Lab 12: Hyperspectral Remote Sensing

Background and Goal

The objective of this lab is to gain experience with the processing of hyperspectral remotely sensed data. Because hyperspectral images have many bands with specific wavelengths, it is common for several of the bands to be corrupt or incorrect from atmospheric influences as well as sensor error. It is necessary to detect these noisy bands and remove them from the processing. We then learned how to detect target features from the hyperspectral image.

Methods

For this lab we used Airborne Visible/Infrared Imaging Spectrometer (AVIRIS) data of a geologic field site in Nevada. The image consisted of 255 bands. Anomaly detection was used to compare results between an image that was not preprocessed and an image that had bad bands removed. Anomaly detection identifies pixels in the image that have a significant deviation of spectral signature compared to the rest of the image. First, we did an anomaly detection of the AVIRIS image without removing the bands. Then, we used the Bad Band Selection Tool to identify the bad bands within the image. Figure 1 shows this tool, with the bad bands highlighted. A total of 20 bands were removed. The two outputs of the anomaly detection function were then compared for differences.

Figure 1: The bad band selection tool, with the bad bands highlighted in red.

Next, we explored the use of target detection. This process creates a mask that highlights instances of a given spectral frequency (the target). For this lab, the target was the mineral Buddingtonite. First, we used a simple target detection method that used a spectral signature that was collected from a classified image. This would create a mask in much the same way the anomaly detection did, highlighting areas with the same spectral signature. Next, we used a spectral signature from the USGS spectral signature library and analyzed the two outputs for differences.

Results

Figures 2 and 3 show the anomaly masks for the image without bands removed and the image with bad bands removed, respectively. There isn’t too much of a difference just by looking at them, but by using the swipe tool in the spectral analysis workstation, a difference was determined. After removing the bands, the anomalies were highlighted in the same area, however they were a little larger.

Figure 2: The anomaly mask of the image without the bad bands removed. 

Figure 3: The anomaly mask of the image with the bad bands removed. The anomalies are in the same spot, though they are slightly larger.

Figure 4 shows the results of the target detection overlay. There was an error with the simple target detection which just created a grey box for the target mask. The target detection with the USGS Spectral library signature worked just fine though. I’m guessing that the results of the simple target detection would show more spread and peppering, as the spectral signature was based off of a classification image.

Figure 4: The results of the overlay comparing the simple target detection and the spectral library target detection. There was an error with the spectral signature in the simple target detection, leaving only a grey box.

Sources

Erdas Imagine. (2010). [AVIRIS hyperspectral image]. Hexagon Geospatial. Obtained from Erdas Imagine 2010.