RemoteNet: Remote Sensing Image Segmentation Network based on Global-Local Information
This work addresses segmentation for remote sensing applications, but it appears incremental as it builds on existing transformer and convolution methods without a major breakthrough.
The authors tackled the problem of semantic segmentation in remote sensing images, which is challenging due to large scale and cluttered backgrounds, by proposing RemoteNet, a network that integrates global and local features using transformers and convolutions, achieving effective results on two public datasets.
Remotely captured images possess an immense scale and object appearance variability due to the complex scene. It becomes challenging to capture the underlying attributes in the global and local context for their segmentation. Existing networks struggle to capture the inherent features due to the cluttered background. To address these issues, we propose a remote sensing image segmentation network, RemoteNet, for semantic segmentation of remote sensing images. We capture the global and local features by leveraging the benefits of the transformer and convolution mechanisms. RemoteNet is an encoder-decoder design that uses multi-scale features. We construct an attention map module to generate channel-wise attention scores for fusing these features. We construct a global-local transformer block (GLTB) in the decoder network to support learning robust representations during a decoding phase. Further, we designed a feature refinement module to refine the fused output of the shallow stage encoder feature and the deepest GLTB feature of the decoder. Experimental findings on the two public datasets show the effectiveness of the proposed RemoteNet.