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Research Article | Open Access
Volume 9 | Issue 3 | Year 2022 | Article Id. IJCSE-V9I3P103 | DOI : https://doi.org/10.14445/23488387/IJCSE-V9I3P103

Urban Remote Sensing Image Segmentation using Dense U-Net+

Keerti Maithil, Tasneem Bano Rehman

Citation :

Keerti Maithil, Tasneem Bano Rehman, "Urban Remote Sensing Image Segmentation using Dense U-Net+," 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.

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