relation: https://eprints.uet.vnu.edu.vn/eprints/id/eprint/3264/
title: Aerial Image Semantic Segmentation Using Neural Search Network Architecture
creator: Bui, Duc Thinh
creator: Nguyen, Do Van
creator: Nguyen, Thi Thu Thuy
creator: Tran, Quoc Long
creator: Le, Thanh Ha
subject: Information Technology (IT)
description: 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.
date: 2018-11-19
type: Conference or Workshop Item
type: PeerReviewed
format: application/pdf
language: en
identifier: https://eprints.uet.vnu.edu.vn/eprints/id/eprint/3264/1/Thinh_miwai_0703_paper.pdf
identifier:   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.