{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,10,12]],"date-time":"2025-10-12T02:24:49Z","timestamp":1760235889918,"version":"build-2065373602"},"reference-count":45,"publisher":"MDPI AG","issue":"10","license":[{"start":{"date-parts":[[2021,10,4]],"date-time":"2021-10-04T00:00:00Z","timestamp":1633305600000},"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":["61906097,41875184"],"award-info":[{"award-number":["61906097,41875184"]}],"id":[{"id":"10.13039\/501100001809","id-type":"DOI","asserted-by":"publisher"}]},{"name":"Priority Academic Program Development of Jiangsu Higher Education Institutions(PAPD)","award":["None"],"award-info":[{"award-number":["None"]}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["IJGI"],"abstract":"<jats:p>Semantic segmentation of remote sensing images (RSI) plays a significant role in urban management and land cover classification. Due to the richer spatial information in the RSI, existing convolutional neural network (CNN)-based methods cannot segment images accurately and lose some edge information of objects. In addition, recent studies have shown that leveraging additional 3D geometric data with 2D appearance is beneficial to distinguish the pixels\u2019 category. However, most of them require height maps as additional inputs, which severely limits their applications. To alleviate the above issues, we propose a height aware-multi path parallel network (HA-MPPNet). Our proposed MPPNet first obtains multi-level semantic features while maintaining the spatial resolution in each path for preserving detailed image information. Afterward, gated high-low level feature fusion is utilized to complement the lack of low-level semantics. Then, we designed the height feature decode branch to learn the height features under the supervision of digital surface model (DSM) images and used the learned embeddings to improve semantic context by height feature guide propagation. Note that our module does not need a DSM image as additional input after training and is end-to-end. Our method outperformed other state-of-the-art methods for semantic segmentation on publicly available remote sensing image datasets.<\/jats:p>","DOI":"10.3390\/ijgi10100672","type":"journal-article","created":{"date-parts":[[2021,10,4]],"date-time":"2021-10-04T09:24:15Z","timestamp":1633339455000},"page":"672","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":2,"title":["HA-MPPNet: Height Aware-Multi Path Parallel Network for High Spatial Resolution Remote Sensing Image Semantic Seg-Mentation"],"prefix":"10.3390","volume":"10","author":[{"ORCID":"https:\/\/orcid.org\/0000-0002-0673-7754","authenticated-orcid":false,"given":"Suting","family":"Chen","sequence":"first","affiliation":[{"name":"Jiangsu Collaborative Innovation Center of Atmospheric Environment and Equipment Technology (CICAEET), Nanjing University of Information Science & Technology, Nanjing 210044, China"},{"name":"Wuxi Institute of Technology (NUIST-WIT), Nanjing University of Information Science & Technology, Wuxi 214100, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Chaoqun","family":"Wu","sequence":"additional","affiliation":[{"name":"Jiangsu Collaborative Innovation Center of Atmospheric Environment and Equipment Technology (CICAEET), Nanjing University of Information Science & Technology, Nanjing 210044, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Mithun","family":"Mukherjee","sequence":"additional","affiliation":[{"name":"School