View allAll Photos Tagged hyperspectral_imaging
These images were taken during a hyperspectral imaging flyover to gather data on cladophora, a species of algae that causes harmful algal blooms in the Great Lakes. These flyovers are conducted in collaboration with the USGS, the EPA, and the Michigan Tech Research Institute.
Credit: Zachary Haslick, Aerial Associates Photography Inc, www.skypics.com
These images were taken during a hyperspectral imaging flyover to gather data on cladophora, a species of algae that causes harmful algal blooms in the Great Lakes. These flyovers are conducted in collaboration with the USGS, the EPA, and the Michigan Tech Research Institute.
Credit: Zachary Haslick, Aerial Associates Photography Inc, www.skypics.com
These images were taken during a hyperspectral imaging flyover to gather data on cladophora, a species of algae that causes harmful algal blooms in the Great Lakes. These flyovers are conducted in collaboration with the USGS, the EPA, and the Michigan Tech Research Institute.
Credit: Zachary Haslick, Aerial Associates Photography Inc, www.skypics.com
These images were taken during a hyperspectral imaging flyover to gather data on cladophora, a species of algae that causes harmful algal blooms in the Great Lakes. These flyovers are conducted in collaboration with the USGS, the EPA, and the Michigan Tech Research Institute.
Credit: Zachary Haslick, Aerial Associates Photography Inc, www.skypics.com
These images were taken during a hyperspectral imaging flyover to gather data on cladophora, a species of algae that causes harmful algal blooms in the Great Lakes. These flyovers are conducted in collaboration with the USGS, the EPA, and the Michigan Tech Research Institute.
Credit: Zachary Haslick, Aerial Associates Photography Inc, www.skypics.com
Taken at the 2011 National Book Festival in Washington DC. Photo by ideonexus. Creative commons licensed, so please feel free to reuse for your own purposes!
A coastal imaging instrument called the Hyperspectral Imager for the Coastal Ocean, or HICO, is beaming images from the International Space Station to Oregon State University. (photo courtesy of Oregon State University and the Naval Research Laboratory)
Known primarily for their N900 series of semi-autonomous work mechs, the Nought Corporation also has a long history of producing dedicated military systems.
While never intended to compete with mainline military frames such as the ST-07 Chub, Nought's military products have nonetheless been reliable sellers, and have found homes in military and law enforcement organizations throughout human space.
Pictured here is an N312B, the latest model in the N300 series of semi-autonomous military mechs. The B variant is equipped with a pair of hyperspectral imagers, a 55mm autocannon, and a set of missile launchers.
Combined with a sophisticated communications array, these systems allow the 312B to acquire, identify, and engage ground and air targets at long ranges under almost any battlefield condition.
To the right is the mech's operator, who would typically direct the 312B from a remote location.
14th INTERNATIONAL SYMPOSIUM
ON CELLULOSE CHEMISTRY AND TECHNOLOGY
Iaşi – ROMANIA, September 8-10, 2010
In memoriam Academician Cristofor I. Simionescu
14th INTERNATIONAL SYMPOSIUM
ON CELLULOSE CHEMISTRY AND TECHNOLOGY
Iaşi – ROMANIA, September 8-10, 2010
In memoriam Academician Cristofor I. Simionescu
With the aging of the Grumman S-2 Tracker and the increasing effectiveness of Soviet submarines, the U.S. Navy issued a requirement for a new carrier-based ASW aircraft. Lockheed won the contract, partnering with LTV to design carrier-specific equipment and Univac to design the ASW suite. The resulting S-3A Viking took its first flight in January 1972 and entered the fleet in February 1974.
Unlike its predecessor, the S-2, which integrated the hunter-killer team concept into a single airframe, the S-3A Viking took a giant leap forward by completely computerizing the sub-hunting process. It integrated the entire sensor suite into one system, a feat that wasn’t possible on the S-2. This was made possible by the Univac AN/AYK-10 computer, Texas Instruments AN/APS-116 radar, and AN/ASQ-81 MAD sensor in a retractable tail boom. The S-3A Viking, flown by a crew of four, was so efficiently designed that it was hailed as the most compactly designed aircraft in history by one aviation historian.
