WeoGeo was born from a need to preview, share, and distribute geospatial content. Our experience with this goes back nearly 9 years in developing a technology called environmental HyperSpectral Imaging (HSI) spectroscopy (see our non-profit research efforts at the Florida Environmental Research Institute). HSI technology is built upon collecting images at many narrow discrete wavelengths to build up a calibrated spectrum for each pixel in the image (Figure 1). Each of these discrete wavelengths is stored as a unique spectral channel yielding dozens, even hundreds, of bands of color information (as opposed to consumer cameras with three bands: Red, Green, and Blue). We created some novel techniques (including WeoGeo) to process, store, and deliver those hundreds of bands efficiently.

Figure 1. HyperSpectral Imaging Concept.

HSI is not a new field. The US government has been actively supporting it development for over 2 decades. The best known aircraft HSI instrument is run by NASA JPL. They have been operating the AVIRIS sensor since the early 1990’s for earth sciences studies. Two recent satellite HSI missions include NASA’s Hyperion and ESA’s CHRIS sensors. Our contribution to this field has been focused on dark target spectroscopy for water applications. Our primary patrons in the development of HSI for water have included the Office of Naval Research (ONR) and the National Oceanic and Atmospheric Administration (NOAA). Both agencies have an interest in finding and identifying things in the water using automated targeting and classification techniques. Basically we have been trying to “see” through the water to determine the depth of the water, the bottom habitat, and the water quality (Figure 2).

Figure 2. Imaging through the water. The color of light leaving the water is affected by the depth of the water, the stuff in the water, and stuff on the bottom.

Water is called a “dark target” because the reflectance of light from beneath the water is usually less than 1%. (“bright” land targets can be greater than 50%). This is important for signal processing where the quality of the feature map is strongly dependent on the signal to noise in the imagery, which is directly dependent on the target reflectance. The Spectrographic Aerial Mapping System with On-board Navigation (SAMSON) that FERI built and deploys is specifically designed to simultaneously handle bright and target targets.

Figure 3. FERI’s Spectrographic Aerial Mapping System with On-board Navigation (SAMSON; top image) and Ground Processing Unit (GPU; bottom image).

During September of 2006 FERI conducted a mission for NOAA to demonstrate the capabilities of HSI for detecting red tides. Figure 4 shows some results from one the largest Harmful Algal Bloom (HAB) ever recorded in the US. This three band false color composite was created with 3 narrow bands in the blue, green, and near infrared from the full 188 band hyperspectral imaging cube.

Figure 4. False color composite of red tide in Monterey Bay created from HSI image.

An example of how imaging spectroscopy is useful in quantitatively determining the extent of the HAB in this region may be seen in Figure 5 where the full spectra (uncorrected for atmospheric interference and illumination effects) is shown in comparison to a spectra collected outside of the red tide region. The biggest difference is seen in the near infrared region which is responding to increased reflectance of light by the dinoflagellates in the bloom.

Figure 5. A quantitative look at the spectra from an HSI image inside and outside of the bloom. The green line is the spectra inside of the bloom, the pink line is from outside of the bloom. The big difference around 710 nm results from the large numbers of dinoflagellates that reflect light out of the water. A different effect accounts for the difference seen in the 400 to 600 nm range where the dinoflagellates have pigments that absorb light. These pigments result in less light being reflected out of the water where high concentrations of these dinoflagellates are be found.

The more subtle differences in the blue and green regions relate to the differences in absorption of light by the pigments in the dinoflagellates. The change in relative reflectance is what gives this bloom its characteristic “red” color (Figure 6).

Figure 6. Red tide (HAB) as seen from the research vessels collecting data during the experiment. (Photo courtesy of Dr. R. Kudela, UCSC.)

An advantage of HSI is automatically rendering data into feature extracted maps. Automated, in this case, means that an algorithm (as opposed to an expert) can render the imaging data stream into maps of bathymetry, red tides, sea grass beds, wetlands vegetation, habitat maps, land use change, etc. Automated is important because these imaging data can be terabytes in size. The time requirements just to load the imagery into computer memory for viewing and editing can be onerous. Trying to manipulate and analyze the imagery for features, targets, and materials taxes the time and computer systems requirements to the point of making HSI technology and products the realm of the few.

The ideal approach is to use well calibrated sensors to remove atmospheric and illumination effects (the subjects of future blog entries) to generate HSI imagery that can be directly processed into target and feature maps during the initial image processing. This approach can render products like Figure 7 in less than 8 hours of processing on FERI’s field processing station (right side of Figure 3). These map products are much smaller in size than the original imagery data and contain valuable information for users that are unfamiliar with spectroscopy itself. Using automated feature extraction techniques with HSI provides a mechanism for mapping our world more quantitatively and more frequently than is currently being accomplished with traditional field and photogrammetry techniques. It is the future of remote sensing.

Figure 7. The concept of automated feature extraction and classification applied to the wetlands of Morro Bay, CA using HSI data.

The concept for a server that could handle TBs of HSI imagery was originally conceived as a mechanism for FERI to serve its research partners. WeoGeo Market and Server took this concept and expanded it to handle a larger number of map forms, in a more intuitive manner. The Market provides a portal where other can contribute their value-added mapping content and be compensated. Server gives an enterprise the ability to manage its geospatial content, as well as easily monetize that content. Together they help address what became one of our hardest technical challenges at FERI – How do we serve our partners the maps that they want?