Supervised hyperspectral classification is one of the important applications in hyperspectral remote sensing. Currently, the enormous complexity of the hyperspectral data makes the hyperspectral feature extraction a very challenging job. In this letter, we propose a feature extraction approach by developing a two-dimensional compact variational mode decomposition (2D-C-VMD) for the supervised hyperspectral images (HSIs) classification. Specifically, the HSIs is first decomposed into a series of mode components using 2D-C-VMD. Then for the subsequent supervised classification, a collection of feature samples are created by elaborating the collected pattern components. Finally, a support vector machine is adopted as the classifier to validate the aforementioned feature extraction algorithm. According to experimental findings, this approach obtained a high level of classification accuracy with a compact feature extraction process and the time for feature extraction is also shortened.

Soil organic carbon (SOC) content is a critical parameter for evaluating soil health. However, high redundancy and invalid information in soil hyperspectral data can reduce the accuracy and stability of SOC prediction models. This study developed a global partial least squares regression (PLSR) model and a local PLSR model for agricultural soils in the LUCAS 2015 database. Some variable selection methods were combined with the regression models and their effects on prediction accuracy were explored. In addition, when the genetic algorithm is utilized for spectral feature selection, we obtained a more representative spectral subset through a novel coding approach. The results illustrated that the best SOC estimation accuracy was achieved by the local PLSR combined with a coding-improved genetic algorithm (GA), with R ² of 0.71, RMSEP of 5.7 g kg ^-1 , and RPD of 1.87. This study demonstrates that appropriate spectral band selection only slightly enhances the model performance of both global and local regressions, as PLSR models using the full spectrum show similar performance. Local PLSR models consistently outperform global ones using full spectrum or variable selection algorithms.

In order to solve the problem of noise interference in hyperspectral image (HSI) feature extraction, an improved two-dimensional compact variational mode decomposition (2D-C-VMD) method is proposed to extract the spatial dimension feature information of HSI. By smoothing the low-frequency mode components obtained from the first decomposition of 2D-C-VMD, the method is called L-2D-C-VMD, which can further eliminate the adverse effects of spatial domain noise on the classification of different ground objects to a certain extent. Through simulation test analysis on the public datasets Indian Pine and Pavia University. When the Indian Pine training sample takes 4 %, the OA value of the proposed classification method can be increased by about 2 %. When the Pavia University training sample is taken at 0.5 %, the OA value of the proposed classification method can be increased by nearly 3 %, and the proposed method has better competitiveness with fewer training samples.