The S-3A—nicknamed "Hoover" for the sound of its engines—acquired a reputation for being a reliable, easy-to-fly aircraft, and spawned many variants, including the US-3A carrier-onboard delivery (COD) transport aircraft and the ES-3A Shadow Elint variant. A dedicated KS-3A tanker never went into production, but S-3s were increasingly equipped with buddy refueling packs. When the KA-6D Intruder dedicated tankers were retired from the U.S. Navy in the mid-1990s, the S-3s took over the role, though its relatively slow speeds meant that it could not accompany strikes into enemy territory. Despite this, the S-3 always could carry not only antisubmarine ordnance such as torpedoes and depth charges but also bombs and later the AGM-84 Harpoon antiship missile and AGM-65 Maverick AGM. The S-3's anti-ship capabilities were used in both Gulf Wars: in 1991, an S-3 sank an Iraqi attack boat with conventional bombs, while in 2003 an S-3 destroyed an Iraqi command post with a Maverick in Basra.
Beginning in 1991, the S-3As in service were modified to S-3B standards. This involved significant upgrades to the avionics and the installation of a new APS-127V synthetic-aperture radar, which gave the S-3B a significant ship detection and SAR capability. Though the ES-3A was withdrawn from service in the mid-1990s, several S-3Bs were converted to littoral reconnaissance (Gray Wolf) and ground surveillance (Brown Boy) roles. With the reduction of submarine threats to the U.S. Navy, the S-3 fleet was being gradually retired; those remaining in service have had their ASW equipment removed and serve primarily as tankers. Their role has been largely replaced by the SH-60B/F Seahawk series, and, aside from a handful of test aircraft, the S-3 was retired from service in 2009.
However, even though the aircraft was mostly retired from military service, not all of them were grounded. At NASA’s Glenn Research Center in Cleveland, one S-3B was being used daily as a flight research aircraft. It was acquired in 2004 and flown for the next 16 years on a wide variety of research missions. It was originally designed by Lockheed as an anti-submarine warfare aircraft. NASA’s S-3B Viking was completely reconfigured in 2006 for flight research purposes. All weapons systems were removed and replaced with civilian avionics, GPS, and satellite communications systems to conduct flight communications research. One of its major contributions was helping NASA’s aeronautical innovators define communications standards that the Federal Aviation Administration (FAA) can apply to unmanned aircraft systems for safe operation in U.S. airspace.
This S-3B has conducted research flights over every terrain in the national airspace, which includes mountains, hills, bodies of water, plains, and deserts. The results of the flight research have given NASA, the FAA, and its commercial partners a path for building secure, reliable command-and-control radios used for communication from the ground to unmanned aircraft systems. NASA’s S-3B, N601NA, also flew research flights to monitor algal bloom growth in Lake Erie and developed hyperspectral imaging equipment to provide more accurate data for university scientists studying the problem. These hyperspectral imagers, mounted to the Viking’s belly, analyzed a wide spectrum of light to identify the types of harmful algal blooms in the water. This aircraft is now preserved at the Gillespie Field Annex which can be found nearby.
14th INTERNATIONAL SYMPOSIUM
ON CELLULOSE CHEMISTRY AND TECHNOLOGY
Iaşi – ROMANIA, September 8-10, 2010
In memoriam Academician Cristofor I. Simionescu
Kelley marks the presense and absense of eelgrass beds using GPS as Gillian runs a transect along one edge of the slough. Working at low tide we could easily see the eelgrass blades on the surface of the water. Now hopefully we will be able to also detect these beds (and as importantly, the gaps between beds) using the hyperspectral images.
Image: ESA
The French Frigate Shoals, highlighted in this Proba image, is an atoll consisting of a 35-kilometre crescent-shaped reef surrounding a dozen small islets located in the Pacific Ocean about 800 kilometres northwest of Honolulu, Hawaii.
