Morphological CNN Combined with Noise Inclined Module and Denoising Framework

Classifying hyperspectral data faces challenges like noise interference and effectively incorporating both spectral and spatial information. Noise from various sources distorts spectral signatures, complicating accurate classification. Although hyperspectral data offers detailed spectral information, it suffers from an inadequacy of spatial context. To tackle this, our research introduces a novel method merging morphological CNNs with a Noise Inclined Module and a robust denoising framework. Our unique module effectively reduces noise, and the morphological CNNs capture both spectral and spatial details concurrently. This integration enhances classification accuracy by incorporating spatial relationships. By mitigating noise and integrating spatial context, our approach significantly advances hyperspectral data classification, addressing key hurdles for more precise analysis.