Hyperspectral Image Dataset for Benchmarking on Salient Object Detection
This provides a benchmark for researchers in hyperspectral image analysis, but it is incremental as it primarily offers a new dataset rather than a novel method.
The authors tackled the lack of a dedicated dataset for hyperspectral salient object detection by releasing a new dataset of 60 hyperspectral images with ground-truth binary images and sRGB renderings, and they evaluated existing models using the AUC metric.
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.