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

10.14445/23488387/IJCSE-V9I3P103

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

Abstract:

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.

Keywords:

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

References:

[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.