Hyper-Object Challenge @ ICASSP 2026

The Hyper-Object Challenge pushes the frontier of low-cost hyperspectral imaging. Participants are invited to develop deep learning models that reconstruct high-fidelity hyperspectral image cubes (400–1000 nm, 61 bands) of real-world objects from cost-effective input formats, such as spectral mosaics or low-resolution RGB images. The goal is to democratize access to spectral imaging technology for real-world applications like agriculture, cultural heritage, forensics, and material analysis.

We offer two tracks, each focusing on a different practical constraint:

• Track 1 – Spectral Reconstruction from Mosaic Images
  Input: A single-channel spectral mosaic image (1 band per pixel)
  Output: A full-resolution hyperspectral cube with 61 bands
• Track 2 – Joint Spatial & Spectral Super-Resolution
  Input: A low-resolution RGB image (captured by a commodity camera)
  Output: A high-resolution hyperspectral cube (61 bands, upsampled)

You may participate in one or both tracks.

• Training data – Available now
• Public test data – Released on Aug 25, includes input and ground-truth for local validation
• Private test data – Input-only; used for leaderboard ranking
• Hidden test data – Used internally to evaluate your submitted model/code for final ranking

Please refer to the Data Description section for more details.

We use the Spectral-Spatial-Color (SSC) composite score as the main evaluation metric. It combines the following metrics:

• SAM (Spectral Angle Mapper)
• SID (Spectral Information Divergence)
• ERGAS
• PSNR
• SSIM
• ΔE (color difference in rendered RGB space)

Each metric is normalized and combined into a single SSC score in [0, 1], where higher is better.

Each sample's output should be a 61-band hyperspectral image cube, matching the spatial resolution of the input (Track 1) or a defined high-res target (Track 2). Outputs must be saved in .npy format as *.npy files named after the test sample ID.

Yes. External datasets, synthetic data, and pre-trained models are allowed, but must be clearly disclosed in your final write-up. Please do not use test set leakage or non-public data.

Note: The organizers and affiliated students will not participate in the competition.

• You must submit a .zip file containing one .npy file per test sample, named exactly as per the sample ID (e.g., sample123.npy).
• The output of each .npy file must be a float32 H×W×61 array in the range [0, 1].

• The leaderboard reflects scores on the private test set.
• Your final ranking will be based on internal evaluation on the hidden test set using your submitted code.
• This ensures fair and robust evaluation under realistic deployment conditions.

In addition to .zip file predictions, you must submit:

• Your trained model weights
• A standalone inference script (e.g., inference.py) that loads your model and generates output .npy files for any given input directory
• A requirements.txt listing all dependencies

We will run your code in a standardized environment and evaluate on the hidden test set.

• You may make up to 5 submissions per day
• A maximum of 3 final code submissions can be made for internal evaluation
• Your highest SSC score from those 3 code submissions will be used for the final ranking

For the final evaluation, your model must be runnable on a single GPU (e.g., NVIDIA RTX 6000 Ada or similar) with <24GB VRAM and <30 minutes inference time per test sample. If your model exceeds this, you may be disqualified.

Please post your questions on the Kaggle discussion forum. The organizers will actively respond and clarify rules and requirements.

The dataset is released under the MIT License, which permits free use, modification, and distribution for both research and commercial purposes, provided that the original authors are properly credited.

• Featured in a special session at ICASSP 2026
• Invited to contribute to the joint overview paper with the organizers
• Provided with certificates and digital badges