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Aerial Image Semantic Segmentation Using Neural Search Network Architecture

Bui, Duc Thinh and Nguyen, Do Van and Nguyen, Thi Thu Thuy and Tran, Quoc Long and Le, Thanh Ha (2018) Aerial Image Semantic Segmentation Using Neural Search Network Architecture. In: (MIWAI 2018) 12th Multi-disciplinary International Conference on Artificial Intelligence, 18-20th November 2018, Hanoi, Vietnam.

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In remote sensing data analysis and computer vision, aerial image segmentation is a crucial research topic, which has many appli- cations in environmental and urban planning. Recently, deep learning is using to tackle many computer vision problem, including aerial image segmentation. Results have shown that deep learning gains much higher accuracy than other methods on many benchmark data sets. In this work, we propose a neural network called NASNet-FCN, which based on Fully Convolutional Network - a frame work for solving semantic segmenta- tion problem and image feature extractor derived from state-of-the-art object recognition network called Neural Search Network Architecture. Our networks are trained and judged by using benchmark dataset from ISPRS Vaihingen challenge. Results show that our methods achieved state-of-the-art accuracy with potential improvements.

Item Type: Conference or Workshop Item (Paper)
Subjects: Information Technology (IT)
Divisions: Faculty of Information Technology (FIT)
Depositing User: Long Trần Quốc
Date Deposited: 14 Dec 2018 08:16
Last Modified: 14 Dec 2018 08:16

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