Article | 01-December-2016
Hyperspectral data has rich spectrum information, strong correlation between bands and high data redundancy. Feature band extraction of hyperspectral data is a prerequisite and an important basis for the subsequent study of classification and target recognition. Deep belief network is a kind of deep learning model, the paper proposed a deep belief network to realize the characteristics band extraction of hyperspectral data, and use the advantages of unsupervised and supervised learning of deep
Jiang Xinhua,
Xue Heru,
Zhang Lina,
Zhou Yanqing
International Journal on Smart Sensing and Intelligent Systems, Volume 9 , ISSUE 4, 1991–2009
Article | 01-December-2016
This paper uses the combination of information and class separability as a new evaluation criterion for hyperspectral imagery. Moreover, the correlation between bands is used as a constraint condition. The differential evolution algorithm is adopted during the search of optimal band combination. In addition, the game theory is introduced into the band selection to coordinate the potential conflict of searching the optimal band combination using information and class separability these two
Aiye Shi,
Hongmin Gao,
Zhenyu He,
Min Li,
Lizhong Xu
International Journal on Smart Sensing and Intelligent Systems, Volume 9 , ISSUE 4, 1971–1990
Research Article | 01-September-2017
A new technique Multiple-feature-based adaptive sparse representation (MFASR) has been demonstrated for Hyperspectral Images (HSI’s) classification. This method involves mainly in four steps at the various stages. The spectral and spatial information reflected from the original Hyperspectral Images with four various features. A shape adaptive (SA) spatial region is obtained in each pixel region at the second step. The algorithm namely sparse representation has applied to get the coefficients of
S. Srinivasan,
Dr. K. Rajakumar
International Journal on Smart Sensing and Intelligent Systems, Volume 10 , ISSUE 3, 567–593
Research Article | 12-December-2017
R.R. Pullanagari,
I. Yule,
W. King,
D. Dalley,
R. Dynes
International Journal on Smart Sensing and Intelligent Systems, Volume 4 , ISSUE 1, 125–137
Research Article | 15-February-2020
Luiz Carlos Paiva Gouveia,
Bhaskar Choubey
International Journal on Smart Sensing and Intelligent Systems, Volume 7 , ISSUE 5, 1–6