network is applied to better fuse the deeper and wider designs, making the network operate more smoothly. The results of experiments conducted on BJ02, GF02, geoeye and quickbird satellite images verify the efficacy of the proposed method. The proposed method not only achieves competitive classification results but also proves that the network can continue to be robust and perform well even while the amount of labeled training samples is decreasing, which fits the small training samples situation faced by VHRRS per-pixel classification.<\/jats:p>","DOI":"10.3390\/rs10050779","type":"journal-article","created":{"date-parts":[[2018,5,21]],"date-time":"2018-05-21T04:07:30Z","timestamp":1526875650000},"page":"779","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":54,"title":["DenseNet-Based Depth-Width Double Reinforced Deep Learning Neural Network for High-Resolution Remote Sensing Image Per-Pixel Classification"],"prefix":"10.3390","volume":"10","author":[{"ORCID":"https:\/\/orcid.org\/0000-0002-8757-0174","authenticated-orcid":false,"given":"Yiting","family":"Tao","sequence":"first","affiliation":[{"name":"State Key Laboratory of Information Engineering in Surveying, Mapping and Remote Sensing, Wuhan University, Wuhan 430072, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-8434-8009","authenticated-orcid":false,"given":"Miaozhong","family":"Xu","sequence":"additional","affiliation":[{"name":"State Key Laboratory of Information Engineering in Surveying, Mapping and Remote Sensing, Wuhan University, Wuhan 430072, China"},{"name":"Collaborative Innovation Center of Geospatial Technology, Wuhan University, Wuhan 430072, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Zhongyuan","family":"Lu","sequence":"additional","affiliation":[{"name":"State Key Laboratory of Information Engineering in Surveying, Mapping and Remote Sensing, Wuhan University, Wuhan 430072, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-9446-5850","authenticated-orcid":false,"given":"Yanfei","family":"Zhong","sequence":"additional","affiliation":[{"name":"State Key Laboratory of Information Engineering in Surveying, Mapping and Remote Sensing, Wuhan University, Wuhan 430072, China"},{"name":"Collaborative Innovation Center of Geospatial Technology, Wuhan University, Wuhan 430072, China"}],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"1968","published-online":{"date-parts":[[2018,5,18]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","unstructured":"Huang, Z., Cheng, G., Wang, H., Li, H., Shi, L., and Pan, C. (2016, January 10\u201315). Building extraction from multi-source remote sensing images via deep deconvolution neural networks. Proceedings of the IEEE International Geoscience and Remote Sensing Symposium (IGARSS), Beijing, China.","DOI":"10.1109\/IGARSS.2016.7729471"},{"key":"ref_2","doi-asserted-by":"crossref","first-page":"709","DOI":"10.1109\/LGRS.2017.2672734","article-title":"Road structure refined CNN for road extraction in aerial image","volume":"14","author":"Wei","year":"2017","journal-title":"IEEE Geosci. Remote Sens. Lett."},{"key":"ref_3","unstructured":"Hwang, J.-J., and Liu, T.-L. (arXiv, 2015). Pixel-wise deep learning for contour detection, arXiv."},{"key":"ref_4","doi-asserted-by":"crossref","first-page":"4544","DOI":"10.1109\/TGRS.2016.2543748","article-title":"Spectral\u2013spatial feature extraction for hyperspectral image classification: A dimension reduction and deep learning approach","volume":"54","author":"Zhao","year":"2016","journal-title":"IEEE Trans. Geosci. Remote Sens."},{"key":"ref_5","doi-asserted-by":"crossref","first-page":"652","DOI":"10.1016\/j.isprsjprs.2011.04.006","article-title":"Identification of hazelnut fields using spectral and Gabor textural features","volume":"66","author":"Reis","year":"2011","journal-title":"ISPRS J. Photogramm. Remote Sens."},{"key":"ref_6","doi-asserted-by":"crossref","unstructured":"Wang, T., Zhang, H., Lin, H., and Fang, C. (2016). Textural\u2013spectral feature-based species classification of mangroves in Mai Po Nature Reserve from Worldview-3 imagery. Remote Sens., 8.","DOI":"10.3390\/rs8010024"},{"key":"ref_7","doi-asserted-by":"crossref","unstructured":"Yu, H., Yang, W., Xia, G.-S., and Liu, G. (2016). A color-texture-structure descriptor for high-resolution satellite image classification. Remote Sens., 8.","DOI":"10.3390\/rs8030259"},{"key":"ref_8","doi-asserted-by":"crossref","unstructured":"Huang, L., 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_9","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_10","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_11","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_12","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_13","doi-asserted-by":"crossref","unstructured":"Bazi, Y., and Melgani, F. (2018). Convolutional SVM Networks for Object Detection in UAV Imagery. IEEE Trans. Geosci. Remote Sens., 1\u201312.","DOI":"10.1109\/TGRS.2018.2790926"},{"key":"ref_14","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, Fontainebleau Resort, Miami, FL, USA."