Segmentation of Small Objects in Satellite Imagery Using Dense U-Net in
Massachusetts Buildings Dataset

Segmentation of Small Objects in Satellite Imagery Using Dense U-Net in
Massachusetts Buildings Dataset

class imbalance, dataset, end-to-end, convolution, denseU-net

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%

Muhammad Iqbal Izzul Haq1
, Aniati Murni Arymurthy2
, Irham Muhammad Fadhil3

Universitas Indonesia

30-06-2022

Fajar Bagus W

PDF

Indonesia

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