Urban Remote Sensing Image Segmentation using Dense U-Net+

International Journal of Computer Science and Engineering
© 2022 by SSRG - IJCSE Journal
Volume 9 Issue 3
Year of Publication : 2022
Authors : Keerti Maithil, Tasneem Bano Rehman

How to Cite?

Keerti Maithil, Tasneem Bano Rehman, "Urban Remote Sensing Image Segmentation using Dense U-Net+," SSRG International Journal of Computer Science and Engineering , vol. 9,  no. 3, pp. 21-28, 2022. Crossref, https://doi.org/10.14445/23488387/IJCSE-V9I3P103


For a long time, man has been dreaming that we should make such a machine with human-like intelligence, the power to understand like a human and can think like a human. One of the fascinating ideas was to give computers the ability to see and interpret the world around them. The concept of computer vision is based on training a computer, which processes an image to understand and analyze it at a pixel level. Technically, machines attempt to retrieve visual information, handle it, and interpret results through special software algorithms. An important subject within computer vision is image segmentation. Image Segmentation is a process of identifying objects or boundaries to simplify an image and efficiently analyzing it by dividing the image into different regions based on the characteristics of pixels. The existing U-net-based segmentation model and its other variants are the deep learning module design, especially for biomedical image segmentation; initially, it was proposed for cell segmentation. This work finds a new application area: Urban Remote Sensing Image Segmentation using the Dense U-Net+ model. DenseU-Net+ is a powerful form of the U–net architecture inspired by DenseNet. The imbalance is a serious problem in the remote sensing image segmentation class. Another one is that segmentation of large objects in the image is easy, but for small objects, segmentation causes difficulties.


Image segmentation, Computer vision, DenseNet, U-Net, DenseU-Net.


