Name
Abstract | ||
|---|---|---|
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. |
Year | Venue | Field |
|---|---|---|
2017 | arXiv: Computer Vision and Pattern Recognition | Modalities,Scale-space segmentation,Pattern recognition,Segmentation,Computer science,Multispectral image,Artificial intelligence,Machine learning |
DocType | Volume | Citations |
Journal | abs/1703.01918 | 0 |
PageRank | References | Authors |
0.34 | 8 | 3 |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Ronald Kemker | 1 | 163 | 7.77 |
| Carl Salvaggio | 2 | 5 | 4.83 |
| Christopher Kanan | 3 | 310 | 25.31 |