A Hyperspectral Feature Selection Method for Soil Organic Matter Estimation Based on an Improved Weighted Marine Predators Algorithm

Soil organic matter (SOM) content is a crucial indicator for assessing soil fertility, and serves as a key factor in sustaining a viable agricultural system. With the continuous advancement and improvement of remote sensing technology, hyperspectral imagery has been employed for the monitoring of SOM. While the numerous bands reveal finer details within the spectral features, this also brings information redundancy and noise interference. Currently, the dimensionality reduction methods designed for hyperspectral imagery encounter difficulties in achieving optimal band combinations. As a result, swiftly and accurately capturing the spectral features of SOM becomes a challenging task. In this paper, aiming to address the inefficiency and instability in hyperspectral feature selection, we propose a metaheuristic-based algorithm—the improved weighted marine predators algorithm (IWMPA)—for hyperspectral feature selection. Specifically, we simulated the process of hyperspectral feature selection using the foraging strategy of marine predators. We employed prior weight coefficients and reverse learning operations in the initialization phase to accelerate the convergence of the population, and introduced mutation operations into the phase of development to prevent the occurrence of local optima traps. We employed the IWMPA feature selection method to establish SOM estimation models within the research area of Yitong Manchu Autonomous County in China. The results demonstrated that the hyperspectral features selected using the IWMPA approach yield favorable outcomes in the SOM estimation models. Specifically, in the best-performing regression model of this study, the R
²
on the test set was 0.7225. These experimental results suggest that, in comparison to the existing methods, the proposed IWMPA method is more adept at capturing the spectral features of SOM.