ESA’s Proba satellite acquired this image on 9 January 2006 with its Compact High Resolution Imaging Spectrometer (CHRIS), designed to acquire hyperspectral images with a spatial resolution of 17 metres across an area of 13 kilometres.
With a few modifications to the thrust vectoring systems, the 14a is better balanced at all fuel levels, and is more maneuverable. The "head sensor mast" also switched from primarily optical sensors, to lidar, radar, and hyperspectral imaging systems.
From what we saw the second most common salt marsh plant in Elkhorn Slough (Pickle Weed being the dominant species) This is the patch of heath we used for hyperspectral image target to calibrate a new airborne sensor.
Solid state lighting- direct conversion of electricity to visible white light using semiconductor materials- has the potential to replace today’s inefficient incandescent and CFLs lighting systems. Though SSL has ~55 % conversion efficiency compared to 5-25 % of the latter, it is not yet fully matured to rein the general lighting applications. However, each incremental improvement in efficiency opens door to replace less efficient light sources. According to reports, an improvement in luminous efficiency by 1% can save 2 billion dollars per year and avoid mega tonnes of CO2 exhaust to environment. InGaN QWs on nano-patterned GaN nanopyramids are characterised by increased light emitting efficiency, light emitting area, light extraction efficiency, ability to white light generation and improved, all that contributing towards high luminous efficiency. Figure 1 shows the light emission from an array of InGaN/GaN nanopyramids acquired using cathodoluminescence hyperspectral imaging. These blue light emitting nanopyramids are promising for white light SSL applications.
Image: © 2013 Krishnan Jagadamma Lethy
As participant of the ENMAP science project, the Environmental Remote Sensing facility of the University of Trier has acquired a hyperspectral imager which can be used airborne by being mounted into a Cessna 172 Skyhawk of the aero club at the airport Foehren (EDRT) near Trier.
Infrared photographs of butterflies, where brightness correlates with the capability of radiative cooling.
3 band false color image in the NIR (1000 nm - 1700 nm) taken with a Resonon Pika NIR hyperspectral imager.
Dr. Andrea Vander Woude (GLERL) adds algae to 200 gallons of lake water to measure its bio-optical properties. This data helps describe the algal groups present when looking at satellite & hyperspectral images of the Great Lakes. Photo Credit: NOAA GLERL.
CIGLR's Michele Wensman adds algae to 200 gallons of lake water to measure its bio-optical properties. This data helps describe the algal groups present when looking at satellite & hyperspectral images of the Great Lakes. Photo Credit: NOAA GLERL.
Entry in category 2 Women and men of science; Copyright CC-BY-NC-ND: Sandro Meier
Methane is a potent greenhouse gas, making it a critical target for environmental monitoring. However, significant uncertainties persist regarding the precise locations and quantities of methane emissions. Remote sensing techniques offer a powerful solution to address these uncertainties by enabling large-scale, systematic mapping of methane sources. By utilizing hyperspectral imaging, we can detect methane through spectral analysis, matching the gas's unique absorption spectrum with measured spectra from aerial or satellite observations. Our research utilizes the AVIRIS-4 hyperspectral imaging spectrometer, mounted on a Cessna Caravan aircraft, to conduct comprehensive methane detection surveys.
During measurement campaigns, the aircraft operates at altitudes between 1 to 6 kilometres, allowing extensive spatial coverage. Due to the non-pressurized cabin at higher altitudes, the sensor operators must wear oxygen masks to ensure safety, as shown on the picture.
CIGLR's Michele Wensman mixes algae in 200 gallons of lake water in order to measure its bio-optical properties. This data helps describe the algal groups present when looking at satellite & hyperspectral images of the Great Lakes. Photo Credit: NOAA GLERL.
For more information on the Library of Congress Preservation division, visit http://www.loc.gov/preserv/.
