Hyperspectral Remote Sensing

- Introduction -

The last topic of the semester is an introduction to hyperspectral remotely sensed imagery. Over the course of the semester, multispectral imagery (ex. Landsat TM) has been used for different types of analysis. Hyperspectral imagery (ex. AVIRIS) differs from multispectral in the number of bands and range of the electromagnetic spectrum covered. Typically, multispectral imagery will have under 15 or so bands that cover broader ranges of the electromagnetic spectrum in multiple spectral ranges (ex. VIS, NIR, SWIR, and MWIR). Hyperspectral imagery however, can have hundreds of bands that range in a single spectral channel, allowing for much more distinction in specific land surface features. In this lab, bad band removal, anomaly detection, and target detection will be explored.

- Methods -

Image 1: The largest viewer shows both the anomaly mask
and the original image with a swipe function for comparison.

Both anomaly detection and target detection were performed on AVIRIS imagery in Erdas Imagine 2010 with all bands and again with bad bands excluded, for comparison. Anomaly detection was performed by navigating to Raster > Hyperspectral > Anomaly Detection and following the steps of the Anomaly Detection Wizard. After the anomaly mask had been created it was opened in the Spectral Analysis Workstation and compared to the original image (Image 1). This first mask was created using all bands. The process was then repeated with bad bands excluded through an additional step in the Anomaly Detection Wizard.




In the Bad Band Specification window in the Anomaly Detection Wizard, Bad Band Selection tool (Image 2) was opened. In the Bad Band Selection tool, all 224 bands of the AVIRIS image could be cycled through and analyzed by their individual histograms and mean plot window. Bands with multimodal histograms and visible differences in the mean plot window signified bands with low signal-to-noise ratio and should not be included in analysis. These bands were then singled out and saved as a bad band list file that was then used in both anomaly detection and target detection later on.

Image 2: Tool used to select bad bands. Areas of red in the
mean plot window indicate selected bad bands.

Target Detection was performed by navigating to Raster > Hyperspectral > Target Detection and following the steps of the Target Detection Wizard. The first target detection used a custom derived Buddingtonite spectrum library file and used all 224 bands. The second target detection used the USGS Buddingtonite_NHB2301 spectrum library file and excluded the bad bands designated earlier. The resulting target masks were then compared (Image 3).



Image 3: The largest (main) viewer shows the target detection mask
and the original image for comparison.

- Discussion -

In both the anomaly detection and target detection, masked area increased after the bad bands were excluded. The size of the masked areas typically increased and new masked areas were created. This illustrates that using bands with low signal-to-noise ratio resulted in less accurate results. The results of the anomaly detection could be further improved by using only a subset of the image instead of the whole image. This is because the anomalies are distinguished based on an estimated background spectra of the area used. By using a smaller area, more anomalies could potentially be identified. The detection of the specific mineral Buddingtonite would not be possible with multispectral imagery and illustrates the advantages of hyperspectral data in collection of specific land cover types.

- Conclusion -

The selection of bad bands is typically not essential for analysis of multispectral imagery, but as this lab has demonstrated, is essential of hyperspectral imagery. Band bands in hyperspectral imagery can result from atmospheric effects or sensor malfunction. Band histograms can be used to identify the bad bands with low signal-to-noise ratio that should be excluded from analysis. Hyperspectral data is useful for examining specific wavelengths which help analysts determine land cover types and calculate band ratios with far more specificity.

- Sources -

Erdas Imagine, 2010. Modified Spectral Analysis Workstation Tour Guide.