  Many works have been done on salient object detection using supervised or
unsupervised approaches on colour images. Recently, a few studies demonstrated
that efficient salient object detection can also be implemented by using
spectral features in visible spectrum of hyperspectral images from natural
scenes. However, these models on hyperspectral salient object detection were
tested with a very few number of data selected from various online public
dataset, which are not specifically created for object detection purposes.
Therefore, here, we aim to contribute to the field by releasing a hyperspectral
salient object detection dataset with a collection of 60 hyperspectral images
with their respective ground-truth binary images and representative rendered
colour images (sRGB). We took several aspects in consideration during the data
collection such as variation in object size, number of objects,
foreground-background contrast, object position on the image, and etc. Then, we
prepared ground truth binary images for each hyperspectral data, where salient
objects are labelled on the images. Finally, we did performance evaluation
using Area Under Curve (AUC) metric on some existing hyperspectral saliency
detection models in literature.
