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All Journal Jurnal RESTI (Rekayasa Sistem dan Teknologi Informasi)
Muhammad Iqbal Izzul Haq
Universitas Indonesia
Computer Science & IT Engineering

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bahasa inggris Muhammad Iqbal Izzul Haq; Aniati Murni Arymurthy; Irham Muhammad Fadhil
Jurnal RESTI (Rekayasa Sistem dan Teknologi Informasi) Vol 6 No 3 (2022): Juni 2022
Publisher : Ikatan Ahli Informatika Indonesia (IAII)

Show Abstract | Download Original | Original Source | Check in Google Scholar | Full PDF (369.564 KB) | DOI: 10.29207/resti.v6i3.3993

Abstract

Class imbalance is a serious problem that disrupts the process of semantic segmentation of satellite imagery in urban areas in Earth remote sensing. Due to the large objects dominating the segmentation process, small object are consequently limited, so solutions based on optimizing overall accuracy are often unsatisfactory. Due to the class imbalance of semantic segmentation in Earth remote sensing images in urban areas, we developed the concept of Down-Sampling Block (DownBlock) to obtain contextual information and Up-Sampling Block (UpBlock) to restore the original resolution. We proposed an end-to-end deep convolutional neural network (DenseU-Net) architecture for pixel-wise urban remote sensing image segmentation. this method to segmentation the small object in satellite imagery.The accuracy of the small object class in this study was further improved using our proposed method. This study used data from the Massachusetts Buildings dataset using Dense U-Net method and obtained an overall accuracy of 84.34%.
Co-Authors Aniati Murni Arymurthy Irham Muhammad Fadhil