Abstract
Recently, the multiobjective evolutionary algorithms (MOEAs) have been designed to cope with the sparse unmixing problem. Due to the excellent performance of MOEAs in solving the NP hard optimization problems, they have also achieved good results for the sparse unmixing problems. However, most of these MOEA-based methods only deal with a single pixel for unmixing and are subjected to low efficiency and are time-consuming. In fact, sparse unmixing can naturally be seen as a multitasking problem when the hyperspectral imagery is clustered into several homogeneous regions, so that evolutionary multitasking can be employed to take advantage of the implicit parallelism from different regions. In this paper, a novel evolutionary multitasking multipopulation particle swarm optimization framework is proposed to solve the hyperspectral sparse unmixing problem. First, we resort to evolutionary multitasking optimization to cluster the hyperspectral image into multiple homogeneous regions, and directly process the entire spectral matrix in multiple regions to avoid dimensional disasters. In addition, we design a novel multipopulation particle swarm optimization method for major evolutionary exploration. Furthermore, an intra-task and inter-task transfer and a local exploration strategy are designed for balancing the exchange of useful information in the multitasking evolutionary process. Experimental results on two benchmark hyperspectral datasets demonstrate the effectiveness of the proposed method compared with the state-of-the-art sparse unmixing algorithms.
Highlights
With the progress of remote sensing technology, hyperspectral imagery, which can obtain hundreds of sequential spectrum bands, has been widely applied in both civilian and military scenarios, for example, land-cover classification [1–3], environmental monitoring [4–6] and target detection [7,8], and so forth
In order to reflect the superiority of our proposed algorithm, evolutionary multitasking multipopulation particle swarm optimization (EMMPSO) compares with the state-of-art algorithms, including SUnSAL, CLSUnSAL, two-phase multiobjective sparse unmixing (Tp-MOSU) and evolutionary multitasking sparse reconstruction (MTSR)
The experimental results on two datasets have proved that our proposed EMMPSO is able to achieve a competitive performance by evolutionary multitasking and local exploration strategy
Summary
With the progress of remote sensing technology, hyperspectral imagery, which can obtain hundreds of sequential spectrum bands, has been widely applied in both civilian and military scenarios, for example, land-cover classification [1–3], environmental monitoring [4–6] and target detection [7,8], and so forth. In some recent studies [22–24], a hyperspectral image is clustered into multiple homogeneous regions based on the assumption that the probability of the active endmember set in the homogeneous region is likely to be the same, which reduces the complexity of unmixing, and further enhances the spatial correlation of pixels in the same category This coincides with the idea of evolutionary multitasking framework emerging in recent years. The multiobjective optimization is applied to each task simultaneously to obtain a compromise between the reconstruction error and the endmember sparsity It is different from the traditional MOEA-based algorithms that EMMPSO can process the entire matrix due to the decomposition strategy of evolutionary multitasking, aiming at pixel-based unmixing only. A novel evolutionary multitasking multipopulation particle swarm optimization framework is proposed to solve the sparse unmixing problem.
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