Wang Li, Bi Yang, Wang Wei, Li Junfang
Department of Electronic Engineering, Xi'an Aeronautical Institute, 259 West Second Ring Road, Xi'an, 710077, Shaanxi, China.
Sci Rep. 2024 Aug 2;14(1):17934. doi: 10.1038/s41598-024-68382-y.
Based on double-compressed sampling, a hyperspectral spectral unmixing algorithm (SU_DCS) is proposed, which could directly complete the endmember extraction and abundance estimation. On the basis of the linear mixed model (LMM), we designed spatial and spectral sampling matrices, obtained spatial and spectral measurement data, and constructed a joint unmixing model containing endmember and abundance information. By using operator separation and Lagrangian multiplier algorithm, the endmember matrix, abundance matrix and remixing image can be quickly obtained by matrix operation. The parameters of the unmixing algorithm, including regularization parameter, convergence threshold and spatial sampling rate, are determined using synthetic simulated hyperspectral data. The proposed algorithm is applied to two kinds of real hyperspectral data, with or without ground truth, in order to verify the effectiveness and reliability of the algorithm. Firstly, we provide the performance of the algorithm on real datasets without ground truth. Compared with algorithm VCA_FCLS and algorithm CPPCA_VCA_FCLS, the endmember spectral curve extracted by the proposed SU_DCS is almost consistent with that obtained by VCA_FCLS, and is more smooth than that of obtained by CPPCA_VCA_FCLS. Additionally, the abundance estimation map estimated by the SU_DCS has consistency with the results obtained by VCA_FCLS. Moreover, the proposed SU_DCS has higher peak signal-to-noise ratio (PSNR) for remixing images with higher computational efficiency. Secondly, we provide the performance of the proposed algorithm on four real datasets with ground truth, including dataset Cuprite, dataset Samson, dataset Jasper and dataset Urban. We provide the results of endmember extraction and abundance estimation from the compressed data under different sampling rate conditions. The extracted endmember maintains good consistency with the true spectral curves, and the estimated abundance map can also maintain good spatial consistency with the ground truth. The comparison results with other four comparative algorithms also indicate that the proposed algorithm can obtain relatively accurate endmembers and abundance information from compressed data, the reliability and validity of the proposed algorithm have been proved. In summary, the main innovation of the proposed algorithm is that it can extract endmembers and estimate abundance with high accuracy from a small amount of measurement data.
基于双压缩采样，提出了一种高光谱光谱解混算法（SU_DCS），该算法可直接完成端元提取和丰度估计。在线性混合模型（LMM）的基础上，设计了空间和光谱采样矩阵，获取了空间和光谱测量数据，并构建了一个包含端元和丰度信息的联合解混模型。通过算子分离和拉格朗日乘子算法，可通过矩阵运算快速得到端元矩阵、丰度矩阵和重混图像。利用合成模拟高光谱数据确定了解混算法的参数，包括正则化参数、收敛阈值和空间采样率。将所提算法应用于两种有无地面真值的真实高光谱数据，以验证算法的有效性和可靠性。首先，给出了该算法在无地面真值的真实数据集上的性能。与算法VCA_FCLS和算法CPPCA_VCA_FCLS相比，所提SU_DCS提取的端元光谱曲线与VCA_FCLS获得的光谱曲线几乎一致，且比CPPCA_VCA_FCLS获得的光谱曲线更平滑。此外，SU_DCS估计的丰度估计图与VCA_FCLS获得的结果具有一致性。而且，所提SU_DCS对于重混图像具有更高的峰值信噪比（PSNR），且计算效率更高。其次，给出了所提算法在四个有地面真值的真实数据集上的性能，包括Cuprite数据集、Samson数据集、Jasper数据集和Urban数据集。给出了不同采样率条件下从压缩数据中提取端元和估计丰度的结果。提取的端元与真实光谱曲线保持良好的一致性，估计的丰度图也能与地面真值保持良好的空间一致性。与其他四种对比算法的比较结果也表明，所提算法能够从压缩数据中获得相对准确的端元和丰度信息，证明了所提算法的可靠性和有效性。综上所述，所提算法的主要创新点在于它能够从少量测量数据中高精度地提取端元和估计丰度。
