This paper summarizes the top contributions to the fi rst semi-supervised hyperspectral object detection challenge(SSHODC), which was organized as a part of the Perception Beyond the Visible Spectrum (PBVS) 2022 workshopat the Computer Vision and Pattern Recognition (CVPR) conference. In this challenge, we gathered and annotateda fi rst-of-its-kind hyperspectral dataset with temporally contiguous frames collected from a university rooftopobserving a 4-way vehicle intersection over a period of three days. The dataset contains a total of 2890 frames,captured at an average resolution of 1600 X 192 pixels, with 51 hyperspectral bands from 400nm to 900nm. Weuse 989 images as the training set, 605 images as validation set and 1296 images as the evaluation (test) set. Eachset was acquired on a diff erent day to maximize the variance in weather conditions. For this challenge, we onlyprovide labels for 10% of the data, hence formulating a semi-supervised learning task for the participants which isevaluated in terms of average precision over the entire set of classes, as well as individual moving object classes:namely vehicle, bus and bike. Our challenge received participation registration from 38 individuals, with 8participating in the validation phase and 3 participating in the test phase. We provide a description of the datasetacquisition, with challenge formulation, proposed methods and qualitative and quantitative results in this paper.