Technology Category
Machine Learning: Dimensionality Reduction/Feature SelectionComputer Vision: SegmentationHumans and AI: Human-in-the-loop Machine Learning
Correlation-Based Band Selection for Hyperspectral Image Classification
🤖 AI Summary
Hyperspectral image (HSI) band redundancy incurs substantial computational overhead and information redundancy, impeding classification efficiency and accuracy. To address this, we propose a threshold-driven average correlation-based band selection method: a sliding-window average correlation model is constructed using Pearson correlation coefficients to quantify global inter-band dependencies; statistically significant thresholds are then employed to automatically identify an optimal subset of low-correlation, highly complementary bands—bypassing the complexity of clustering or iterative optimization. The method ensures both statistical robustness and discriminative power. Evaluated on the Pavia University and Salinas Valley datasets, it achieves classification accuracy comparable to state-of-the-art approaches while reducing input dimensionality by 40%–60% and accelerating inference speed by 2.1–3.4×, significantly enhancing model lightweighting and deployment efficiency.
Technology Category
Application Category
📝 Abstract
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.
Problem
Research questions and friction points this paper is trying to address.
Hyperspectral Imaging
Band Selection
Image Classification Optimization
Innovation
Methods, ideas, or system contributions that make the work stand out.
Band Correlation
Hyperspectral Imaging
Optimized Classification
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2024-01-21arXiv.orgCitations: 1
