  Remote sensing images are known of having complex backgrounds, high
intra-class variance and large variation of scales, which bring challenge to
semantic segmentation. We present LoG-CAN, a multi-scale semantic segmentation
network with a global class-aware (GCA) module and local class-aware (LCA)
modules to remote sensing images. Specifically, the GCA module captures the
global representations of class-wise context modeling to circumvent background
interference; the LCA modules generate local class representations as
intermediate aware elements, indirectly associating pixels with global class
representations to reduce variance within a class; and a multi-scale
architecture with GCA and LCA modules yields effective segmentation of objects
at different scales via cascaded refinement and fusion of features. Through the
evaluation on the ISPRS Vaihingen dataset and the ISPRS Potsdam dataset,
experimental results indicate that LoG-CAN outperforms the state-of-the-art
methods for general semantic segmentation, while significantly reducing network
parameters and computation. Code is available
at~\href{https://github.com/xwmaxwma/rssegmentation}{https://github.com/xwmaxwma/rssegmentation}.
