High-Resolution Multispectral Dataset for Semantic Segmentation
This provides a new benchmark for researchers developing semantic segmentation algorithms for remote sensing and multispectral imagery, though it is incremental as it primarily adds data rather than novel methods.
The authors tackled the lack of publicly available benchmark datasets for semantic segmentation of non-RGB imagery by introducing a high-resolution multispectral dataset with pre-split training/testing folds, enabling standardized evaluation and advancement in classification frameworks.
Unmanned aircraft have decreased the cost required to collect remote sensing imagery, which has enabled researchers to collect high-spatial resolution data from multiple sensor modalities more frequently and easily. The increase in data will push the need for semantic segmentation frameworks that are able to classify non-RGB imagery, but this type of algorithmic development requires an increase in publicly available benchmark datasets with class labels. In this paper, we introduce a high-resolution multispectral dataset with image labels. This new benchmark dataset has been pre-split into training/testing folds in order to standardize evaluation and continue to push state-of-the-art classification frameworks for non-RGB imagery.