Authors: Daniele Rege Cambrin, Eleonora Poeta, Eliana Pastor, Tania Cerquitelli, Elena Baralis, Paolo Garza
Abstract: Segmentation of crop fields is essential for enhancing agricultural
productivity, monitoring crop health, and promoting sustainable practices. Deep
learning models adopted for this task must ensure accurate and reliable
predictions to avoid economic losses and environmental impact. The newly
proposed Kolmogorov-Arnold networks (KANs) offer promising advancements in the
performance of neural networks. This paper analyzes the integration of KAN
layers into the U-Net architecture (U-KAN) to segment crop fields using
Sentinel-2 and Sentinel-1 satellite images and provides an analysis of the
performance and explainability of these networks. Our findings indicate a 2\%
improvement in IoU compared to the traditional full-convolutional U-Net model
in fewer GFLOPs. Furthermore, gradient-based explanation techniques show that
U-KAN predictions are highly plausible and that the network has a very high
ability to focus on the boundaries of cultivated areas rather than on the areas
themselves. The per-channel relevance analysis also reveals that some channels
are irrelevant to this task.
Source: http://arxiv.org/abs/2408.07040v1