[1] Peng Shuai Yin, Rui Yuan, Yiming Cheng, and Qingo WU, “Deep Guidance Network for Biomedical Image Segmentation,” IEEE Access , 2020. Doi: 10.1109/ACCESS.2020.3002835
[2] Rongsheng Dong, Xiaoquan Pan, and Fengying Li, “Dense U-Net-Based Semantic Segmentation of Small Objects in Urban Remote Sensing Images,” IEEE Access , 2019. Doi: 10.1109/ACCESS.2019.2917952
[3] Mahnoor Ali, Syed Omer Gilani, Asim Waris, Kashan Zafar, and Mohsin Jamil, (Senior Member, IEEE), “Brain Tumor Image Segmentation using Deep Networks,” IEEE Access, 2020. Doi: 10.1109/ACCESS.2020
[4] Takafumi Nemoto, Natsumi Futakami, Masamichi Yagi, Atsuhiro Kumabe, Atsuya Takeda, Etsuo Kunieda and Naoyuki Shigematsu, “ Efficacy Evaluation of 2D, 3D U-Net Semantic Segmentation and Atlas-Based Segmentation of Normal Lungs Excluding the Trachea and Main Bronchi,” Journal of Radiation Research, vol. 61, No. 2, Pp. 257–264, 2020. Doi: 10.1093/Jrr/Rrz086 
[5] Wei Guo, Han Xun, Zhou, Zhaoxuan Gong, and Guodong Zhang, “Double U-Nets for Image Segmentation by Integrating the Region and Boundary Information,” IEEE Access, 2021. Doi:10.1109/ACCESS.2021.3075294
[6] Santosh Jangid, P S Bhatnagar, “Semantic Image Segmentation Using Deep Convolutional Neural Networks and Super-Pixels,” International Journal of Applied Engineering Research, 2018.
[7] Ameya Wagh, Shubham Jain, Apratim Mukherjee, Emmanuel Agu, Peder C. Pedersen, Diane Strong, Bengisu Tulu, Clifford Lindsay, and Ziyang Liu, “Semantic Segmentation of Smartphone Wound Images: Comparative Analysis of AHRF and CNN-Based Approaches,” IEEE Access, 2020. Doi: 10.1109/ACCESS.2020.3014175
[8] Shuchao Chen, Han Yang, Jiawen Fu, Weijian Mei, Shuai Ren, Yifei Liu, Zhihua Zhu, Lizhi Liu, Haogiang L, and Hongbo Chen, “U-Net Plus: Deep Semantic Segmentation for Esophagus and Esophageal Cancer in Computed Tomography Images,” IEEE Access, 2019. Doi: 10.1109/ACCESS.2019.2923760
[9] Xiaoqiang WU and Lng Zhao, “Study on Iris Segmentation Algorithm Based on Dense U-Net,” IEEE Access, 2019. Doi:10.1109/ACCESS.2019.2938809
[10] Wataru Ohyama, Masakazu Suzuki, and Seiichi Uchida, “ Detecting Mathematical Expressions in Scientific Document Images Using a U-Net Trained on A Diverse Dataset,” IEEE Access, 2019. Doi:10.1109/ACCESS.2019.2945825
[11] Nhian Siddique, Sidiki Pahedini, Colin P. Elkin and Vijay Devabhaktuni, “U-Net and Its Variants for Medical Image Segmentation: A Review of Theory and Applications,” IEEE Access, 2021. Doi:10.1109/ACCESS.2021.3086020
[12] Leilei Xu, Yujun Liu, Peng Yang, Hao Chen, Hanyue Zhang, Dan Wang, and Xin Zhang, “HA U-Net: Improved Model for Building Extraction From High-Resolution Remote Sensing Imagery,” IEEE Access, 2020. Doi:10.1109/ACCESS.2021.3097630
[13] Neil Micallef, Dylan Seychell, and Claude J. Bajada, “Exploring the U-Net++ Model for Automatic Brain Tumor Segmentation,” IEEE Access, 2021. Doi:10.1109/ACCESS.2021.3111131
[14] Yaohui Liu, Lutz Gross, Zhiqiang Li, Xiaoli Li, Xiwei Fan, and Wenhua Qi, “Automatic Building Extraction on High-Resolution Remote Sensing Imagery Using Deep Convolutional Encoder-Decoder With Spatial Pyramid Pooling,” IEEE Access, 2019. Doi:10.1109/ACCESS.2019.2940527.
[15] Binge Cui, Xin Chen, and Yan Lu, “Semantic Segmentation of Remote Sensing Images Using Transfer Learning and Deep Convolutional Neural Network With Dense Connection,” IEEE Access, 2019. DOI 10.1109/ACCESS.2020.3003914
[16] Teerapong Panboonyue, Kulsawasd Jitkajornwanich, Siam Lawawirojwong, Panu Srestasathiern and Peerapon Vateekul, “Transformer-Based Decoder Designs for Semantic Segmentation on Remotely Sensed Images,” Remote Sensing, vol. 13, pp. 5100, 2021. Https://Doi:Org/10.3390/Rs13245100.
[17] Tuan Linh Giang, Kinh Bac Dang, Quang Toan Le, Vu Giang Nguyen, Si Son Tong, and Van-Manh Pham, “U-Net Convolutional Networks for Mining Land Cover Classification Based on High-Resolution UAV Imagery,” IEEE Access, 2020, Doi:10.1109/ACCESS.2020.3030112
[18] Rui Li, Chenxi Duan, Shunyi Zheng, Ce Zhang, and Peter M. Atkinson, “MACU-Net for Semantic Segmentation of Fine-Resolution Remotely Sensed Images,” Electrical Engineering and Systems Science, 2022.
[19] Jian Huang, Shanhui Liu, Yutian Tang, and Xiushan Zhang; “Object-Level Remote Sensing Image Augmentation Using U-Net-Based Generative Adversarial Networks,” Wireless Communications and Mobile Computing, Article ID 1230279, 2021. Https://Doi:Org/10.1155/2021/1230279
[20] Hafiz Sami Ullah, Muhammad Hamza Asad, and Abdul Bais, “End To End Segmentation of Canola Field Images Using Dilated U-Net,” IEEE Access, 2021. Doi:10.1109/ACCESS.2021.3073715
[21] Mo Han, Yuwei Bao, Ziyan Sun, Shiping Wen, Liming Xia, Jingyanj Zhao, Junfeng Du, and Zheng Yan, “Automatic Segmentation of Human Placenta Images With U-Net,” IEEE Access, 2019. Doi:10.1109/ACCESS.2019.2958133
[22] Xiaolong Liu, Zhidong Deng, Yuhan Yangn, “Recent Progress in Semantic Image Segmentation,” 2018. Https://Doi:Org/10.1007/S10462-018-9641-3
[23] Shervin Minaee, Yuri Boykov, Fatih Porikli, Antonio Plaza, Nasser Kehtarnavaz, and Demetri Terzopoulos, “Image Segmentation Using Deep Learning: A Survey,” IEEE Transactions on Pattern Analysis and Machine Intelligence, 2020.
[24] Edy Irwansyah, Yaya Heryadi, “Semantic Image Segmentation for Building Detection in Urban Areas with Aerial Photograph Image Using U-Net Models,” 2020 IEEE Asia-Pacific Conference on Geoscience, Electronics and Remote Sensing Technology (AGERS), 2021.
[25] Ruyue Xin, Jiang Zhang, and Yitong Shao, “Complex Network Classification with Convolutional Neural Network,” Tsinghua Science and Technology, 2020. DOI. 10 .26599/T ST.2019.9010055.