Infrared, lidar, and hyperspectral Images of the Southern California coast seen during a survey flight along the coastline during El Niño season by researchers at Scripps Institution of Oceanography at UC San Diego
Dr. Andrea Vander Woude (GLERL) measures the bio-optical properties of 200 gallons of lake water after algae is added. This data helps describe the algal groups present when looking at satellite & hyperspectral images of the Great Lakes. Photo Credit: NOAA GLERL.
Hyperspectral 3D cube. This is a subset of my study site. It's also a pretty good visual representation of what hyperspectral images are....in short: they are 100's of images (all of the same scene) stacked on top of each other. Each image contains pixel data collected from one small section of the very big Electromagnetic (EM) Spectrum. There is more information here then our poor little human eyes could ever hope to see (without the help of computers and sensors).
Midjourney images created 2024-03-19.
The prompts are in the image names. I append "-U#", where # is the original result image number.
By IMARC Group, Hyperspectral imaging systems (HIS) are tools that are utilized for diverse applications, such as target discrimination, terrain and vegetation characterization and non-invasive medical-imaging spectroscopy.
How We Match Lipids in Images. a) We look at a standard sample of a specific type of lipid using a special kind of light called spontaneous Raman spectroscopy. Then, we process this data to create a reference pattern. b) We take a picture of a sample using another type of light called SRS to create a special image called a Hyperspectral Image (HSI). c) Each tiny dot in this image holds information about the type of lipids present there. We compare these dots to our reference pattern using a method called spectral angle mapping, which helps us see if they're similar. If they're different, they'll have a lower cosine similarity. d) We show an example of a mouse brain picture with highlighted areas where we've found specific lipids like sphingosine, cholesterol, and TAG (triacylglyceride). The intensity of each pixel in these areas reflects how similar they are to our reference pattern. SRS stands for Stimulated Raman Scattering, and HSI stands for Hyperspectral Image. (Panels (a) and (b) were made with BioRender.com)
This SWIR camera independently developed and designed by GHOPTO has a resolution of 640×512, 15μm pixel pitch, and is equipped with a USB3.0 interface. It can achieve a high frame rate of 240fps and 14-bit digital output at the resolution of 640×512. Windowing (optional) realizes higher rate picture transmission. The camera has TE Cooler built in and lower dark current, and the readout noise is as low as 40e-. In addition, the camera has a variety of gain modes and non-uniformity correction, which can improve high-definition images in low light conditions at night, and can also image through fog and haze. Small in size and light in weight, it is easy to integrate in surveillance systems such as drones, shipborne, and airborne optoelectronic pods. It is widely used in wafer inspection, surveillance, hyperspectral imaging and other fields.
PERFORMANCE ADVANTAGES OF GH-SW640-U3
Compact industrial design
01
Automatic on-board image processing
02
Region of interest (ROI) control
03
Single point correction of electronic shutter
04
VGA/QVGA resolution
05
Small size, light weight and low power
06
FEATURES OF GH-SW640-U3
High frame rate， 240fps @ 640×512
TEC
Low read noise
Low dark current
USB3.0
Windowing
Low power dissipation
SDK provided
TECHNICAL INFORMATION OF GH-SW640-U3
TYPE
GH-SW640-U3
Array Type
InGaAs
FPA Format
640×512
Active Area
9.6mm×7.68mm
Pixel Pitch
15μm
Lens Mount
M42×1
Spectral Response
0.9μm-1.7μm (Optional 0.4μm -1.7μm )
Quantum Efficiency
> 70%
Full Well Capacity
1.8Me-
Cooling Capability
TEC
Dark Current
30fA@0.1V&18℃
Output
USB3.0
Digital Output
14bit
Frame Rate
240fps@640×512
Windowing
Programmable
Shutter Mode
Global shutter
Readout Modes
IWR
Exposure Time
60μs~
Operating Temperature
-20°～+70°
Weight
280g (no lens)
Voltage
12V +-2V
Dimension ( D x W x H )
65mm×58mm×65mm
Power Dissipation
<5W (no TEC)
Trigger Interface
RS-422 / TTL compatible
Noise with ROIC
＜40e-（CDS mode）
Image Correction
1-point & 2-point correction
Software
SDK provided
www.ghopto.com/products/640-swir-ingaas-high-cost-perform...