},{"key":"ref_15","doi-asserted-by":"crossref","unstructured":"Pohlen, T., Hermans, A., Mathias, M., and Leibe, B. (arXiv, 2016). Full-Resolution Residual Networks for Semantic Segmentation in Street Scenes, arXiv.","DOI":"10.1109\/CVPR.2017.353"},{"key":"ref_16","doi-asserted-by":"crossref","first-page":"391","DOI":"10.1109\/TGRS.2017.2748160","article-title":"Unsupervised spectral-spatial feature learning via deep residual conv-deconv network for hyperspectral image classification","volume":"56","author":"Mou","year":"2018","journal-title":"IEEE Trans. Geosci. Remote Sens."},{"key":"ref_17","doi-asserted-by":"crossref","first-page":"4843","DOI":"10.1109\/TIP.2017.2725580","article-title":"Going deeper with contextual CNN for hyperspectral image classification","volume":"26","author":"Lee","year":"2017","journal-title":"IEEE Trans. Image Process."},{"key":"ref_18","doi-asserted-by":"crossref","unstructured":"Huang, G., Liu, Z., Weinberger, K.Q., and van der Maaten, L. (2017, January 21\u201326). Densely connected convolutional networks. Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, Honolulu, HI, USA.","DOI":"10.1109\/CVPR.2017.243"},{"key":"ref_19","doi-asserted-by":"crossref","unstructured":"Huang, G., Sun, Y., Liu, Z., Sedra, D., and Weinberger, K.Q. (2016, January 6\u201312). Deep networks with stochastic depth. Proceedings of the European Conference on Computer Vision (ECCV), Amsterdam, The Netherlands.","DOI":"10.1007\/978-3-319-46493-0_39"},{"key":"ref_20","unstructured":"Springenberg, J.T., Dosovitskiy, A., Brox, T., and Riedmiller, M. (arXiv, 2014). Striving for simplicity: The all convolutional net, arXiv."},{"key":"ref_21","unstructured":"Lee, C.Y., Xie, S., Gallagher, P., Zhang, Z., and Tu, Z. (2015, January 9\u201312). Deeply-supervised nets. Proceedings of the Artificial Intelligence and Statistics, San Diego, CA, USA."},{"key":"ref_22","unstructured":"Krizhevsky, A., Sutskever, I., and Hinton, G.E. (2012, January 3\u20138). ImageNet classification with deep convolutional neural networks. Proceedings of the Advances in Neural Information Processing Systems (NIPS), Lake Tahoe, NV, USA."},{"key":"ref_23","unstructured":"Simonyan, K., and Zisserman, A. (arXiv, 2014). Very deep convolutional networks for large-scale image recognition, arXiv."},{"key":"ref_24","unstructured":"Lin, M., Chen, Q., and Yan, S. (arXiv, 2013). Network in network, arXiv."},{"key":"ref_25","doi-asserted-by":"crossref","first-page":"1983","DOI":"10.1162\/NECO_a_00766","article-title":"Fusion of scores in a detection context based on Alpha integration","volume":"27","author":"Soriano","year":"2015","journal-title":"Neural Comput."},{"key":"ref_26","doi-asserted-by":"crossref","unstructured":"Tao, Y., Xu, M., Zhong, Y., and Cheng, Y. (2017). GAN-Assisted Two-Stream Neural Network for High-Resolution Remote Sensing Image Classification. Remote Sens., 9.","DOI":"10.3390\/rs9121328"},{"key":"ref_27","doi-asserted-by":"crossref","first-page":"2349","DOI":"10.1109\/TGRS.2017.2778343","article-title":"Two-Stream Deep Architecture for Hyperspectral Image Classification","volume":"56","author":"Hao","year":"2017","journal-title":"IEEE Trans. Geosci. Remote Sens."},{"key":"ref_28","doi-asserted-by":"crossref","first-page":"937","DOI":"10.1109\/TGRS.2017.2756851","article-title":"Multisource remote sensing data classification based on convolutional neural network","volume":"56","author":"Xu","year":"2018","journal-title":"IEEE Trans. Geosci. Remote Sens."},{"key":"ref_29","doi-asserted-by":"crossref","unstructured":"Hu, J., Mou, L., Schmitt, A., and Zhu, X.X. (2017, January 6\u20138). FusioNet: A two-stream convolutional neural network for urban scene classification using PolSAR and hyperspectral data. Proceedings of the Urban Remote Sensing Event (JURSE), Dubai, UAE.","DOI":"10.1109\/JURSE.2017.7924565"},{"key":"ref_30","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_31","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 International Conference on Computer Vision and Pattern Recognition, Boston, MA, USA.","DOI":"10.1109\/CVPR.2015.7298594"},{"key":"ref_32","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 (MM), Orlando, FL, USA.","DOI":"10.1145\/2647868.2654889"},{"key":"ref_33","doi-asserted-by":"crossref","first-page":"7405","DOI":"10.1109\/TGRS.2016.2601622","article-title":"Learning rotation-invariant convolutional neural networks for object detection in VHR optical remote sensing images","volume":"54","author":"Cheng","year":"2016","journal-title":"IEEE Trans. Geosci. Remote Sens."