Hyperspectral Data Processing

For effective utilization of Hyperspectral sensors data sets, different kind of processing and analyzing techniques are required for various applications. All the Hyperspectral sensors developed have enabled generation of remotely sensed laboratory spectra of various materials such as rocks, soils, plants, snow, ice, water and man-made materials. These laboratory quality spectra have been used to obtain compositional information of the earth surface as they are able to detect absorption features caused by minerals in visible, SWIR and TIR region of electromagnetic spectrum. AVIRIS sensor by NASA JPL has been used especially for the mapping of cations and anion for identification of various minerals and rocks. The large amount of spectral information in hyperspectral data is useful for species level discrimination by identifying components unique to certain species of plants. This hyperspectral technology also provides a means for optical oceanographers to classify and quantify complex oceanic environments.
Data Pre-Processing Techniques
Sensor error correction
Most of the hyperspectral sensors like Hyperion are pushbroom scanners in which poorly calibrated detectors produces vertical bad lines on the image. Due to the poor calibration the bad lines is having different values then neighboring pixels either the values are constant or lower than the neighboring values. These bad lines can be corrected by replacing their DN values with the average DN values of their immediate left and right neighboring pixels because of the high spatial correlation. Atmospheric correction
The atmosphere scatters some of the electromagnetic energy which travels from the sun to the Earth’s surface and from Earth’s surface to the sensor. Therefore, the electromagnetic energy received at the sensor may be more or less than that due to reflectance from the earth’s surface alone. Atmospheric correction attempts to minimize these effects on image spectra. Atmospheric correction is traditionally considered to be indispensable before quantitative image analysis using hyperspectral data. Various atmospheric correction algorithms have been developed to calculate concentrations of atmospheric gases directly from the from hyperspectral data. Atmospheric correction is divided into two types: Relative and absolute methods.
01.Relative method
is divided into three types
a. Flat field correction
b. Empirical line correction
c. Internal apparent relative reflectance correction
2. Absolute atmospheric correction.
This method is based on some atmospheric correction models which require the information regarding the atmospheric condition, altitude, geometry between sun and the satellite, aerosol level, water absorption, time of acquisition of the image and more details. The absolute atmospheric correction methods have the advantage over other methods that these can be run under any atmospheric condition. Some of them are
a. FLAASH (Fast Line-of-sight Atmospheric Analysis of Spectral Hypercubes);
an atmospheric correction modeling tool in ENVI for retrieving spectral reflectance from hyperspectral radiance images. FLAASH incorporates the MODTRAN 4 radiation transfer model to compensate for atmospheric effects.
b. ATCOR (Atmospheric and Topographic CORrection).
c. ATREM (Atmospheric REMoval Program)
d. ACORN (Atmospheric CORrection Now)