Hyperspectral Image Dataset for Benchmarking on Salient Object Detection
Nevrez Imamoglu , Yu Oishi, Xiaoqiang Zhang , Guanqun Ding, Yuming Fang, Toru Kouyama , Ryousuke Nakamura
<Abstract> Many works have been done on salient object detection using supervised or unsupervised approaches on color 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 color 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 labeled on the images. Finally, we did performance evaluation using Area Under Curve (AUC) metric on some existing hyperspectral saliency detection models in literature.