of Artificial Intelligence, Nanjing University of Information Science & Technology, Nanjing 210044, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Yujie","family":"Zheng","sequence":"additional","affiliation":[{"name":"The 28th Research Institute of China Electronics Technology Group Corporation, Nanjing 210008, China"}],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"1968","published-online":{"date-parts":[[2021,10,4]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","unstructured":"Hu, B., Xu, Y., Huang, X., Cheng, Q., Ding, Q., Bai, L., and Li, Y. (2021). Improving Urban Land Cover Classification with Combined Use of Sentinel-2 and Sentinel-1 Imagery. ISPRS Int. J. Geo-Inf., 10.","DOI":"10.3390\/ijgi10080533"},{"key":"ref_2","doi-asserted-by":"crossref","unstructured":"Kampffmeyer, M.C., Salberg, A., 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. Paper presented at the 2016 IEEE Conference on Computer Vision and Pattern Recognition Workshops (CVPRW), Las Vegas, NV, USA.","DOI":"10.1109\/CVPRW.2016.90"},{"key":"ref_3","doi-asserted-by":"crossref","unstructured":"Damos, M.A., Zhu, J., Li, W., Hassan, A., and Khalifa, E. (2021). A Novel Urban Tourism Path Planning Approach Based on a Multiobjective Genetic Algorithm. ISPRS Int. J. Geo-Inf., 10.","DOI":"10.3390\/ijgi10080530"},{"key":"ref_4","doi-asserted-by":"crossref","unstructured":"Ding, C., Weng, L., Xia, M., and Lin, H. (2021). Non-Local Feature Search Network for Building and Road Segmentation of Remote Sensing Image. ISPRS Int. J. Geo-Inf., 10.","DOI":"10.3390\/ijgi10040245"},{"key":"ref_5","doi-asserted-by":"crossref","unstructured":"Liu, S., and Tang, J. (2021). Modified Deep Reinforcement Learning with Efficient Convolution Feature for Small Target Detection in VHR Remote Sensing Imagery. ISPRS Int. J. Geo-Inf., 10.","DOI":"10.3390\/ijgi10030170"},{"key":"ref_6","doi-asserted-by":"crossref","unstructured":"Xie, F., Hu, D., Li, F., Yang, J., and Liu, D. (2018). Semi-Supervised Classification for Hyperspectral Images Based on Multiple Classifiers and Relaxation Strategy. ISPRS Int. J. Geo-Inf., 7.","DOI":"10.3390\/ijgi7070284"},{"key":"ref_7","doi-asserted-by":"crossref","first-page":"487","DOI":"10.1080\/01431161.2019.1643937","article-title":"RWSNet: A semantic segmentation network based on SegNet combined with random walk for remote sensing","volume":"41","author":"Jiang","year":"2019","journal-title":"Remote Sens."},{"key":"ref_8","first-page":"640","article-title":"Fully Convolutional Networks for Semantic Segmentation","volume":"39","author":"Long","year":"2012","journal-title":"IEEE Trans. Pattern Anal. Mach. Intell."},{"key":"ref_9","doi-asserted-by":"crossref","first-page":"2481","DOI":"10.1109\/TPAMI.2016.2644615","article-title":"SegNet: A Deep Convolutional Encoder-Decoder Architecture for Image Segmentation","volume":"39","author":"Badrinarayanan","year":"2017","journal-title":"IEEE Trans. Pattern Anal. Mach. Intell."},{"key":"ref_10","doi-asserted-by":"crossref","unstructured":"Ronneberger, O., Fischer, P., and Brox, T. (2015, January 5\u20139). U-Net: Convolutional Networks for Biomedical Image Segmentation. Paper presented at the International Conference on Medical image computing and computer-assisted intervention, Munich, Germany.","DOI":"10.1007\/978-3-319-24574-4_28"},{"key":"ref_11","unstructured":"Chen, L., Papandreou, G., Kokkinos, I., Murphy, K., and Yuille, A. (2014). Semantic Image Segmentation with Deep Convolutional Nets and Fully Connected CRFs. arXiv."