The panchromatic limitations of WorldView-1, recently launched by Digital Globe, have brought a few posts (e.g. free geography tools and the confused life) on the fusion of high spatial resolution panchromatic imagery (PAN) with lower spatial resolution multispectral imagery (MSI). I thought I would briefly comment on image fusion because over the years it has become easier to accomplish, but the results or limitation of the fused product may be difficult to understand.

There are many ways to accomplish pan-sharpening including band substitution, color space transformation and substitution, and Principle Component Substitution (Jensen,2005). As mentioned on the confused life, temporal decorrelations introduce artifacts into a fused, or PAN-sharpened image. However there are other artifacts that can be equally important if one is trying to create a quantitative product for classification mapping or target detection.

The inherent difficulty with all of the PAN sharpening methods is that they are fundamentally based on the technical and environmental conditions under which the PAN imagery was collected. Since it is difficult, if not impossible, to accurately correct for illumination and atmospheric conditions in PAN sharpened imagery (subject for a much longer post), the PAN-sharpened images may be limited to classification and detection within a scene. Inter-scene comparisons (i.e. change detection between scenes or cross scene classifications) using spectral properties require the aforementioned corrections. In addition, when the instantaneous field of view (IFOV) of the PAN and/or MSI sensors are too large, spectral and illumination changes will be present at the edges of the image, making even within scene classifications difficult. Because of these issues, PAN-sharpened multispectral images are frequently used to identify features based on relative color differences within an image, rather than target identification or environmental characterization based on a spectral signature itself.

Figure 1. The fusion of high spatial resolution MSI (left figure) with lower spatial resolution HSI (middle figure) into a high spatial resolution, high spectral resolution image (right image). The bottom row of images represents the spectral plots at the pixel located at the center of the red cross hairs in the images directly above them.

We have done some work in this area, mainly focused on sharpening hyperspectral imagery (HSI) with multispectral imagery (MSI). Figure 1 shows the results of some of our efforts. The left image is a high resolution MSI from an Applanix DSS. Underneath it is the digital value of the RGB channel of the image. The middle image is the lower spatial resolution HSI; and underneath it is the full spectrum resolution of the HSI vector (~3 nm resolution). By fusing these two images together (right image), we were able to create a high spatial resolution sharpened HSI image whose spectral vector matched reasonably well with the spectral vector from the original HSI image. The use of atmospheric- and illumination-corrected HSI imagery means that we could make classification comparisons or target detections using these spectra much more robustly across scenes in time and space.

When making fused, or derivative, mapping products the value of the map is critically determined by the base mapping material and the skill of the map producer. Understanding the limitations of the base mapping material as well as the fusion techniques themselves is a critical determinate in the value of a derivative mapping product.

References
Jensen, John J., Introductory Digital Image Processing: A Remote Sensing Perspective. Prentice-Hall, Englewood Cliffs, NJ, 2005, 526 pp.

The worldwide spatial information management industry has been estimated at ~$50 billion. While large, the industry is dominated by specialization and niche practices that have reduced the flow of spatial information between location-aware enterprises. This reduction in information flow decreases efficiency and productivity within enterprises, and between industries.

Let us examine the different vertical markets that make up the spatial information industry, including urban planning, emergency response, real estate, natural resource management, environmental protection, agriculture, asset management, construction, advertising, etc. They all use slightly different tactics to acquire their spatial awareness or geospatial intelligence (Figure 1; this figure and the next are from a 2007 Where 2.0 presentation. If you are interested in the full presentation let me know.). However, all of these industries have very similar needs in that they require high quality maps to make fundamental (insert your favorite term here, e.g. business, asset, resource, targeting, etc.) decisions.

Figure 1. Vertical silos in the spatial information business keep the markets small and separated.

If we can break down these vertical silos, such that the maps in one niche were used as raw material into the next niche, we can re-order our geospatial markets to look like Figure 2. Here, the silos become building blocks for higher valued information products, which in turn are used as base products for higher valued geo-enabled processes. These building blocks now increase business process efficiency and productivity for the spatially-aware enterprise. As any process manager will tell you, increasing efficiency and productivity is good, really good, because it means you can do more for less.

Figure 2. Silos are changed into building blocks for higher valued industries, increasing efficiency of productivity and resource management.

A recent article from Geoff Zeiss (who was building upon a 2004 article by Paul Teicholz) used the construction industry as an example of the impact of information silos. He first points out the size of the construction industry, worldwide = $2.3 trillion, US = $1.2 trillion. That’s trillion with a T.

