Validating Hyperspectral Image Segmentation
This addresses validation issues in remote sensing for researchers developing segmentation algorithms, but it is incremental as it focuses on improving benchmarking rather than proposing a new method.
The authors identified that current validation strategies for hyperspectral image segmentation algorithms can lead to over-optimistic results, and they introduced a new routine for generating benchmarks to provide fair training-test data partitions.
Hyperspectral satellite imaging attracts enormous research attention in the remote sensing community, hence automated approaches for precise segmentation of such imagery are being rapidly developed. In this letter, we share our observations on the strategy for validating hyperspectral image segmentation algorithms currently followed in the literature, and show that it can lead to over-optimistic experimental insights. We introduce a new routine for generating segmentation benchmarks, and use it to elaborate ready-to-use hyperspectral training-test data partitions. They can be utilized for fair validation of new and existing algorithms without any training-test data leakage.