Abstract
Addressing crop-mapping problems using high-resolution hyperspectral images requires innovative solutions that integrate advanced image processing and machine learning. In this paper, we propose an approach designed to address the main challenges of crop mapping using hyperspectral images, including the analysis of high-dimensional feature spaces, the limited number of training samples and the complex spectral-spatial relationships. The proposed architecture exploits Contractive-Expansive-Contractive (CEC) connections, which extract features capable of capturing both global and local patterns, while enhance discriminative features and suppress noise and irrelevant information. The CEC network is used to integrate multiscale features and learn the optimal decision boundaries for segmentation. The proposed architecture is evaluated on a Prisma hyperspectral dataset and compared against other methodologies suited for crop-type mapping. The experimental results demonstrate that the proposed approach achieves higher overall and class-wise segmentation accuracy.