TY - GEN
T1 - Banana disease detection by fusion of close range hyperspectral image and high-resolution RGB image
AU - Liao, Wenzhi
AU - Ochoa, Daniel
AU - Zhao, Yongqiang
AU - Rugel, Gladys Maria Villegas
AU - Philips, Wilfried
N1 - Publisher Copyright:
© 2018 IEEE.
PY - 2018/10/31
Y1 - 2018/10/31
N2 - Early detection of banana disease can limit the spread of disease, as well as reduce the treatment costs. Current methods focus on either manually interpretation or calculation of spectral indices (e.g., the normalized difference vegetation index). In this paper, we exploit the fusion of close range hyperspectral (HS) image and high-resolution (HR) visible RGB image for potential disease detection in banana leaves. Our approach applies the joint bilateral filter to transfer the textural structures of HR RGB image to low-resolution HS image and obtain an enhanced HS image. Initial experimental results on Musa acuminata (banana) leaf images demonstrate the efficiency of our fusion approach, with significant improvements over either single data source or some conventional methods.
AB - Early detection of banana disease can limit the spread of disease, as well as reduce the treatment costs. Current methods focus on either manually interpretation or calculation of spectral indices (e.g., the normalized difference vegetation index). In this paper, we exploit the fusion of close range hyperspectral (HS) image and high-resolution (HR) visible RGB image for potential disease detection in banana leaves. Our approach applies the joint bilateral filter to transfer the textural structures of HR RGB image to low-resolution HS image and obtain an enhanced HS image. Initial experimental results on Musa acuminata (banana) leaf images demonstrate the efficiency of our fusion approach, with significant improvements over either single data source or some conventional methods.
KW - Banana diseases
KW - Close range hyperspectral image
KW - Data fusion
KW - Morphology
UR - https://www.scopus.com/pages/publications/85064207357
U2 - 10.1109/IGARSS.2018.8519115
DO - 10.1109/IGARSS.2018.8519115
M3 - 会议稿件
AN - SCOPUS:85064207357
T3 - International Geoscience and Remote Sensing Symposium (IGARSS)
SP - 1744
EP - 1747
BT - 2018 IEEE International Geoscience and Remote Sensing Symposium, IGARSS 2018 - Proceedings
PB - Institute of Electrical and Electronics Engineers Inc.
T2 - 38th Annual IEEE International Geoscience and Remote Sensing Symposium, IGARSS 2018
Y2 - 22 July 2018 through 27 July 2018
ER -