원문정보
초록
영어
The study on classification methods of hyperspectral image is a focal growing area in remote sensing applications because the wide spectral range, providing a very high spectral resolution, allows the detection and classification surfaces and chemical elements of the observed image. Semi-supervised learning method which takes a large number of unlabeled samples and minority labeled samples, improving classification and predicting the accuracy effectively have been a new research direction. In this paper we proposed a new semi-supervised classification method of hyperspectral image based on combining Renyi entropy and multinomial logistic regression algorithm. The multinomial logistic regression was performed to describe a direct relationship between the selected sample as and their category. A lot of unlabeled samples are constantly added to the sample data using Renyi entropy algorithm. The test analysis of image classification in test area showed the advantages of classification method based on combining Renyi entropy and multinomial logistic regression algorithm for hyperspectral remote sensing image.
목차
1. Introduction
2. Materials and Methods
2.1. Problem Description
2.2. The Principle of Multinomial Logistic Regression Algorithm
2.3. Selected Unlabeled Samples using Renyi Entropy Algorithm
2.4. The Process of Algorithm
3. The Study Area and Validation Images
4. Results
4.1. Classification Accuracy Comparison between Different Label Selection Algorithms
4.2. Classification Accuracy Comparison for Different Classifier Algorithms
5. Conclusions and Future Work
Acknowledgements
References