학술논문

Fusarium Wilt Inspection for Phalaenopsis Using Uniform Interval Hyperspectral Band Selection Techniques
Document Type
Conference
Source
IGARSS 2020 - 2020 IEEE International Geoscience and Remote Sensing Symposium Geoscience and Remote Sensing Symposium, IGARSS 2020 - 2020 IEEE International. :2831-2834 Sep, 2020
Subject
Aerospace
Computing and Processing
Geoscience
Photonics and Electrooptics
Signal Processing and Analysis
Hyperspectral imaging
Support vector machines
Hybrid fiber coaxial cables
Feature extraction
Diseases
Correlation
Signal to noise ratio
Spectral angle mapper
Constrain energy minimization
Virtual dimensionality
Band selection
Band prioritization
Band de-correlation
Spectral information divergence
Support vector machine
Language
ISSN
2153-7003
Abstract
In this paper, we propose a method to inspect the quality of Phalaenopsis by using hyperspectral imaging techniques. Phalaenopsis is easy to get infected with Fusarium wilt. We use the k-means clustering method to find out that the reflection spectrum of Phalaenopsis stem changes. The methods of the Spectral Angle Mapper (SAM) and Constrained Energy Minimization (CEM) are then used to find the area of the infected area. The Harsanyi, Farrand and Chang (HFC) methods and virtual dimensions (VD) are used to estimate the amount of spectrum required for band selection (BS). Band priority (BP) is used to calculate the priority of each band, and band de-correlation (BD) will remove band data with high correlation with each other. Then use the support vector machine (SVM) to detect Phalaenopsis wilt. The detection accuracy of VNIR and SWIR is 0.81 and 0.86, respectively, with band selection.