A Deep Learning-Based Hyperspectral Object Classification Approach via Imbalanced Training Samples Handling

Abstract

Object classification in hyperspectral images involves accurately categorizing objects based on their spectral characteristics. However, the high dimensionality of hyperspectral data and class imbalance pose significant challenges to object classification performance. To address these challenges, we propose a framework that incorporates dimensionality reduction and re-sampling as preprocessing steps for a deep learning model. Our framework employs a novel subgroup-based dimensionality reduction technique to extract and select the most informative features with minimal redundancy. Additionally, the data are resampled to achieve class balance across all categories. The reduced and balanced data are then processed through a hybrid CNN model, which combines a 3D learning block and a 2D learning block to extract spectral–spatial features and achieve satisfactory classification accuracy. By adopting this hybrid approach, we simplify the model while improving performance in the presence of noise and limited sample size. We evaluated our proposed model on the Salinas scene, Pavia University, and Kennedy Space Center benchmark hyperspectral datasets, comparing it to state-of-the-art methods. Our object classification technique achieves highly promising results, with overall accuracies of 99.98%, 99.94%, and 99.46% on the three datasets, respectively. This proposed approach offers a compelling solution to overcome the challenges of high dimensionality and class imbalance in hyperspectral object classification.

Spectrally Segmented-Enhanced Neural Network for Precise Land Cover Object Classification in Hyperspectral Imagery

“…Conversely, band selection algorithms, while effective at selecting informative band subsets directly from the original band space of HSIs, often struggle to capture the intricate spectral-spatial correlations inherent in HSI data. Despite the application of popular feature selection methods like chi-squared, select K best, and mutual information feature selection [18,19], their efficacy for HSI classification may be limited by the inherent challenges in capturing the nuanced spectral and spatial properties unique to HSI datasets.…”

A Framework for Fine-Grained Land-Cover Classification Using 10 m Sentinel-2 Images

“…Existing research has attempted to rebalance sample quantities through re-sampling and re-weighting [35]. However, these methods have specific shortcomings in specific use cases, such as the fact that under-sampling can lead to the loss of essential feature information, while over-sampling can result in overfitting for minority class data [36]. When dealing with multi-classification tasks, the re-weighting method increases the complexity of determining the appropriate weight for each category due to the potential correlation between different categories [37].…”