Transparent and Collaborative AI for Segmentation-Based Hyperspectral Image Classification

Hyperspectral imaging has become a cornerstone technology across applications such as remote sensing, precision agriculture, environmental monitoring, and intelligent surveillance, due to its ability to capture rich and discriminative spectral information. Although significant advances have been made in machine learning– and deep learning–based classification techniques, their deployment in real-world settings remains constrained. Many existing approaches exhibit limited interpretability, high computational complexity, and little to no integration of human expertise. Moreover, data-driven models often struggle in scenarios with scarce labeled samples and fail to exploit valuable domain knowledge effectively. To address these limitations, this work introduces an interpretable, human-in-the-loop framework for segmentation-based hyperspectral image classification. The proposed approach combines spectral–spatial feature fusion with affinity propagation–based segmentation and a computationally efficient Extreme Learning Machine classifier. Reliability and transparency are enhanced by embedding explainability mechanisms and structured expert feedback directly within the learning process. In contrast to fully automated pipelines, the framework allows human experts to assess uncertain predictions and iteratively refine the model through guided feedback. Rigorous mathematical formulations are presented to describe feature integration, similarity computation, classifier training, and feedback-driven optimization. Extensive experiments on widely used hyperspectral benchmark datasets demonstrate that the proposed framework consistently outperforms conventional classification methods, particularly under limited training data conditions. Performance gains are evident not only in classification accuracy but also in robustness and interpretability. These results highlight the effectiveness of integrating segmentation techniques, lightweight learning models, and human-centered AI principles to build reliable hyperspectral classification systems. Overall, the proposed solution provides a scalable, transparent, and practical approach for real-world applications where expert oversight and explainability are critical.