Paul’s article examines a decline in construction productivity, during a period when all other industries were looking at increases in productivity (Figure 3). Paul points to a lack of IT integration and R&D by the building industry as a reason for this real fall in productivity, while all other non-farm industries appear to have used IT to become more productive. Geoff goes farther (and I tend to agree with him) that part of the problem relates to the ‘Islands of Information’ that are created, and not shared, by the various disciplines involved with the construction industry:

Disciplines such as architecture, structural engineering, construction, civil engineering, and GIS are classic information silos. Each maintains its own information island comprised of design applications and data. This has created a nightmare for operations and maintenance, emergency planners and responders, urban planners, and others who require seamless access to urban terrain including building interiors and exteriors, roads and highways, and above ground and underground utilities. The biggest challenge is not typically data, because the data that would help these folks already exists because much of (sic) it is created when buildings and infrastructure were designed. The biggest challenge is that islands of information and technology make it difficult to integrate existing data in a seamless view.

Figure 3. Labor productivity declines 1964-2003. (from ACEbytes Viewpoint #4)

WeoGeo was started to specifically address the creating, sharing, and marketing of geospatial content that will help increase the productivity of spatially-aware industries. We have built an easy to use interface and system to rapidly list, host, discover, customize, and deliver value added geo-intelligence in a way that generates revenue for content providers, which will be affordable for content users. We are using a classic exchange mechanism to create a neomarket to “remake” the silos into “connections” between the islands of geospatial information (I know I am mixing metaphors, but I couldn’t help it. Sorry.)

Does it matter? Are there enough inefficiencies to be found that will translate into dollars to make a difference? Here is another quote from Geoff’s piece:

Several years ago the National Institute of Standards and Technology (NIST) commissioned a study on Interoperability to attempt to quantify the efficiency losses in the U.S. capital facilities industry… NIST estimated that in 2002 poor interoperability cost the US capital facilities industry $15.8 billion.

That leaves some room for improvement in efficiency. And this is just one spatially-aware industry. An increase in productivity in these industries will create a more efficient use of (natural) resources, which over time creates a positive feedback into the quality of operations (and life) for all those using planetary resources.

I posted last week about bandwidth issues associated with geospatial data and our AWS S3 solution. The deciding factor for us to use Amazon’s offerings was not necessarily the edge distribution capabilities of S3, but the synergy from combining S3 data storage and distribution with virtual computing capabilities of EC2. There are multiple issues in image processing that require a ton of memory space and CPU horsepower. In both Market and Server, we offer the following basic map distribution options to our map providers -

Geo Clipping (6 zoom levels, allowing for ~125 million possible selections per data set)
Spatial Resampling (4 levels)
Layer Resampling (depends on data)
Output File types (5 - JPEG, GeoTIFF, ENVI, ESRI BIL, ERDAS IMG)
Projections (5 - UTM, Transverse Mercator, Lambert Conic, Albers Equal, Geographic)
Datums (3 - WGS84, NAD 83, NAD 27)

These options result in millions of possible map variants, which preclude the storage of each variant for distribution. So processing power for conversion is critical; and this processing power needs to be connected to a large, web-addressable, temporary data storage array to house the unique variant that a map user has selected. Now for a true mapping marketplace, this infrastructure needs to support 100s to possibly 1000s of simultaneous map requests from the same base map like the 40 GB image in Figure 1. Doing our NeoMapping Market correctly requires the creation of enormous processing, storage, and bandwidth infrastructure.

Figure 1. 40 GB, 156 layer HyperSpectral Imagery (HSI) map listed on WeoGeo Market. (Click on image to go to the listing in the Market).

However, who could afford that infrastructure upfront? Our original estimates for acquiring base computation needs and placing them into a co-location facility were around $500K. While not a lot of money in the scale of today’s internet operations, it was big for us. In addition, we were trying to develop the software architecture to support the Market and Server, and these expenses were large in it of themselves. AWS provided a unique and simultaneous answer to many of our immediate storage, processing, and distribution needs.

Developing our infrastructure on the scalable AWS solution allows us to say we can support the 1000s of map requests required for a functioning digital marketplace. The user experience is vital to the service’s credibility and therefore our success. However, there is a true (and in a number of cases unexpectedly high) cost in this decision. We traded high capital expenditures for high operating expenditures. In an upcoming post, I’ll talk about the Total Cost of Operations (TCO) on AWS, and some of the ways we are moving to reduce these high operating expenses through stability and scaling solutions. Some of these solutions we have turned into products that we provide to others (e.g WeoCEO)..

I would be interested in hearing about the actual experience of others on AWS and whether S3 and EC2 could or could not meet their needs.