The Preservation Research and Testing Division conducts hyperspectral imaging on an 18th-century illustrated manuscript by Maria Merian, October 29, 2024. Photo by Shawn Miller/Library of Congress.
Note: Privacy and publicity rights for individuals depicted may apply.
Entry in category 1 Object of study; Copyright CC-BY-NC-ND: Sandro Meier
Methane is a potent greenhouse gas, making it a critical target for environmental monitoring. However, significant uncertainties persist regarding the precise locations and quantities of methane emissions. Remote sensing techniques offer a powerful solution to address these uncertainties by enabling large-scale, systematic mapping of methane sources. By utilizing hyperspectral imaging, we can detect methane through spectral analysis, matching the gas's unique absorption spectrum with measured spectra from aerial or satellite observations.
The image demonstrates a detected methane plume overlaid on an RGB image, captured by the airborne hyperspectral imaging spectrometer AVIRIS-4. This specific methane plume originates from a controlled release experiment conducted in autumn 2024. Such experiments are essential for validating the detection and quantification capabilities of advanced imaging systems like AVIRIS-4, providing critical insights into methane emission monitoring technologies.
True Color representation of airborne VNIR spectral data, taken with a Resonon Pika II hyperspectral imager and PCAQ flight data acquisition system.
GH-SW320 InGaAs area image sensors are two-dimensional image sensors that have a hybrid structure consisting of a CMOS readout circuit (ROIC: readout integrated circuit) and back-illuminated InGaAs photodiodes for short wavelength infrared (SWIR, 900nm-1700nm) and extended wavelength infrared (Vis-SWIR, 400nm-1700nm) regions.
The detector can be supplied in two types of metallic package:
Nitrogen & Vacuum, which both include a Thermo-Electric Cooler (TEC)
It is well-adapted to a large range of applications, such as surveillance, hyperspectral imaging, remote sensing, machine vision, industrial sorting, process inspection, and science.
TECHNICAL INFORMATION OF GH-SW320
Key Features
GH-SW320
Sensor Type
InGaAs PIN-Photodiode
Format
320 x 256
Pixel Pitch
15 μm
Spectral Response
0.9µm-1.7µm / 0.4um-1.7µm (optional)
Integration Type
Snapshot
Readout Modes
ITR , IWR , CDS
Exposure Time
2μs to full frame
Maximum Full Frame Rate
500 Hz
Maximum Pixel Rate
18MHz
Output Signal Swing
≤2V
Temperature Sensor Output
Yes
Quantum Efficiency(QE)
>70% ( 1.0um ~ 1.6um ）
Noise with ROIC
35e- @HG
Dark Current
30fA @ 0.1V&18℃
Array Operability
> 99.5% ( Minimum )
Non Uniformity without Correction
＜ 5%
Dimension (W x H x D)
36 mm x 25.4 mm x 7.2 mm
Windows
Quartz / Sapphire
Number of Pins
28-pin Metal DIP Package
Packaging Characteristics
Hermetically Sealed
Operating Temperature
- 40℃ to 70℃
Storage Temperature
- 40℃ to 70℃
My research focuses on identifying biodiversity using remote sensing tools such as satellites and drones. Here, I am capturing hyperspectral images and drone images of vegetation in Montreal. We conducted vegetation surveys and captured images of 1-meter vegetation plots in all of Montreal’s large nature parks last summer. Every species of plant was identified in the plot and the goal was to find image metrics from our remote sensors that would correlate with plant biodiversity on the ground. These metrics can be simple like the amount of light reflected in the green wavelengths or more complex vegetation indices and image textures. This technology allows us to map and monitor biodiversity over large areas where field work would be difficult and time-consuming. In my master’s thesis I am going focus on scaling up this approach using remote sensors from planes and satellites to map forest biodiversity across the province of Quebec.