},{"key":"ref_34","doi-asserted-by":"crossref","unstructured":"Liang, H., Lin, X., Zhang, Q., and Kang, X. (2017, January 14\u201316). Recognition of spoofed voice using convolutional neural networks. Proceedings of the IEEE Global Conference on Signal and Information Processing (GlobalSIP), Montreal, QC, Canada.","DOI":"10.1109\/GlobalSIP.2017.8308651"},{"key":"ref_35","doi-asserted-by":"crossref","unstructured":"Salehinejad, H., Barfett, J., Aarabi, P., Valaee, S., Colak, E., Gray, B., and Dowdell, T. (arXiv, 2017). A Convolutional Neural Network for Search Term Detection, arXiv.","DOI":"10.1109\/PIMRC.2017.8292587"},{"key":"ref_36","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_37","doi-asserted-by":"crossref","first-page":"6232","DOI":"10.1109\/TGRS.2016.2584107","article-title":"Deep feature extraction and classification of hyperspectral images based on convolutional neural networks","volume":"54","author":"Chen","year":"2016","journal-title":"IEEE Trans. Geosci. Remote Sens."},{"key":"ref_38","doi-asserted-by":"crossref","first-page":"2486","DOI":"10.1109\/TGRS.2016.2645610","article-title":"Accurate object localization in remote sensing images based on convolutional neural networks","volume":"55","author":"Long","year":"2017","journal-title":"IEEE Trans. Geosci. Remote Sens."},{"key":"ref_39","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_40","first-page":"23","article-title":"An Unsupervised Convolutional Feature Fusion Network for Deep Representation of Remote Sensing Images","volume":"15","author":"Yu","year":"2018","journal-title":"IEEE Geosci. Remote Sens. Lett."},{"key":"ref_41","doi-asserted-by":"crossref","unstructured":"Song, W., Li, S., Fang, L., and Lu, T. (2018). Hyperspectral Image Classification with Deep Feature Fusion Network. IEEE Trans. Geosci. Remote Sens., 1\u201312.","DOI":"10.1109\/TGRS.2018.2794326"},{"key":"ref_42","doi-asserted-by":"crossref","first-page":"645","DOI":"10.1109\/TGRS.2016.2612821","article-title":"Convolutional neural networks for large-scale remote-sensing image classification","volume":"55","author":"Maggiori","year":"2017","journal-title":"IEEE Trans. Geosci. Remote Sens."},{"key":"ref_43","unstructured":"Kampffmeyer, M., Salberg, A.-B., and Jenssen, R. (July, January 26). Semantic segmentation of small objects and modeling of uncertainty in urban remote sensing images using deep convolutional neural networks. Proceedings of the IEEE Computer Vision and Pattern Recognition Workshops (CVPRW), Las Vegas, NV, USA."},{"key":"ref_44","unstructured":"Yu, F., and Koltun, V. (arXiv, 2015). Multi-scale context aggregation by dilated convolutions, arXiv."},{"key":"ref_45","doi-asserted-by":"crossref","unstructured":"Szegedy, C., Vanhoucke, V., Ioffe, S., Shlens, J., and Wojna, Z. (2016, January 27\u201330). Rethinking the inception architecture for computer vision. Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (CVPR), Las Vegas, NV, USA.","DOI":"10.1109\/CVPR.2016.308"},{"key":"ref_46","first-page":"249","article-title":"Understanding the difficulty of training deep feedforward neural networks","volume":"9","author":"Glorot","year":"2010","journal-title":"J. Mach. Learn. Res."},{"key":"ref_47","doi-asserted-by":"crossref","first-page":"881","DOI":"10.1109\/TGRS.2016.2616585","article-title":"Dense semantic labeling of subdecimeter resolution images with convolutional neural networks","volume":"55","author":"Volpi","year":"2017","journal-title":"IEEE Trans. Geosci. Remote Sens."},{"key":"ref_48","doi-asserted-by":"crossref","first-page":"6805","DOI":"10.1109\/TGRS.2017.2734697","article-title":"Unsupervised-Restricted Deconvolutional Neural Network for Very High Resolution Remote-Sensing Image Classification","volume":"55","author":"Tao","year":"2017","journal-title":"IEEE Trans. Geosci. Remote Sens."},{"key":"ref_49","doi-asserted-by":"crossref","first-page":"2094","DOI":"10.1109\/JSTARS.2014.2329330","article-title":"Deep learning-based classification of hyperspectral data","volume":"7","author":"Chen","year":"2014","journal-title":"IEEE J. Sel. Top. Appl. Earth Obs. Remote Sens."}],"container-title":["Remote Sensing"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/www.mdpi.com\/2072-4292\/10\/5\/779\/pdf","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,10,11]],"date-time":"2025-10-11T15:04:48Z","timestamp":1760195088000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.mdpi.com\/2072-4292\/10\/5\/779"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2018,5,18]]},"references-count":49,"journal-issue":{"issue":"5","published-online":{"date-parts":[[2018,5]]}},"alternative-id":["rs10050779"],"URL":"https:\/\/doi.org\/10.3390\/rs10050779","relation":{},"ISSN":["2072-4292"],"issn-type":[{"value":"2072-4292","type":"electronic"}],"subject":[],"published":{"date-parts":[[2018,5,18]]}}}