},{"key":"ref_12","unstructured":"Tao, A., Sapra, K., and Catanzaro, B. (2020). Hierarchical Multi-Scale Attention for Semantic Segmentation. arXiv."},{"key":"ref_13","first-page":"3349","article-title":"Deep High-Resolution Representation Learning for Visual Recognition","volume":"99","author":"Wang","year":"2020","journal-title":"IEEE Trans. Pattern Anal. Mach. Intell."},{"key":"ref_14","doi-asserted-by":"crossref","unstructured":"Zhao, H., Shi, J., Qi, X., Wang, X., and Jia, J. (2017, January 21\u201326). Pyramid scene parsing network. Paper presented at the IEEE conference on computer vision and pattern recognition, Honolulu, HI, USA.","DOI":"10.1109\/CVPR.2017.660"},{"key":"ref_15","doi-asserted-by":"crossref","first-page":"834","DOI":"10.1109\/TPAMI.2017.2699184","article-title":"DeepLab: Semantic Image Segmentation with Deep Convolutional Nets, Atrous Convolution, and Fully Connected CRFs","volume":"40","author":"Chen","year":"2017","journal-title":"IEEE Trans. Pattern Anal. Mach. Intell."},{"key":"ref_16","doi-asserted-by":"crossref","unstructured":"Yang, M., Yu, K., Zhang, C., Li, Z., and Yang, K. (2018, January 18\u201323). DenseASPP for Semantic Segmentation in Street Scenes. Paper presented at the 2018 IEEE\/CVF Conference on Computer Vision and Pattern Recognition, Salt Lake City, UT, USA.","DOI":"10.1109\/CVPR.2018.00388"},{"key":"ref_17","doi-asserted-by":"crossref","unstructured":"Wang, X., Girshick, R., Gupta, A., and He, K. (2018, January 18\u201323). Non-local Neural Networks. Paper presented at the 2018 IEEE\/CVF Conference on Computer Vision and Pattern Recognition, Salt Lake City, UT, USA.","DOI":"10.1109\/CVPR.2018.00813"},{"key":"ref_18","doi-asserted-by":"crossref","unstructured":"Zhao, H., Zhang, Y., Liu, S., Shi, J., Loy, C., Lin, D., and Jia, J. (2018, January 8\u201314). PSANet: Point-wise Spatial Attention Network for Scene Parsing. Paper presented at the 2018 European Conference on Computer Vision, Munich, Germany.","DOI":"10.1007\/978-3-030-01240-3_17"},{"key":"ref_19","doi-asserted-by":"crossref","unstructured":"Wurm, M., Droin, A., Stark, T., Gei\u00df, C., Sulzer, W., and Taubenb\u00f6ck, H. (2021). Deep Learning-Based Generation of Building Stock Data from Remote Sensing for Urban Heat Demand Modeling. ISPRS Int. J. Geo-Inf., 10.","DOI":"10.3390\/ijgi10010023"},{"key":"ref_20","first-page":"794","article-title":"IMG2DSM: Height simulation from single imagery using conditional generative adversarial net","volume":"15","author":"Ghamisi","year":"2018","journal-title":"Remote Sens."},{"key":"ref_21","doi-asserted-by":"crossref","first-page":"20","DOI":"10.1016\/j.isprsjprs.2017.11.011","article-title":"Beyond RGB: Very high resolution urban remote sensing with multimodal deep networks","volume":"140","author":"Audebert","year":"2018","journal-title":"ISPRS-J. Photogramm. Remote Sens"},{"key":"ref_22","unstructured":"Lichao, M., and Zhu, X.X. (2018). IM2HEIGHT: Height estimation from single monocular imagery via fully residual convolutional-deconvolutional network. arXiv."