Quantitative methods to assess or predict the quality of a spectral image continue to be the subject of a number of
current research activities. An accepted methodology would be highly desirable for use in data collection tasking or
data archive searching in ways analogous to the current prediction of panchromatic image quality through the National
Imagery Interpretation Rating Scale (NIIRS) using the General Image Quality Equation (GIQE). A number of
approaches to the estimation of quality of a spectral image have been published, but most capture only the performance
of automated algorithms applied to the spectral data. One recently introduced metric, however, the General Spectral
Utility Metric (GSUM), provides for a framework to combine the performance from the spectral aspects together with
the spatial aspects. In particular, this framework allows the metric to capture the utility of a spectral image resulting
when the human analyst is included in the process. This is important since nearly all hyperspectral imagery analysis
procedures include an analyst.
To investigate the relationships between candidate spectral metrics and task performance from volunteer human
analysts in conjunction with the automated results, simulated images are generated and processed in a blind test. The
performance achieved by the analysts is then compared to predictions made from various spectral quality metrics to
determine how well the metrics function.
The task selected is one of finding a specific vehicle in a cluttered environment using a detection map produced from
the hyperspectral image along with a panchromatic rendition of the image. Various combinations of spatial resolution,
number of spectral bands, and signal-to-noise ratios are investigated as part of the effort.
Quantitative methods to assess or predict the quality of a spectral image are the subject of a number of current research activities. An accepted methodology would be highly desirable to use for data collection tasking or data archive searches in way analogous to the current uses of the National Imagery Interpretation Rating Scale (NIIRS) General Image Quality Equation (GIQE). A number of approaches to the estimation of quality of a spectral image have been published. An issue with many of these approaches is that they tend to be constructed around specific tasks (target detection, background classification, etc.) While this has often been necessary to make the quality assessment tractable, it is desirable to have a method that is more general. One such general approach is presented in a companion paper (Simmons, et al). This new approach seeks to get at the heart of the general spectral imagery quality analysis problem−assessing the confidence of an image analyst in performing a specified task with a specific spectral image. In this approach the quality from spatial and spectral aspects of the imagery are treated separately and then a fusion concept known as “semantic transformation” is used to combine the utility, or confidence, from these two aspects into an overall quality metric. This paper compares and contrasts the various methods published in the literature with this new General Spectral Utility Metric (GSUM). In particular, the methods are applied to a target detection problem using data from the airborne HYDICE instrument collected at Forest Radiance I. While the GSUM approach is seen to lead to intuitively pleasing results, its sensitivity to image parameters was not seen to be consistent with previously published approaches. However, this likely resulted more from limitations of the previous approaches than with problems with GSUM. Further studies with additional spectral imaging applications are recommended along with efforts to integrate a performance predication capability into the GSUM framework.
Published approaches to assessing and predicting spectral image utility are generally based on regression methods which fit coefficients to an equation with terms representing spatial scale, spectral fidelity, and signal-to-noise. Such approaches are patterned after the National Imagery Interpretability Rating Scale General Image Quality Equation (NIIRS GIQE) designed for use with remotely-sensed panchromatic imagery. Preliminary testing of these approaches suggests that they will work for some subsets of spectral imagery applications but are not generally applicable to all spectral imaging problems.
We present here an approach that gets at the heart of the general problem−assessing the confidence of an image analyst in performing a specified task with a specific spectral image. While applicable in other areas such as health imaging, our approach to spectral utility assessment is presented in this paper from a remote sensing point of view. Our approach allows trade-offs in tasking and system design across the “spectrum” of imagers including panchromatic, multispectral, hyperspectral, and even ultraspectral.
Our approach is based on a fusion concept called “semantic transformation.” We assume that spectral and spatial information are largely separable with both contributing to the overall utility of the image. The “semantic transformation” combines the spatial and spectral information in a common term (in our case <i>confidence</i>) to give an overall confidence in performing the specified task.
Addressing the spatial and spectral information separately allows us the freedom to assess the information contained in each in ways that the information is actually assimilated (i.e., usually spatial information in exploited visually while spectral information consisting of more than three or four bands is usually exploited by computer algorithms). For the spectral information, we can use either generic exploitation algorithms or the specific algorithms that the image analyst would be expected to use.
Testing of our approach was done with a parametric set of simulated imagery where Ground Sampled Distance (GSD) and the number of spectral bands were varied. Our initial test led to some refinements of our approach, which are discussed.
When imaging data is collected using airborne remote sensing systems, it is common that the image quality (IQ) of the collected data is not uniform over the entire region of collection. This non-uniformity of IQ is often a limiting factor to the utility of collected data. It would therefore be useful to have a mechanism to predict, assess and manage the non-uniformity of the IQ of remote sensing data both before and after data collection. A mechanism is proposed to model spatially and temporally varying IQ aspects of an imaging collection as a matrix across the region of collection. Within this framework an image quality metric such as a NIIRS based IQE or other IQ predictor is applied to the matrix of parameters, thus sampling IQ such that a 'map' or 'picture' of image quality is created. This allows specific knowledge of IQ performance at particular locations in an image, allowing better resource management when multiple targets with separate collection requirements are collected in the same imaging event. Application to mission planning and optimization of system resources under contingency operations, such as when a system must operate in a degraded state, are also discussed.
Hyperspectral imagers sample the electromagnetic spectrum at greater resolution than more traditional imaging systems, which result in a higher band-to-band correlation and greater amounts of data. With bandwidth limitations on the communications channels and storage space, intelligent system design, band selection, and/or data compression will be very important. The data from a new hyperspectral sensor, SEBASS, which collects data in the thermal IR was characterized for compression. As expected, it was found that the data's spectral characteristics were very dependent on scheme content and the collection time of day. It was found that the band-to-band correlation was greater in this data than either HYDICE or AVIRIS hyperspectral data. Compression ratios of 7:1 lossless and 20:1 with minimal loss were achieved compared to 3:1 lossless and 7:1 lossy for HYDICE and AVIRIS data. This increase in compression is directly attributable to the increase in band-to-band correlation. Unique characteristics of the thermal IR hyperspectral data is also discussed.
It is common practice in digital imaging to apply a spatial modulation transfer function compensation (MTFC) function as a convolution filter to accomplish image sharpening. MTFC in the spatial domain is applied to back out blurring introduced by the various MTF degraders in the image chain. Analogously, in hyperspectral imaging, there is generally a blurring in the spectral dimension due to overlapping spectral bands. This blurring effect can cause narrow-band absorption features to become less apparent when a material is imaged. In a recent study at Kodak, we showed that a hyperspectral signature can be 'sharpened' in the spectral dimension by developing a set of convolution kernels that effectively reduce the overlap among the spectral responsivity of the detectors (i.e., using an appropriate convolution kernel that effectively narrows the spectral responsivity of a detector). Our initial simulations have shown that the main limitation of this technique is its performance in noisy conditions.
By their very nature, hyperspectral imagers collect much more data per pixel than more traditional imaging systems. With bandwidth limitations on the communications channels and storage space, intelligent system design, band selection, and/or data compression will be very important. In two recent government-funded studies (completed in Dec. 1996), Kodak developed two preliminary compression options for hyperspectral imaging. As part of these studies, the band-to- band data correlation structures for both AVIRIS and HYDICE hyperspectral imaging systems were evaluated. Some surprising results were noted that have important implications to system designers.