Ad-hoc pre-tained models for multi-spectral satellite images

This study addresses the challenge of adapting pretrained models, originally designed for three-band (RGB) imagery, to multispectral data with more than three bands, a mismatch that often leads to suboptimal performance in remote sensing tasks. To overcome this limitation, we propose the development of pretrained multispectral deep learning models tailored to the spectral characteristics of the PeruSat-1 sensor for remote sensing applications. We evaluated several architectures from the ResNet family (ResNet34, ResNet52, ResNet101, ResNet152) and VGG16 using a curated dataset that preserves spatial-spectral priors. Each model was trained and tested on classification tasks involving 2, 3, and 4 land cover classes, using both RGB and multispectral inputs. The results show that the proposed multispectral models generalize well, particularly in low- and medium-complexity scenarios, supporting their suitability for transfer learning. These findings underscore the importance and feasibility of creating pretrained models specifically designed for multispectral imagery, enabling more accurate and efficient environmental monitoring, resource management, and other remote sensing applications, without relying on suboptimal adaptations of RGB-based models.