TIIS (한국인터넷정보학회)
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| 한글제목(Korean Title) |
Convolutional Neural Network with Expert Knowledge for Hyperspectral Remote Sensing Imagery Classification |
| 영문제목(English Title) |
Convolutional Neural Network with Expert Knowledge for Hyperspectral Remote Sensing Imagery Classification |
| 저자(Author) |
Chunming Wu
Meng Wang
Lang Gao
Weijing Song
Tian Tian
Kim-Kwang Raymond Choo
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| 원문수록처(Citation) |
VOL 13 NO. 08 PP. 3917 ~ 3941 (2019. 08) |
한글내용
(Korean Abstract) |
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영문내용
(English Abstract) |
The recent interest in artificial intelligence and machine learning has partly contributed to an interest in the use of such approaches for hyperspectral remote sensing (HRS) imagery classification, as evidenced by the increasing number of deep framework with deep convolutional neural networks (CNN) structures proposed in the literature. In these approaches, the assumption of obtaining high quality deep features by using CNN is not always easy and efficient because of the complex data distribution and the limited sample size. In this paper, conventional handcrafted learning-based multi features based on expert knowledge are introduced as the input of a special designed CNN to improve the pixel description and classification performance of HRS imagery. The introduction of these handcrafted features can reduce the complexity of the original HRS data and reduce the sample requirements by eliminating redundant information and improving the starting point of deep feature training. It also provides some concise and effective features that are not readily available from direct training with CNN. Evaluations using three public HRS datasets demonstrate the utility of our proposed method in HRS classification.
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| 키워드(Keyword) |
Hyperspectral imagery classification
convolutional neural network
principal component analysis
gray-level co-occurrence matrix
differential Mathematical morphology
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| 파일첨부 |
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