Live Webinar – Delivering On-Demand Geoanalytics at Scale

Join us for a live webinar
Tuesday, April 18, 2017 | 10:30am EDT/3:30pm BST

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Delivering On-Demand Geoanalytics at Scale

Things have changed. Vast amounts of imagery are freely available on cloud platforms while big datasets can be hosted and accessed in enterprise environments in ways that were previously cost prohibitive. The ability to efficiently and accurately analyze this data at scale is critical to making informed decisions in a timely manner.

Developed with your imagery needs in mind, the Geospatial Services Framework (GSF) provides a scalable, highly configurable framework for deploying batch and on-demand geospatial applications like ENVI and IDL as a web service. Whether you are a geospatial professional in need of a robust software stack for end-to-end data processing, or a decision maker in need of consolidated analytics for deriving actionable information from complex large-scale data, GSF can be configured to meet your needs.

This webinar will show you real-world example applications that:

  • Describe the capabilities of GSF for scalable data processing and information delivery
  • Introduce the diverse ecosystem of geospatial analysis tools exposed by GSF
  • Illustrate the development of customized ENVI applications within the GSF environment

What are your geospatial data analysis needs?

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If you can’t attend the live webinar, register anyways and we’ll email you a link to the recording.

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HICO Image Processing System – Update

(14-Dec-2016) Please pardon the interruption. The HICO Image Processing System is currently being migrated to another hosting platform and will be back up and running soon. Stay tuned here for an announcement once the maintenance is complete. Thank you.

HICO IPS

A Year of Hyperspectral Image Processing in the Cloud – HICO IPS reaches a milestone

It has been a little more than one year since we first launched the HICO Image Processing System (HICO IPS), and its performance continues to be exceptional. In fact, as a prototype, HICO IPS has exceeded all expectations, working flawlessly since it was first launched in May 2015.

HICO IPS

HICO IPS is a web-application for on-demand remote sensing image analysis that allows users to interactively select images and algorithms, dynamically launch analysis routines, and then see results displayed directly in an online map interface. More details are as follows:

  • System developed to demonstrate capabilities for remote sensing image analysis in the cloud
  • Software stack utilizes a combination of commercial and open-source software
  • Core image processing and data management performed using ENVI Services Engine
  • Operational system hosted on Rackspace cloud server
  • Utilizes imagery from the Hyperspectral Imager for the Coastal Ocean (HICO), which was deployed on the International Space Station (ISS) from 2009-2014
  • Example algorithms are included for assessing coastal water quality and other nearshore environmental conditions
  • Application developed in collaboration between HySpeed Computing and Exelis Visual Information Solutions (now Harris Geospatial Solutions)
  • Project supported by the Center for the Advancement of Science in Space (CASIS)

And here’s a short overview of HICO IPS accomplishments and performance in the past year, including some infographics to help illustrate how the system has been utilized:

  • The application has received over 5000 visitors
  • Users represent over 100 different countries
  • System has processed a total of 1000 images
  • Equivalent area processed is 4.5 million square kilometers
  • The most popular scene selected for analysis was the Yellow River
  • The most popular algorithm was Chlorophyll followed closely by Land Mask
  • Application has run continuously without interruption since launch in May 2015

HICO IPS infographics

Try it out today for yourself: http://hyspeedgeo.com/HICO/

ENVI Analytics Symposium 2016 – Geospatial Signatures to Analytical Insights

HySpeed Computing is pleased to announce our sponsorship of the upcoming ENVI Analytics Symposium taking place in Boulder, CO from August 23-24, 2016.

EAS 2016

Building on the success of last year’s inaugural symposium, the 2016 ENVI Analytics Symposium “continues its exploration of remote sensing and big data analytics around the theme of Geospatial Signatures to Analytical Insights.