Jennifer Donnini, Geography, Urban and Environmental Studies
Entry in category 3 Locations and instruments; Copyright CC-BY-NC-ND: Sandro Meier
Methane is a potent greenhouse gas, making it a critical target for environmental monitoring. Key anthropogenic sources of methane include agriculture, fossil fuel industries, and waste management sectors. However, significant uncertainties persist regarding the precise locations and quantities of methane emissions.
Remote sensing techniques offer a powerful solution to address these uncertainties by enabling large-scale, systematic mapping of methane sources. By utilizing hyperspectral imaging, we can detect methane through spectral analysis, matching the gas's unique absorption spectrum with measured spectra from aerial or satellite observations.
This image was captured during a measurement campaign focusing on methane emissions from active coal mines in Poland. The hyperspectral approach allows for efficient detection and quantification of methane sources across extensive industrial landscapes, providing critical data for understanding and mitigating greenhouse gas emissions.
Entry in category 3 Locations and instruments; Copyright CC-BY-NC-ND: Sandro Meier
Methane is a potent greenhouse gas, making it a critical target for environmental monitoring. However, significant uncertainties persist regarding the precise locations and quantities of methane emissions. Remote sensing techniques offer a powerful solution to address these uncertainties by enabling large-scale, systematic mapping of methane sources.
In our research, we use an airborne hyperspectral imaging spectrometer called AVIRIS-4 built into a Cessna Caravan which is depicted on the image. By utilizing hyperspectral imaging, we can detect methane through spectral analysis, matching the gas's unique absorption spectrum with measured spectra from aerial or satellite observations. This approach enables precise identification of methane release points, providing crucial insights into the distribution and magnitude of this potent greenhouse gas. This methodology represents a significant advancement in environmental monitoring and climate change research.
Entry in category 4 Video loop; Copyright CC-BY-NC-ND: Sandro Meier
Methane is a potent greenhouse gas, making it a critical target for environmental monitoring. Key anthropogenic sources of methane include agriculture, fossil fuel industries, and waste management sectors. However, significant uncertainties persist regarding the precise locations and quantities of methane emissions.
Remote sensing techniques offer a powerful solution to address these uncertainties by enabling large-scale, systematic mapping of methane sources. By utilizing hyperspectral imaging, we can detect methane through spectral analysis, matching the gas's unique absorption spectrum with measured spectra from aerial or satellite observations.
This image was captured during a measurement campaign focusing on methane emissions from natural gas compressor stations and industrial facilities in Germany. It allows us to detect and quantify the sources across extensive industrial landscapes, providing critical data for understanding and mitigating methane emissions.
HYPERSPECTRAL IMAGES USING COMPUTATIONAL ENGINEERING
FORMATION OF HYPERSPECTRAL IMAGES USING COMPUTATIONAL ENGINEERING
Images are quite often used for comparison to identify changes in an environment. The field of Computational Engineering is able to model and analyze such changes and for that, different types of images are considered.
The most common of these images is a three-layer RGB image which symbolizes the environmental condition viewed at Red wavelength, Green wavelength, and Blue wavelength all layered together on top of each other to form a single-color image.
Adding further layers at various wavelengths now exceeding the three layers, the image enters the multi-spectral and hyper-spectral image domain. As points of observation (wavelength) have increased, so have the details or information collected from the image.
To form such images, material conditions are required. Major recent works aim for a modeling, analysis, and prediction tool for generating IR signature with segmental analysis of various environments (water, vegetation, rocks, etc.) based on transient heat transfer, flow state, and material composition (e.g., density, specific heat, thermal conductivity).
To reduce errors of computation when compared with actual experimental results, experimental emissivity and reflectance are introduced for IR signature calculation.
www.wanttono.com/education/hyperspectral-images-using-com...