Semi-Supervised Hyperspectral Object Detection Challenge

Semi-supervised learning has developed into a highly researched problem as it minimizes the labeling costs while still achieving performance levels comparable to a fully labeled dataset. However, most semi-supervised learning algorithms are based on pre-trained models on ImageNet and are thus challenging to port to other image domains, especially those with more than three bands. In this competition, we present the application of a newly acquired dataset collected from a university rooftop with a hyperspectral camera to perform object detection. The camera produces images at a spatial resolution of 189-212 x 1600 pixels with 371 spectral bands, which we downsample to 51 bands. Each image in the dataset consists of visible and near-infrared measurements captured over three days on a fixed viewpoint. We use data captured on morning day as the training set. The other two days collectively constitute the validation and test sets on the competition server.