Alshahrani Atheer Abdullah, Bchir Ouiem, Ben Ismail Mohamed Maher
Computer Science Department, Applied College, King Khalid University, Abha 61421, Saudi Arabia.
Department of Computer Science, College of Computer and Information Sciences, King Saud University, Riyadh 11633, Saudi Arabia.
Sensors (Basel). 2025 Apr 19;25(8):2592. doi: 10.3390/s25082592.
Hyperspectral unmixing plays a fundamental role in mining meaningful information from hyperspectral data. It promotes advancements in various scientific, environmental, and industrial applications by extracting meaningful information from hyperspectral data. However, it is still hindered by several challenges, including accurately identifying the number of endmembers in a hyperspectral image, extracting the endmembers, and estimating their abundance fractions. This research addresses these challenges by employing a convolutional-neural-network-based autoencoder that leverages both the spatial and spectral information present in the hyperspectral image. Additionally, a self-learning module utilizing a fuzzy clustering algorithm is designed to determine the number of endmembers. A novel approach is also introduced that estimates the abundances of the endmembers from the autoencoder and the clustering output. Real datasets and relevant performance metrics were used to validate and evaluate the performance of the proposed method. The results demonstrate that our approach outperforms related methods, achieving improvements of 47% in Spectral Angle Distance (SAD) and 42% in root-mean-square error (RMSE).
高光谱解混在从高光谱数据中挖掘有意义的信息方面起着基础性作用。它通过从高光谱数据中提取有意义的信息，推动了各种科学、环境和工业应用的发展。然而，它仍然受到几个挑战的阻碍，包括准确识别高光谱图像中端元的数量、提取端元以及估计它们的丰度分数。本研究通过采用基于卷积神经网络的自动编码器来解决这些挑战，该自动编码器利用了高光谱图像中存在的空间和光谱信息。此外，设计了一个利用模糊聚类算法的自学习模块来确定端元的数量。还引入了一种新颖的方法，从自动编码器和聚类输出中估计端元的丰度。使用真实数据集和相关性能指标来验证和评估所提方法的性能。结果表明，我们的方法优于相关方法，在光谱角距离（SAD）上提高了47%，在均方根误差（RMSE）上提高了42%。
