Infrared and Laser Engineering, Volume. 46, Issue 6, 638001(2017)
Nonlinear unmixing using backtracking optimization for hyperspectral images
A postnonlinear unmixing algorithm was presented for hyperspectral images based on backtracking optimization to improve the unmixing accuracy. On the basis of the postnonlinear mixing model, the reconstruction error between the observed images and the reconstructed images was used as the objective function, backtracking search optimization algorithm was used to search in the solution space to obtain the optimal solution which minimize the objective function. In the search process, the boundary control mechanism of the backtracking search optimization algorithm effectively ensured the constraint condition in the hyperspectral image unmixing, and then the abundance and nonlinear parameters can be estimated accurately. The experiments conducted for both synthetic images and real remote sensing images show that the algorithm proposed is provided with excellent unmixing performance. The unmixing accuracy achieved is significantly better than the state-of-the-art nonlinear hyperspectral images unmixing algorithms.(15JCYBJC17100)
Get Citation
Copy Citation Text
Chen Lei, Gan Shizhong, Sun Qian. Nonlinear unmixing using backtracking optimization for hyperspectral images[J]. Infrared and Laser Engineering, 2017, 46(6): 638001
Category: 高光谱成像遥感
Received: Oct. 10, 2016
Accepted: Nov. 20, 2016
Published Online: Jul. 10, 2017
The Author Email: