  Remote sensing image semantic segmentation is an important problem for remote
sensing image interpretation. Although remarkable progress has been achieved,
existing deep neural network methods suffer from the reliance on massive
training data. Few-shot remote sensing semantic segmentation aims at learning
to segment target objects from a query image using only a few annotated support
images of the target class. Most existing few-shot learning methods stem
primarily from their sole focus on extracting information from support images,
thereby failing to effectively address the large variance in appearance and
scales of geographic objects. To tackle these challenges, we propose a
Self-Correlation and Cross-Correlation Learning Network for the few-shot remote
sensing image semantic segmentation. Our model enhances the generalization by
considering both self-correlation and cross-correlation between support and
query images to make segmentation predictions. To further explore the
self-correlation with the query image, we propose to adopt a classical spectral
method to produce a class-agnostic segmentation mask based on the basic visual
information of the image. Extensive experiments on two remote sensing image
datasets demonstrate the effectiveness and superiority of our model in few-shot
remote sensing image semantic segmentation. Code and models will be accessed at
https://github.com/linhanwang/SCCNet.
