In recent years, the spectral analysis has been one of the most important research areas in remote sensing, which has received different traditional solutions. Most of these methods have several special conditions to work well. However, they suffer from the problems such as sticking in local optimum and having sensibility to parameters initialization. Although the Evolutionary Computation (EC) techniques may also have such deficiencies, recently their combination with the traditional methods has been used increasingly to overcome the defects. Some other methods use EC exclusively to solve the problems. In the new scheme, the spectral analysis problem is modeled as an optimization problem demonstrating the EC techniques lead to optimum solutions. In this paper, we address the methods using the EC to solve the spectral analysis problem. Having introduce the EC technique briefly in each section of this paper, we explain methods use EC. For each method, the assumptions and limitations are discussed and the various components of the EC method such as individual representation, evolutionary operators and fitness function are assessed.
Fayyazi, H., Dehghani, H., & Hosseini, M. (2021). A Survey of Evolutionary Computations Usage in spectral Analysis of hyperspectral images. Soft Computing Journal, 1(2), 46-59.
MLA
Hossein Fayyazi; Hamid Dehghani; Mojtaba Hosseini. "A Survey of Evolutionary Computations Usage in spectral Analysis of hyperspectral images". Soft Computing Journal, 1, 2, 2021, 46-59.
HARVARD
Fayyazi, H., Dehghani, H., Hosseini, M. (2021). 'A Survey of Evolutionary Computations Usage in spectral Analysis of hyperspectral images', Soft Computing Journal, 1(2), pp. 46-59.
VANCOUVER
Fayyazi, H., Dehghani, H., Hosseini, M. A Survey of Evolutionary Computations Usage in spectral Analysis of hyperspectral images. Soft Computing Journal, 2021; 1(2): 46-59.
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