“The concept of a spectral signature in remote sensing involves measuring reflectance/emittance characteristics of an object with respect to wavelength. Extending the concept of a spectral signature to a geospatial signature opens the aperture of our imagination to include textural, spatial, contextual, and temporal characteristics that can lead to the discovery of new patterns in data. Extraction of signatures can in turn lead to new analytical insights on changes in the environment which impact decisions from national security to critical infrastructure to urban planning.

“Join your fellow thought leaders and practitioners from industry, academia, government, and non-profit organizations in Boulder for an intensive exploration of the latest advancements of analytics in remote sensing.”

Key topics to be discussed at this year’s event include Global Security and GEOINT, Big Data Analytics, Small Satellites, UAS and Sensors, and Algorithms to Insights, among many others.

There will also be a series of pre- and post-symposium workshops to gain in-depth knowledge on various geospatial analysis techniques and technologies.

For more information: http://harrisgeospatial.com/eas/Home.aspx

It’s shaping up to be a great conference. We look forward to seeing you there.

The Panama Canal from Space – A collection of satellite images before and after the Expansion Project

In commemoration of completion of the Panama Canal Expansion Project, and in tribute to the upcoming official opening on June 26, we present a series of before and after satellite photos highlighting the Expansion Project and showcasing this engineering marvel.

Work on the Panama Canal Expansion took nearly 9 years to complete, starting in September 2007, at a cost of US$5.2 billion. By adding a third set of locks on both the Pacific and Caribbean sides of the canal, dredging the existing navigation channel, adding a new approach channel on the Caribbean side and a new 6.1 km access channel on the Pacific side, and raising the Gatun Lake maximum operating level, the Expansion doubles the capacity of the Canal and significantly increases the size of vessels that can transit the Canal.

Below we present before and after satellite images of the newly expanded Canal, provide an overview of the Expansion Project, show a rare nearly-cloudless image from Landsat-5, and even include one of the earliest Landsat images of the Panama Canal acquired by Landsat-1 on March 18, 1973.

Panama Canal Expansion - Pacific

Satellite views of the Pacific Ocean entrance to the Panama Canal, before (left; Landsat-7 on November 20, 2002) and after (right; Landsat-8 on June 11, 2016) the Expansion Project. Note the addition of the third set of locks, the three sets of water reutilization basins immediately adjacent to the new locks, and the new access channel that now bypasses Miraflores Lake.

 

Panama Canal Expansion - Caribbean

Satellite views of the Caribbean Sea entrance to the Panama Canal, before (left; Landsat-7 on May 28, 2002) and after (right; Landsat-8 on February 20, 2016) the Expansion Project. Note the addition of the third set of locks, the three sets of water reutilization basins immediately adjacent to the new locks, and the new approach channel.

 

Overview - Panama Canal Expansion Project

An overview of the Panama Canal Expansion Project (from: http://micanaldepanama.com/expansion/).

 

Panama Canal - Landsat5 Cloud Free

This is a rare nearly-cloudless glimpse of the entire Panama Canal acquired by Landsat-5 on March 27, 2000.

 

Panama Canal - Landsat1

For the remote sensing history aficionados, this is the earliest Landsat image of the Panama Canal listed in the USGS archives, from Landsat-1 on March 18, 1973, over 40 years ago.

ISS National Lab Releases Gap Analysis on Earth Observation Capabilities from ISS

HySpeed Computing is proud to announce release of the “Campaign Good Earth, Gap Analysis Report” – authored by our own Dr. James Goodman. The report provides an investigative review of remote sensing capabilities from the International Space Station (ISS), including current facilities and resources as well as opportunities for future development.

Campaign Good Earth Gap Analysis Report

ISS National Lab, On Station (28 April 2016) – Last year, CASIS commissioned a study to evaluate the capabilities and limitations of the ISS as a host for commercial remote sensing payloads, including the products and needs of the data analytics community. A full report is now available detailing the findings of this study in the context of the expanding commercial market for Earth observation technologies and analysis.