},{"key":"ref_23","doi-asserted-by":"crossref","unstructured":"Srivastava, S., Volpi, M., and Tuia, D. (2017, January 23\u201328). Joint height estimation and semantic labeling of monocular aerial images with CNNs. Proceedings of the 2017 IEEE International Geoscience and Remote Sensing Symposium (IGARSS), Fort Worth, TX, USA.","DOI":"10.1109\/IGARSS.2017.8128167"},{"key":"ref_24","doi-asserted-by":"crossref","first-page":"48","DOI":"10.1016\/j.isprsjprs.2018.06.007","article-title":"Deep multi-task learning for a geographically-regularized semantic segmentation of aerial images","volume":"144","author":"Volpi","year":"2018","journal-title":"ISPRS-J. Photogramm. Remote Sens."},{"key":"ref_25","unstructured":"Zilong, H., Wang, X., Huang, L., Huang, C., Wei, Y., and Liu, W. (November, January 27). CCNet: Criss-Cross Attention for Semantic Segmentation. Paper presented at the 2019 IEEE\/CVF International Conference on Computer Vision (ICCV), Seoul, Korea."},{"key":"ref_26","doi-asserted-by":"crossref","unstructured":"Woo, S., Park, J., Lee, J., and Kweon, I.S. (2018, January 8\u201314). CBAM: Convolutional Block Attention Module. Paper presented at the 2018 European Conference on Computer Vision, Munich, Germany.","DOI":"10.1007\/978-3-030-01234-2_1"},{"key":"ref_27","doi-asserted-by":"crossref","unstructured":"Fu, J., Liu, J., Tian, H., Li, Y., Bao, Y., Fang, Z., and Lu, H. (2019, January 15\u201320). Dual Attention Network for Scene Segmentation. \u201dPaper presented at the 2019 IEEE\/CVF Conference on Computer Vision and Pattern Recognition (CVPR), Long Beach, CA, USA.","DOI":"10.1109\/CVPR.2019.00326"},{"key":"ref_28","doi-asserted-by":"crossref","unstructured":"Guo, R., Liu, J., Li, N., Liu, S., Chen, F., Cheng, B., and Ma, C. (2018). Pixel-wise classification method for high resolution remote sensing imagery using deep neural networks. ISPRS Int. J. Geo-Inf., 7.","DOI":"10.3390\/ijgi7030110"},{"key":"ref_29","doi-asserted-by":"crossref","first-page":"94","DOI":"10.1016\/j.isprsjprs.2020.01.013","article-title":"ResUNet-a: A deep learning framework for semantic segmentation of remotely sensed data","volume":"162","author":"Diakogiannis","year":"2020","journal-title":"ISPRS J. Photogramm. Remote Sens."},{"key":"ref_30","doi-asserted-by":"crossref","unstructured":"Wang, J., Shen, L., Qiao, W., Dai, Y., and Li, Z. (2019). Deep Feature Fusion with Integration of Residual Connection and Attention Model for Classification of VHR Remote Sensing Images. Remote Sens., 11.","DOI":"10.3390\/rs11131617"},{"key":"ref_31","doi-asserted-by":"crossref","unstructured":"He, K., Zhang, X., Ren, S., and Sun, J. (2016, January 27\u201330). Deep residual learning for image recognition. Paper presented at the 2016 IEEE conference on computer vision and pattern recognition, Las Vegas, NV, USA.","DOI":"10.1109\/CVPR.2016.90"},{"key":"ref_32","doi-asserted-by":"crossref","first-page":"158","DOI":"10.1016\/j.isprsjprs.2017.11.009","article-title":"Classifification with an edge: Improving semantic image segmentation with boundary detection","volume":"135","author":"Marmanis","year":"2018","journal-title":"ISPRS J. Photogramm. Remote Sens."},{"key":"ref_33","doi-asserted-by":"crossref","unstructured":"Li, H., Qiu, K., Li, C., Mei, X., Hong, L., and Tao, C. (2020). SCAttNet: Semantic segmentation network with spatial and channel attention mechanism for high-resolution remote sensing images. IEEE Geosci. Remote Sens. Lett.","DOI":"10.1109\/LGRS.2020.2988294"},{"key":"ref_34","first-page":"1","article-title":"Hybrid Multiple Attention Network for Semantic Segmentation in Aerial Images","volume":"99","author":"Niu","year":"2021","journal-title":"IEEE Trans. Geosci. Remote Sens."