Robust Land Cover Classification Using Joint Spectral, Spatial, and Elevation Representations
Keywords:
land cover classification, hyperspectral imaging, LiDAR, deep learning, multi-modal fusion, robustness, fairness, policy, infrastructureAbstract
Land cover classification is a fundamental task in remote sensing with direct implications for environmental monitoring, urban planning, agricultural management, and climate change mitigation. Traditional approaches relying solely on spectral signatures often suffer from ambiguities introduced by spatial heterogeneity and topographic variation. This paper proposes a robust classification framework that jointly learns spectral, spatial, and elevation representations through a multi-stream deep architecture. The system fuses hyperspectral imagery with LiDAR-derived digital elevation models, enabling the model to distinguish classes that exhibit similar spectral responses but differ in vertical structure. We discuss the architectural trade-offs between early fusion, intermediate fusion, and late fusion strategies, emphasizing the importance of representation alignment and cross-modal attention mechanisms. Beyond technical performance, the paper examines deployment infrastructure challenges, including computational cost, data acquisition logistics, and model interpretability. Robustness is analyzed with respect to sensor noise, seasonal variation, and adversarial perturbations, while fairness considerations address biases in training data that may disproportionately affect underrepresented land cover types. Policy and governance implications are explored, particularly in the context of global land cover monitoring initiatives that require standardized, reproducible, and equitable classification pipelines. The proposed framework demonstrates that integrating elevation information not only improves accuracy but also enhances the resilience of classification systems under distribution shifts. Through a synthesis of empirical findings and system-level reasoning, the paper contributes a comprehensive perspective on the design and deployment of joint spectral-spatial-elevation classifiers for operational use.
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