TY - GEN
T1 - A multispectral imaging and semantic computing system for agricultural monitoring and analysis
AU - Wijitdechakul, Jinmika
AU - Kiyoki, Yasushi
AU - Sasaki, Shiori
AU - Koopipat, Chawan
N1 - Publisher Copyright:
© 2017 The authors and IOS Press.
Copyright:
Copyright 2017 Elsevier B.V., All rights reserved.
PY - 2017
Y1 - 2017
N2 - Multispectral image becomes widely used for environmental analysis to detect an object or phenomena that human eyes cannot capture. One of the main type of images acquired by remote sensing such as satellite or aircraft for earth observation. This paper presents a multispectral analysis for aerial images that captured by dual cameras (visible and infrared camera), which are mounted on an unmanned autonomous vehicle (UAV) or Drone. In our experiments, four spectral bands (three visible and one infrared band) were imaged, processed and analyzed to detect agricultural area and measure the health of vegetation. To interpret environmental phenomena and realize an environmental analysis, this study applies semantic analysis by creating a multispectral semantic image space, combined with three numerical indicators (the normalized difference vegetation index (NDVI), the normalized difference water index (NDWI) and the soil adjusted vegetation index (SAVI)) that can be used to analyze plant health, photosynthetic activity and detect environmental object to determine an agricultural area. This paper also proposed the concept of multi-spectrum semantic-image space for agricultural monitoring by defining the correlation meaning from multi-dimensional parameters which related to agricultural analysis to realize and explain agriculture conditions. This paper presents the experimental study on a rice field, a cornfield, a salt farm and a coconut farm in Thailand.
AB - Multispectral image becomes widely used for environmental analysis to detect an object or phenomena that human eyes cannot capture. One of the main type of images acquired by remote sensing such as satellite or aircraft for earth observation. This paper presents a multispectral analysis for aerial images that captured by dual cameras (visible and infrared camera), which are mounted on an unmanned autonomous vehicle (UAV) or Drone. In our experiments, four spectral bands (three visible and one infrared band) were imaged, processed and analyzed to detect agricultural area and measure the health of vegetation. To interpret environmental phenomena and realize an environmental analysis, this study applies semantic analysis by creating a multispectral semantic image space, combined with three numerical indicators (the normalized difference vegetation index (NDVI), the normalized difference water index (NDWI) and the soil adjusted vegetation index (SAVI)) that can be used to analyze plant health, photosynthetic activity and detect environmental object to determine an agricultural area. This paper also proposed the concept of multi-spectrum semantic-image space for agricultural monitoring by defining the correlation meaning from multi-dimensional parameters which related to agricultural analysis to realize and explain agriculture conditions. This paper presents the experimental study on a rice field, a cornfield, a salt farm and a coconut farm in Thailand.
KW - Multispectral image
KW - agricultural
KW - semantic analysis
UR - http://www.scopus.com/inward/record.url?scp=85002836932&partnerID=8YFLogxK
UR - http://www.scopus.com/inward/citedby.url?scp=85002836932&partnerID=8YFLogxK
U2 - 10.3233/978-1-61499-720-7-314
DO - 10.3233/978-1-61499-720-7-314
M3 - Conference contribution
AN - SCOPUS:85002836932
T3 - Frontiers in Artificial Intelligence and Applications
SP - 314
EP - 333
BT - Information Modelling and Knowledge Bases XXVIII
A2 - Thalheim, Bernhard
A2 - Jaakkola, Hannu
A2 - Kiyoki, Yasushi
A2 - Yoshida, Naofumi
PB - IOS Press
ER -