The ISS provides a unique vantage point for Earth observation, and the ISS infrastructure itself provides many advantages as a robust platform for sensor deployment. Real-time and time-series information gathered from remote sensing applications have proven invaluable to resource management, environmental monitoring, geologic and oceanographic studies, and assistance with disaster relief efforts. This report, an analysis of the gaps between ISS capabilities and limitations in the remote sensing market, is meant to initiate a path toward optimal use of the ISS National Lab as a platform for project implementation and technology development. The report includes:

  • Expert contacts from NASA, CASIS, commercial leaders, and government agencies
  • Recommendations for how to support humanitarian and educational enrichment
  • Implementation strategies for hardware and technology adaptation on the ISS
  • Details on current and planned missions, data sources, and validation requirements

Download the report here.

Remote Sensing in the Cloud – Characterizing shallow coastal environments using HICO IPS

This is the last in a series of posts devoted to reviewing the algorithms currently implemented in the cloud-based HICO Image Processing System (HICO IPS). Links to the other posts in this series are provided below. Here we provide an overview of the algorithm utilized for retrieving multiple layers of water and habitat information for shallow coastal environments.

HICO IPS

Objective – Demonstrate the capability to simultaneously derive information on water properties, water depth and seabed features in shallow coastal environments using a complex hyperspectral image processing algorithm.

Algorithm – Derives inherent optical properties for absorption and backscattering (phytoplankton absorption, detritus and gelbstoff absorption, particle backscattering, total absorption, and total backscattering [m-1]), bottom albedo (reflectance at 550 nm), and water depth (depth [m]) using a semi-analytical inversion model (Goodman and Ustin 2007; Lee et al. 1999, 1998).

Note that this algorithm uses a computationally intensive optimization scheme, which can result in long processing times for large areas. As a reference, the example shown here required approximately 15 minutes to generate output. It is suggested that users select relatively small regions-of-interest when implementing this algorithm.

Inputs – User specified HICO scene, with optional region-of-interest; optional NDWI land/water mask, with user adjustable NDWI threshold; and specification of desired output parameter.

HICO IPS Shark Bay subset

Output – Selected output parameter depicted using a blue-red color ramp, where blue represents low values and red represents high values. If the NDWI land/water mask was selected, then the selected optical property is only calculated and mapped for the water pixels.

HICO IPS Shark Bay aquacor

Try it out today for yourself: http://hyspeedgeo.com/HICO/

 

Related posts

Introducing the HICO Image Processing System

Calculating a land/water mask using HICO IPS

Deriving chlorophyll concentration using HICO IPS

Evaluating water optical properties using HICO IPS

 

References

Goodman J, Ustin SL (2007) Classification of benthic composition in a coral reef environment using spectral unmixing, Journal of Applied Remote Sensing, vol. 1(1), 011501-011501.

Lee Z, Carder KL, Mobley CD, Steward RG, Patch JS (1999) Hyperspectral remote sensing for shallow waters. 2. Deriving bottom depths and water properties by optimization, Applied Optics, vol. 38(18), 3831-3843.

Lee Z, Carder KL, Mobley CD, Steward RG, Patch JS (1998) Hyperspectral remote sensing for shallow waters. I. A semianalytical model, Applied Optics, vol. 37(27), 6329-6338.

Remote Sensing in the Cloud – Evaluating water optical properties using HICO IPS

This is part of an ongoing series dedicated to reviewing the algorithms currently implemented in the cloud-based HICO Image Processing System (HICO IPS). Links to additional posts in this series describing the other algorithms are provided below. Here we provide an overview of the algorithm utilized for evaluating water optical properties in coastal and oceanic water.

HICO IPS

Objective – Retrieve water optical properties for coastal and oceanic water from hyperspectral imagery using a generalized multi-band algorithm.

Algorithm – Estimate water optical properties for absorption and backscattering (specifically, total absorption, phytoplankton absorption, detritus and gelbstoff absorption, total backscattering, and particle backscattering) using the Quasi-Analytical Algorithm (QAA v5; Lee et al. 2009, 2002).

Inputs – User specified HICO scene, with optional region-of-interest; optional NDWI land/water mask, with user adjustable NDWI threshold; and specification of desired optical property.

