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  • In Jour Smart Sensing And Intelligent Systems

 

Article | 01-December-2016

HYPERSPECTRAL DATA FEATURE EXTRACTION USING DEEP BELIEF NETWORK

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

A HYPERSPECTRAL BAND SELECTION BASED ON GAME THEORY AND DIFFERENTIAL EVOLUTION ALGORITHM

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 REVIEW ON MULTIPLE-FEATURE-BASED ADAPTIVE SPARSE REPRESENTATION (MFASR) AND OTHER CLASSIFICATION TYPES

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

THE USE OF OPTICAL SENSORS TO ESTIMATE PASTURE QUALITY

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

On Evolution of CMOS Image Sensors

Luiz Carlos Paiva Gouveia, Bhaskar Choubey

International Journal on Smart Sensing and Intelligent Systems, Volume 7 , ISSUE 5, 1–6

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