TY  - CONF
ID  - SisLab3264
UR  - https://eprints.uet.vnu.edu.vn/eprints/id/eprint/3264/
A1  - Bui, Duc Thinh
A1  - Nguyen, Do Van
A1  - Nguyen, Thi Thu Thuy
A1  - Tran, Quoc Long
A1  - Le, Thanh Ha
Y1  - 2018/11/19/
N2  - 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.
TI  - Aerial Image Semantic Segmentation Using Neural Search Network Architecture
M2  - Hanoi, Vietnam
AV  - public
T2  - (MIWAI 2018) 12th Multi-disciplinary International Conference on Artificial Intelligence
ER  -