},{"key":"ref_35","doi-asserted-by":"crossref","first-page":"3602","DOI":"10.1109\/TCYB.2020.3028931","article-title":"Joint and Progressive Subspace Analysis (JPSA) With Spatial\u2013Spectral Manifold Alignment for Semisupervised Hyperspectral Dimensionality Reduction","volume":"51","author":"Hong","year":"2020","journal-title":"IEEE Trans. Cybern."},{"key":"ref_36","doi-asserted-by":"crossref","unstructured":"Zhang, Z., Zhang, X., Peng, C., Xue, X., and Sun, J. (2018, January 8\u201314). Exfuse: Enhancing feature fusion for semantic segmentation. Paper presented at the 2018 European Conference on Computer Vision, Munich, Germany.","DOI":"10.1007\/978-3-030-01249-6_17"},{"key":"ref_37","doi-asserted-by":"crossref","unstructured":"Lin, T., Doll\u00e1r, P., Girshick, R.B., He, K., Hariharan, B., and Belongie, S. (2017, January 21\u201326). Feature Pyramid Networks for Object Detection. Paper presented at the IEEE conference on computer vision and pattern recognition, Honolulu, HI, USA.","DOI":"10.1109\/CVPR.2017.106"},{"key":"ref_38","unstructured":"Yann, D., Fan, A., Auli, M., and Grangier, D. (2017, January 6\u201311). Language Modeling with Gated Convolutional Networks. Paper presented at the 34th International Conference on Machine Learning (ICML) 2017, Sydney, NSW, Australia."},{"key":"ref_39","unstructured":"Towaki, T., Acuna, D., Jampani, V., and Fidler, S. (November, January 27). Gated-SCNN: Gated Shape CNNs for Semantic Segmentation. Paper presented at the 2019 IEEE\/CVF International Conference on Computer Vision (ICCV), Seoul, Kore."},{"key":"ref_40","unstructured":"Xiangtai, L., Zhao, H., Han, L., Tong, Y., Tan, S., and Yang, K. (2020, January 7\u201320). Gated Fully Fusion for Semantic Segmentation. Paper presented at the AAAI Conference on Artificial Intelligence, New York, NY, USA."},{"key":"ref_41","doi-asserted-by":"crossref","unstructured":"Peng, Y., Sun, S., Wang, Z., Pan, Y., and Li, R. (2020, January 4\u20136). Robust Semantic Segmentation by Dense Fusion Network on Blurred VHR Remote Sensing Images. Paper presented at the 2020 6th International Conference on Big Data and Information Analytics (BigDIA), ShenZhen, China.","DOI":"10.1109\/BigDIA51454.2020.00031"},{"key":"ref_42","doi-asserted-by":"crossref","unstructured":"Sun, S., Yang, L., Liu, W., and Li, R. (2018, January 19\u201320). Feature Fusion Through Multitask CNN for Large-scale Remote Sensing Image Segmentation. Proceedings of the 2018 10th IAPR Workshop on Pattern Recognition in Remote Sensing (PRRS), Beijing, China.","DOI":"10.1109\/PRRS.2018.8486170"},{"key":"ref_43","unstructured":"Wang, P., Shen, X., Cohen, S., Price, B., and Yuille, A. (2015, January 7\u201312). Towards unified depth and semantic prediction from a single image. Paper presented at the 2015 IEEE Conference on Computer Vision and Pattern Recognition (CVPR), Boston, MA, USA."},{"key":"ref_44","doi-asserted-by":"crossref","first-page":"2011","DOI":"10.1109\/TPAMI.2019.2913372","article-title":"Squeeze-and-Excitation Networks","volume":"42","author":"Hu","year":"2019","journal-title":"IEEE Trans. Pattern Anal. Mach. Intell."},{"key":"ref_45","doi-asserted-by":"crossref","first-page":"318","DOI":"10.1109\/TPAMI.2018.2858826","article-title":"Focal Loss for Dense Object Detection","volume":"42","author":"Lin","year":"2018","journal-title":"IEEE Trans. Pattern Anal. Mach. 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