Hyperspectral Imaging
Hyperspectral imaging (HSI) is a powerful tool that can provide substantial information about a scene through remote sensing. This project addresses different challenges in the pipeline of capturing, transmitting, and identifying remote scenes using hyperspectral images. One of the main significant challenges in hyperspectral imaging is analyzing the data and extracting the required information. This is mainly because of the extremely high dimensionality of hyperspectral images, which limits our ability to identify spectral signatures fast and accurately. In addition, noise in hyperspectral images makes matters even more complicated.
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.
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Hyperspectral Reconstruction from RGB Images for Vein Visualization
Abstract
A hyperspectral camera captures a scene in many frequency bands across the spectrum, providing rich information and facilitating numerous applications. The potential of hyperspectral imaging has been established for decades. However, to date hyperspectral imaging has only seen success in specialized and large-scale industrial and military applications. This is mainly due to the high cost of hyperspectral cameras (upwards of $20K) and the complexity of the acquisition system which makes the technology out of reach for many commercial and end-user applications. In this paper, we propose a deep learning based approach to convert RGB image sequences taken by regular cameras to (partial) hyperspectral images. This can enable, for example, low-cost mobile phones to leverage the characteristics of hyperspectral images in implementing novel applications. We show the benefits of the conversion model by designing a vein localization and visualization application that traditionally uses hyperspectral images. Our application uses only RGB images and produces accurate results. Vein visualization is important for point-of-care medical applications. We collected hyperspectral data to validate the proposed conversion model. Experimental results demonstrate that the proposed method is promising and can bring some of the benefits of expensive hyperspectral cameras to the low-cost and pervasive RGB cameras, enabling many new applications and enhancing the performance of others. We also evaluate the vein visualization application and show its accuracy.
