Journal of Computer and Communications, 2019, 7, 53-64
http://www.scirp.org/journal/jcc
ISSN Online: 2327-5227
ISSN Print: 2327-5219
DOI:
10.4236/jcc.2019.77006 Jul. 10, 2019 53 Journal of Computer and Communications
Semantic Segmentation Based Remote Sensing
Data Fusion on Crops Detection
Jose Pena
1,2
, Yumin Tan
1
, Wuttichai Boonpook
1
1
School of Transportation Science and Engineering, Beihang University, Beijing, China
2
Venezuela Space Agency (ABAE), Caracas, Venezuela
Abstract
Data fusion is usually an important process in multi-
sensor remotely sensed
imagery integration environments with the aim of enriching features lacking
in the sensors involved in the fusion process. This technique has attracted
much interest in many researches especially in the field of agriculture. On
the
other hand, deep learning (DL) based semantic segmentation shows high
performance in remote sensing classification, and it requires large datasets in
a supervised learning way. In the paper, a method of fusing multi-source re-
mote sensing images with c
onvolution neural networks (CNN) for semantic
segmentation is proposed and applied to identify crops. Venezuelan Remote
Sensing Satellite-2 (VRSS-2) and the high-
resolution of Google Earth (GE)
imageries have been used and more than 1000 sample sets have b
een collected
for supervised learning process. The experiment results show that the crops
extraction with an average overall accuracy more than 93% has been ob-
tained, which demonstrates that data fusion combined with DL is highly
feasible to crops extracti
on from satellite images and GE imagery, and it
shows that deep learning techniques can serve as an invaluable tools for larger
remote sensing data fusion frameworks, specifically for the applications in
precision farming.
Keywords
Data Fusion, Crops Detection, Semantic Segmentation, VRSS-2
1. Introduction
At present RS technology has received great attention in the agriculture com-
munity due to its ability to provide periodic and regional information for crop
monitoring and thematic mapping [1] [2]. Modern RS to identify any features
How to cite this paper:
Pena, J., Tan, Y.M
.
and
Boonpook, W. (2019) Semantic Seg-
mentation Based Remote Sensing Data
Fusion on Crops Detection
.
Journal of
Computer and
Communications
,
7
, 53-64.
https://doi.org/10.4236/jcc.2019.77006
Received:
May 20, 2019
Accepted:
July 7, 2019
Published:
July 10, 2019
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