HICO IPS Turkish Straits

Output – Selected water optical property at 438 nm (m-1) depicted using a blue-red color ramp where blue represents low values and red represents high values. If the NDWI land/water mask was selected, then the selected optical property is only calculated and mapped for the water pixels.

HICO IPS Turkish Straits QAA

Try it out today for yourself: http://hyspeedgeo.com/HICO/

 

Related posts

Introducing the HICO Image Processing System

Calculating a land/water mask using HICO IPS

Deriving chlorophyll concentration using HICO IPS

Characterizing shallow coastal environments using HICO IPS

 

References

Lee Z, Lubac B, Werdell J, Arnone R (2009) An update of the quasi-analytical algorithm (QAA_v5), International Ocean Color Group Software Report, 9 pp.

Lee Z, Carder KL, Arnone RA (2002) Deriving inherent optical properties from water color: a multiband quasi-analytical algorithm for optically deep waters, Applied optics, vol. 41(27), 5755-5772.

Constellations, Clouds & the Conundrum of Big Data Processing

HySpeed Computing is proud to be featured in the inaugural issue of UPWARD, the quarterly magazine of the ISS National Lab.

UPWARD

Constellations, Clouds & the Conundrum of Big Data Processing

“For millennia, humans have looked up to the sky to find constellations of stars, wondering what mysteries they hold. Today, we live in a world where constellations of satellites look down on us, hoping to unravel mysteries as well – by capturing highly complex images of Earth.

“In the commercial remote sensing market, the imaging of Earth from space has experienced a technical tsunami, giving rise to a population explosion of smaller but far more capable satellites with new sensing and communication capabilities. In the near future, constellations of nano-, micro-, and other small-sats will swarm low Earth orbit like drones filling the skies on Earth.”

See the full article beginning on page 10…

Conundrum of Big Data Processing

Remote Sensing in the Cloud – Deriving chlorophyll concentration using HICO IPS

Continuing our review of the algorithms currently implemented in the cloud-based HICO Image Processing System (HICO IPS), here we provide an overview of the two algorithms utilized for deriving estimates of chlorophyll concentration in oceanic water.

HICO IPS

Objective – Implement multiple algorithms for estimating chlorophyll concentration, as well as a methodology for evaluating the difference between these algorithms.

Algorithms – Derive estimates of surface chlorophyll-a concentration using one of two ocean color algorithms, OC4 (O’Reilly et al. 2000) or OCI (Hu et al. 2012); or for comparison, the difference between these two algorithms (OC4 – OCI). These algorithms first perform spectral resampling of the HICO hyperspectral data to the multispectral SeaWiFS bands on which the algorithms are based.

Inputs – User specified HICO scene, with optional region-of-interest; optional NDWI land/water mask, with user adjustable NDWI threshold; and specification of desired chlorophyll algorithm.

HICO IPS Chesapeake Bay

Output – Surface chlorophyll-a concentration (mg/m^3) depicted using a blue-red color ramp where blue represents low chlorophyll concentration and red represents high concentration. If the NDWI land/water mask was selected, then chlorophyll concentrations are only calculated and mapped for the water pixels (as is logical).

HICO IPS Chesapeake Bay Chlorophyll

Try it out today for yourself: http://hyspeedgeo.com/HICO/

 

Related posts

Introducing the HICO Image Processing System

Calculating a land/water mask using HICO IPS

Evaluating water optical properties using HICO IPS

Characterizing shallow coastal environments using HICO IPS

 

References

Hu C, Lee Z, Franz BA (2012) Chlorophyll-a algorithms for oligotrophic oceans: A novel approach based on three-band reflectance difference, Journal of Geophysical Research, vol. 117(C1), 25 pp.

O’Reilly JE, Maritorena S, O’Brien MC, et al. (2000) SeaWiFS postlaunch calibration and validation analyses, Part 3, NASA Technical Memorandum 2000-206892, vol. 11, 49 pp.