TY - GEN
T1 - The election of spectrum bands in hyper-spectral image classification
AU - Yu, Yi
AU - Li, Yi Fan
AU - Li, Jun Bao
AU - Pan, Jeng Shyang
AU - Zheng, Wei Min
N1 - Publisher Copyright:
© Springer International Publishing AG 2017.
PY - 2017
Y1 - 2017
N2 - This paper present a framework for the pre-process of hyperspectral image classification, it seems to be proved an important and well method in this kind of field. We construct a new calculating method in preprocess which use for reference of digital image process, such us morphology method and relations of position. Because of the data of the spectrum bands of hyper-spectral has some degree of redundancy. Some similar bands have the similar information or even same information, so it would waste many counter resource. On the other hands, redundancy means when you matching every model, you will easily addicted to Hughes phenomenon. So bands electing before we use classification model begin to classify directly is very important. It not only can decrease time-cost also improve the accuracy and degree of stability. Our experiment proved our idea practice in real classification get a gorgeous effect, it shows in different complex classification surroundings, our method also perform great.
AB - This paper present a framework for the pre-process of hyperspectral image classification, it seems to be proved an important and well method in this kind of field. We construct a new calculating method in preprocess which use for reference of digital image process, such us morphology method and relations of position. Because of the data of the spectrum bands of hyper-spectral has some degree of redundancy. Some similar bands have the similar information or even same information, so it would waste many counter resource. On the other hands, redundancy means when you matching every model, you will easily addicted to Hughes phenomenon. So bands electing before we use classification model begin to classify directly is very important. It not only can decrease time-cost also improve the accuracy and degree of stability. Our experiment proved our idea practice in real classification get a gorgeous effect, it shows in different complex classification surroundings, our method also perform great.
KW - Hyper-spectral image classification
KW - Integration of image data by use morphology method and relations of position
KW - Pre-process in hyper-spectral
KW - SVM kernel classification model
KW - Spectrum bands election
UR - https://www.scopus.com/pages/publications/85005996209
U2 - 10.1007/978-3-319-50212-0_1
DO - 10.1007/978-3-319-50212-0_1
M3 - 会议稿件
AN - SCOPUS:85005996209
SN - 9783319502113
T3 - Smart Innovation, Systems and Technologies
SP - 3
EP - 10
BT - Advances in Intelligent Information Hiding and Multimedia Signal Processing - Proceeding of the 12th International Conference on Intelligent Information Hiding and Multimedia Signal Processing, 2016
A2 - Pan, Jeng-Shyang
A2 - Tsai, Pei-Wei
A2 - Huang, Hsiang-Cheh
PB - Springer Science and Business Media Deutschland GmbH
T2 - 12th International Conference on Intelligent Information Hiding and Multimedia Signal Processing, IIH-MSP 2016
Y2 - 21 November 2016 through 23 November 2016
ER -