A hyperspectral camera captures a scene in many frequency bands across the spectrum, providing rich information and facilitating numerous applications in industrial, military, and commercial domains. Example applications of hyperspectral imaging include medical diagnosis (e.g., early detection of skin cancer), food-quality inspection, artwork authentication, forest monitoring, material identification, and remote sensing. The potential of hyperspectral imaging has been established for decades now. However, to date, hyperspectral imaging has only seen success in specialized and large-scale industrial and military applications. This is because of three main challenges: (i) the sheer volume of hyperspectral data which makes it hard to transmit such data in real-time and thus limiting its usefulness for many applications, (ii) the negative impact of the environmental conditions (e.g., rain, fog, and snow) which reduces the utility of the captured hyper-spectral data, and (iii) the high cost of hyperspectral cameras (upwards of $20K USD) which makes the technology out of reach for many commercial and end-user applications. The goal of this project is to address these challenges to enable wide adoption of hyperspectral imaging in many applications.
People
- Neha Sharma
- Puria Azadi
- Mohammad Amin Arab
- Kiana Calagari
- Mohamed Hefeeda
Band and Quality Selection for Efficient Transmission of Hyperspectral Images
Abstract
Due to recent technological advances in capturing and processing devices, hyperspectral imaging is becoming available for many commercial and military applications such as remote sensing, surveillance, and forest fire detection. Hyperspectral cameras provide rich information, as they capture each pixel along many frequency bands in the spectrum. The large volume of hyperspectral images as well as their high dimensionality make transmitting them over limited-bandwidth channels a challenge. To address this challenge, we present a method to prioritize the transmission of various components of hyperspectral data based on the application needs, the level of details required, and available bandwidth. This is unlike current works that mostly assume offline processing and the availability of all data beforehand. Our method jointly and optimally selects the spectral bands and their qualities to maximize the utility of the transmitted data. It also enables progressive transmission of hyperspectral data, in which approximate results are obtained with small amount of data and can be refined with additional data. This is a desirable feature for large-scale hyperspectral imaging applications. We have implemented the proposed method and compared it against the state-of-the-art in the literature using hyperspectral imaging datasets. Our experimental results show that the proposed method achieves high accuracy, transmits a small fraction of the hyperspectral data, and significantly outperforms the state-of-the-art; up to 35% improvements in accuracy was achieved.
Software and Datasets
Publications
- P. Moghadam, N. Sharma, M. Hefeeda, Enabling Hyperspectral Imaging in Diverse Illumination Conditions for Indoor Applications, In Proc. of ACM Multimedia Systems Conference (MMSys’21), Istanbul, Turkey, September 2021. (Artifacts Evaluated and Functional)
- N. Sharma, M. Hefeeda, Hyperspectral Reconstruction from RGB Images for Vein Visualization, In Proc. of ACM Multimedia Systems Conference (MMSys'20), Istanbul, Turkey, June 2020. (Artifacts Evaluated and Functional)
- M. Arab, K. Calagari, M. Hefeeda, Band and Quality Selection for Efficient Transmission of Hyperspectral Images, In Proc. of ACM Conference on Multimedia (MM'19), Nice, France. October 2019.
