FuSS: Fusing Superpixels for Improved Segmentation Consistency
This work addresses segmentation consistency for remote sensing applications, but it appears incremental as it builds on existing frameworks like OpenPCS.
The authors tackled the problem of improving semantic consistency in Open Set Semantic Segmentation by proposing two methods: OpenGMM for multimodal pixel distribution modeling and a post-processing approach using superpixels, including a novel superpixel method called FuSS. They achieved state-of-the-art results on ISPRS Vaihingen and Potsdam datasets, with improvements in quantitative and qualitative metrics.
In this work, we propose two different approaches to improve the semantic consistency of Open Set Semantic Segmentation. First, we propose a method called OpenGMM that extends the OpenPCS framework using a Gaussian Mixture of Models to model the distribution of pixels for each class in a multimodal manner. The second approach is a post-processing which uses superpixels to enforce highly homogeneous regions to behave equally, rectifying erroneous classified pixels within these regions, we also proposed a novel superpixel method called FuSS. All tests were performed on ISPRS Vaihingen and Potsdam datasets, and both methods were capable to improve quantitative and qualitative results for both datasets. Besides that, the post-process with FuSS achieved state-of-the-art results for both datasets. The official implementation is available at: \url{https://github.com/iannunes/FuSS}.