Multiobjective endmember extraction for hyperspectral image

Endmember extraction (EE) is one of the most important issues in hyperspectral mixture analysis, and it is also one of the most challenging tasks due to the intrinsic complexity of remote sensing images and the lack of priori knowledge. In recent years, a number of EE methods have been developed, an...

Celý popis

Uloženo v:
Podrobná bibliografie
Vydáno v:IEEE International Geoscience and Remote Sensing Symposium proceedings s. 1161 - 1164
Hlavní autoři: Rong Liu, Bo Du, Liangpei Zhang
Médium: Konferenční příspěvek
Jazyk:angličtina
Vydáno: IEEE 01.07.2017
Témata:
Complexity theory
endmember extraction
Hyperspectral imaging
Hyperspectral remote sensing
Linear programming
multi-objective
Optimization
Particle swarm optimization
ISSN:2153-7003
On-line přístup:Získat plný text
Tagy: Přidat tag
Žádné tagy, Buďte první, kdo vytvoří štítek k tomuto záznamu!
Popis
Shrnutí:Endmember extraction (EE) is one of the most important issues in hyperspectral mixture analysis, and it is also one of the most challenging tasks due to the intrinsic complexity of remote sensing images and the lack of priori knowledge. In recent years, a number of EE methods have been developed, and several different optimization objectives have been proposed from different perspectives. In all of these methods, there is only one objective function to be optimized, which represents a specific characteristic of the remote sensing image. However, one single-objective function may not provide satisfactory results because of the complexity of remote sensing images. In this paper, a multiobjective discrete particle swarm optimization algorithm (MODPSO) is utilized to tackle the problem of EE, where two objective functions, namely, volume maximization and root-mean-square error (RMSE) minimization are simultaneously optimized. The result set of Pareto-optimal solutions contains a number of non-dominated solutions, from which the user can judge relatively and pick up the most promising one according to the problem requirements. Experiments on two real hyperspectral images were conducted to evaluate the proposed MODPSO algorithm, which confirmed the effectiveness of the proposed algorithm.
ISSN:2153-7003
DOI:10.1109/IGARSS.2017.8127163