HyperCam

Goel, Mayank; Whitmire, Eric; Mariakakis, Alex; Saponas, T. Scott; Joshi, Neel; Morris, Dan; Guenter, Brian; Gavriliu, Marcel; Borriello, Gaetano; Patel, Shwetak

doi:10.1145/2750858.2804282

Cited by 52 publications

(12 citation statements)

References 34 publications

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“…The first one is to design a specific system based on standard RGB cameras. Exploiting time-multiplexed illumination source, multiple color cameras, and a tube of faced reflectors can be used to complete the reconstruction [259]- [261]. However, this method relies rigorously on environmental conditions and extra equipment, which makes it impractical and costly.…”

Section: Fusionmentioning

confidence: 99%

A Review of Spatial Enhancement of Hyperspectral Remote Sensing Imaging Techniques

Aburaed

Alkhatib

Marshall

et al. 2023

IEEE J. Sel. Top. Appl. Earth Observations Remote Sensing

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Remote sensing technology has undeniable importance in various industrial applications, such as mineral exploration, plant detection, defect detection in aerospace and shipbuilding, and optical gas imaging, to name a few. Remote sensing technology has been continuously evolving, offering a range of image modalities that can facilitate the aforementioned applications. One such modality is Hyperspectral Imaging (HSI). Unlike Multispectral Images (MSI) and natural images, HSI consist of hundreds of bands. Despite their high spectral resolution, HSI suffer from low spatial resolution in comparison to their MSI counterpart, which hinders the utilization of their full potential. Therefore, spatial enhancement, or Super Resolution (SR), of HSI is a classical problem that has been gaining rapid attention over the past two decades. The literature is rich with various SR algorithms that enhance the spatial resolution of HSI while preserving their spectral fidelity. This paper reviews and discusses the most important algorithms relevant to this area of research between 2002-2022, along with the most frequently used datasets, HSI sensors, and quality metrics. Meta-analysis are drawn based on the aforementioned information, which is used as a foundation that summarizes the state of the field in a way that bridges the past and the present, identifies the current gap in it, and recommends possible future directions.

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Section: Fusionmentioning

confidence: 99%

A Review of Spatial Enhancement of Hyperspectral Remote Sensing Imaging Techniques

Aburaed

Alkhatib

Marshall

et al. 2023

IEEE J. Sel. Top. Appl. Earth Observations Remote Sensing

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“…Most prototypes at this stage have been relatively low power (∼10 W), stationary, and unable to achieve the levels of irradiance required for remote sensing applications at larger distances, and hence have been predominantly been purposed for the task of object detection, relighting and close-up monitoring (Park et al, 2007;Parmar et al, 2012;Shrestha and Hardeberg, 2013). However, results have shown significant promise in the system's ability to reveal key features in the spectral domain, reconstruct spectra with surprising accuracy (Goel et al, 2015), and operate in conditions where an active illumination source can be directly controlled as required. In the field of multispectral video, passive systems employing dispersive optical elements through scene-scanning or bandpass filtering (Yamaguchi et al, 2006), while providing high spectral resolution, are unsuitable for achieving high framerates due to limited ambient illumination.…”

Section: Midar-the Multispectral Imaging Detection and Active Reflementioning

confidence: 99%

Next-Generation Optical Sensing Technologies for Exploring Ocean Worlds—NASA FluidCam, MiDAR, and NeMO-Net

Chirayath

2019

Front. Mar. Sci.

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We highlight three emerging NASA optical technologies that enhance our ability to remotely sense, analyze, and explore ocean worlds-FluidCam and fluid lensing, MiDAR, and NeMO-Net. Fluid lensing is the first remote sensing technology capable of imaging through ocean waves without distortions in 3D at sub-cm resolutions. Fluid lensing and the purpose-built FluidCam CubeSat instruments have been used to provide refraction-corrected 3D multispectral imagery of shallow marine systems from unmanned aerial vehicles (UAVs). Results from repeat 2013 and 2016 airborne fluid lensing campaigns over coral reefs in American Samoa present a promising new tool for monitoring fine-scale ecological dynamics in shallow aquatic systems tens of square kilometers in area. MiDAR is a recently-patented active multispectral remote sensing and optical communications instrument which evolved from FluidCam. MiDAR is being tested on UAVs and autonomous underwater vehicles (AUVs) to remotely sense living and non-living structures in light-limited and analog planetary science environments. MiDAR illuminates targets with high-intensity narrowband structured optical radiation to measure an object's spectral reflectance while simultaneously transmitting data. MiDAR is capable of remotely sensing reflectance at fine spatial and temporal scales, with a signal-to-noise ratio 10-10 3 times higher than passive airborne and spaceborne remote sensing systems, enabling high-framerate multispectral sensing across the ultraviolet, visible, and near-infrared spectrum. Preliminary results from a 2018 mission to Guam show encouraging applications of MiDAR to imaging coral from airborne and underwater platforms whilst transmitting data across the air-water interface. Finally, we share NeMO-Net, the Neural Multi-Modal Observation & Training Network for Global Coral Reef Assessment. NeMO-Net is a machine learning technology under development that exploits high-resolution data from FluidCam and MiDAR for augmentation of low-resolution airborne and satellite remote sensing. NeMO-Net is intended to harmonize the growing diversity of 2D and 3D remote sensing with in situ data into a single open-source platform for assessing shallow marine ecosystems globally Chirayath and Li NASA FluidCam, MiDAR, and NeMO-Net using active learning for citizen-science based training. Preliminary results from four-class coral classification have an accuracy of 94.4%. Together, these maturing technologies present promising scalable, practical, and cost-efficient innovations that address current observational and technological challenges in optical sensing of marine systems.

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“…Magic finger [21] uses a micro camera placed on the tip of a finger to capture images of the textures for different objects, and then uses a classifying algorithm to identify the corresponding materials. HyperCam [3] uses a sophisticated camera system capable of capturing multi-spectral images, providing high detection of salient textured surfaces for disambiguating objects and organic surfaces. SCiO [11] is a consumer device that uses Near Infra Red (NIR) to sense materials, mainly for testing the quality of food and pills.…”

Section: Related Workmentioning

confidence: 99%

SpeCam

Yeo

Lee

Bianchi

et al. 2017

Proceedings of the 19th International Conference on Human-Computer Interaction With Mobile Devices and Services

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SpeCam is a lightweight surface color and material sensing approach for mobile devices which only uses the front-facing camera and the display as a multi-spectral light source. We leverage the natural use of mobile devices (placing it facedown) to detect the material underneath and therefore infer the location or placement of the device. SpeCam can then be used to support discreet micro-interactions to avoid the numerous distractions that users daily face with today's mobile devices. Our two-parts study shows that SpeCam can i) recognize colors in the HSB space with 10 degrees apart near the 3 dominant colors and 4 degrees otherwise and ii) 30 types of surface materials with 99% accuracy. These findings are further supported by a spectroscopy study. Finally, we suggest a series of applications based on simple mobile micro-interactions suitable for using the phone when placed face-down.

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HyperCam

Cited by 52 publications

References 34 publications

A Review of Spatial Enhancement of Hyperspectral Remote Sensing Imaging Techniques

A Review of Spatial Enhancement of Hyperspectral Remote Sensing Imaging Techniques

Next-Generation Optical Sensing Technologies for Exploring Ocean Worlds—NASA FluidCam, MiDAR, and NeMO-Net

SpeCam

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