Multi Seasonal Deep Learning Classification of Venus Images

Deep neural networks (NNs) trained on hyperspectral images are employed typically for the classification of new images collected from the same sensor, assuming similar characteristics to those of the training images. Creating, however, high-quality ground truth (GT) for training is rather complex, especially when attempting to classify multi-temporal images over seasonal changes. To overcome this difficulty, we propose a novel method that utilizes an additional, one-time collection of hyperspectral FENIX images in the Spring along with ground observations from the end of the Fall. The hyperspectral data are then used for simulation of GT for training. At the same time, the field campaign allows for fine-tuning of the NN to achieve enhanced, multi-seasonal hyperspectral image classification. Indeed, we demonstrate how the proposed method successfully classifies new VEN \mu \mathrm{S} images obtained during different seasons.