VNU-UET Repository: No conditions. Results ordered -Date Deposited. 2024-03-29T12:31:03ZEPrintshttp://eprints.uet.vnu.edu.vn/images/sitelogo.pnghttps://eprints.uet.vnu.edu.vn/eprints/2018-12-14T08:16:21Z2018-12-14T08:16:21Zhttp://eprints.uet.vnu.edu.vn/eprints/id/eprint/3264This item is in the repository with the URL: http://eprints.uet.vnu.edu.vn/eprints/id/eprint/32642018-12-14T08:16:21ZAerial Image Semantic Segmentation Using Neural Search Network ArchitectureIn 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.Duc Thinh Buithinh.ducbui@gmail.comDo Van Nguyenngdovan@gmail.comThi Thu Thuy Nguyenmyngthuy@gmail.comQuoc Long Trantqlong@vnu.edu.vnThanh Ha Leltha@vnu.edu.vn