Multicollinearity-Aware Parameter-Free Strategy for Hyperspectral Band Selection: A Dependence Measures-Based Approach

Hyperspectral bands offer rich spectral and spatial information; however, their high dimensionality poses challenges for efficient processing. Band selection (BS) methods aim to extract a smaller subset of bands to reduce spectral redundancy. Existing approaches, such as ranking-based, clustering-based, and iterative methods, often suffer from issues like sensitivity to initialization, parameter tuning, and high computational cost. This work introduces a BS strategy integrating three dependence measures: Average Band Correlation (ABC) and Mutual Information (MI), and Variance Inflation Factor (VIF). ABC quantifies linear correlations between spectral bands, while MI measures uncertainty reduction relative to ground truth labels. To address multicollinearity and reduce the search space, the approach first applies a VIF-based pre-selection of spectral bands. Subsequently, a clustering algorithm is used to identify the optimal subset of bands based on the ABC and MI values. Unlike previous methods, this approach is completely parameter-free for hyperspectral band selection, eliminating the need for optimal parameter estimation. The proposed method is evaluated on four standard benchmark datasets: WHU-Hi-LongKou, Pavia University, Salinas, and Oil Spill datasets, and is compared to existing state-of-the-art approaches. There is significant overlap between the bands identified by our proposed method and those selected by other methods, indicating that our approach effectively captures the most relevant spectral features. Further, support vector machine (SVM) classification validates that VIF-driven pruning enhances classification by minimizing multicollinearity. Ablation studies confirm that combining ABC with MI yields robust, discriminative band subsets.

Improving Power Plant CO2 Emission Estimation with Deep Learning and Satellite/Simulated Data

CO2 emissions from power plants, as significant super emitters, contribute substantially to global warming. Accurate quantification of these emissions is crucial for effective climate mitigation strategies. While satellite-based plume inversion offers a promising approach, challenges arise from data limitations and the complexity of atmospheric conditions. This study addresses these challenges by (a) expanding the available dataset through the integration of NO2 data from Sentinel-5P, generating continuous XCO2 maps, and incorporating real satellite observations from OCO-2/3 for over 71 power plants in data-scarce regions; and (b) employing a customized U-Net model capable of handling diverse spatio-temporal resolutions for emission rate estimation. Our results demonstrate significant improvements in emission rate accuracy compared to previous methods. By leveraging this enhanced approach, we can enable near real-time, precise quantification of major CO2 emission sources, supporting environmental protection initiatives and informing regulatory frameworks.

Correlation-Based Band Selection for Hyperspectral Image Classification

Hyperspectral images offer extensive spectral information about ground objects across multiple spectral bands. However, the large volume of data can pose challenges during processing. Typically, adjacent bands in hyperspectral data are highly correlated, leading to the use of only a few selected bands for various applications. In this work, we present a correlation-based band selection approach for hyperspectral image classification. Our approach calculates the average correlation between bands using correlation coefficients to identify the relationships among different bands. Afterward, we select a subset of bands by analyzing the average correlation and applying a threshold-based method. This allows us to isolate and retain bands that exhibit lower inter-band dependencies, ensuring that the selected bands provide diverse and non-redundant information. We evaluate our proposed approach on two standard benchmark datasets: Pavia University (PA) and Salinas Valley (SA), focusing on image classification tasks. The experimental results demonstrate that our method performs competitively with other standard band selection approaches.