1 research outputs found
Hyperspectral Unmixing: Ground Truth Labeling, Datasets, Benchmark Performances and Survey
Hyperspectral unmixing (HU) is a very useful and increasingly popular
preprocessing step for a wide range of hyperspectral applications. However, the
HU research has been constrained a lot by three factors: (a) the number of
hyperspectral images (especially the ones with ground truths) are very limited;
(b) the ground truths of most hyperspectral images are not shared on the web,
which may cause lots of unnecessary troubles for researchers to evaluate their
algorithms; (c) the codes of most state-of-the-art methods are not shared,
which may also delay the testing of new methods.
Accordingly, this paper deals with the above issues from the following three
perspectives: (1) as a profound contribution, we provide a general labeling
method for the HU. With it, we labeled up to 15 hyperspectral images, providing
18 versions of ground truths. To the best of our knowledge, this is the first
paper to summarize and share up to 15 hyperspectral images and their 18
versions of ground truths for the HU. Observing that the hyperspectral
classification (HyC) has much more standard datasets (whose ground truths are
generally publicly shared) than the HU, we propose an interesting method to
transform the HyC datasets for the HU research. (2) To further facilitate the
evaluation of HU methods under different conditions, we reviewed and
implemented the algorithm to generate a complex synthetic hyperspectral image.
By tuning the hyper-parameters in the code, we may verify the HU methods from
four perspectives. The code would also be shared on the web. (3) To provide a
standard comparison, we reviewed up to 10 state-of-the-art HU algorithms, then
selected the 5 most benchmark HU algorithms, and compared them on the 15 real
hyperspectral datasets. The experiment results are surely reproducible; the
implemented codes would be shared on the web