This lab exercise was designed to give experience to the class in correcting atmospheric interference in remotely sensed images. It involves performing both relative atmospheric correction and absolute atmospheric correction of remotely sensed images. The methods empirical line calibration, dark object subtraction, and multidate image normalization were used to perform the atmospheric correction.
Methods:
Empirical Line Calibration:
Empirical line calibration (ELC) is a method of performing atmospheric correction which forces remotely sensed data to match in-situ spectral signature requirements. These spectral requirements are found by using a spectral library. In this lab a spectral library was used to perform ELC on a 2011 image of the Eau Claire area.
The first step was to bring the image into ERDAS Imagine and then open up the Spectral Analysis Work Station tool. From there, the image was loaded into the tool and the atmospheric correction tool (Figure 1) was opened to begin collecting samples and referencing them to features within the spectral library. Points were placed on the image in certain areas and then referenced to an ASTER spectral library and a USGS spectral library. An example of a point selected was a point in the middle of Lake Wissota; this was referenced to a tap water feature in the ASTER spectral library, as tap water was the only freshwater feature available. Another example was asphaltic concrete (Figure 2). This example helps show the limited capabilities of the ELC method.
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| The atmospheric adjustment tool can be used in ELC to find points and relate them to land surface features in a spectral library. In this case the features used were asphaltic concrete, pine wood, grass, alunite AL706 and tap water. (Figure 1) |
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| The spectral reflectance between the selected point in the image determined to be concrete and the selected reflectance in the spectral library can be seen in this graph. From this point an equation was developed to bring the taken point closer to the expected reflectance in the spectral library. (Figure 2) |
After all of the points were selected and referenced to some sort of spectral signature in the library, equations were developed by the tool to bring the reflectance in the image closer to the expected reflectance from the libraries. The regression equation developed was then ran by running the preprocess-atmospheric adjustment tool. Saving the preprocessed image was the last step in completing ELC to correct for atmospheric interference.
Enhanced Image Based Dark Object Subtraction:
Enhanced image based dark object subtraction (DOS) is a relatively robust method of correcting for atmospheric interference. DOS involves first converting the image to an at-satellite spectral radiance image and the second involves converting the at-satellite spectral radiance image to true surface reflectance. This process was performed using the same image from 2011 of the Eau Claire area as in the first part.
Model maker (Figure 3) played a large part in running DOS on the image. The first step was to use the equation given to convert every band of the image separately into an at-satellite spectral radiance image. Each band was brought into the model maker and had the equations run on them.
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| The model maker window with all of the inputs, equations, and outputs is shown here. This model was run on all six of the bands of the Eau Claire 2011 image to convert them at at-satellite radiance images as the first part of DOS requires. (Figure 3) |
From here, model maker was once again used to convert all of the radiance images into true surface reflectance images (Figure 4). The information needed for the equations, such as the pixel values and atmospheric transmittance from ground to sensor was all either obtained from the metadata, available online, or given. At this point all of the layers were stacked to create the final true surface reflectance image.
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| This is a look at the equation to convert the radiance image of band one into a true surface reflectance image. Completing these equations for each band of the image was a rather painstaking and involved process as different values exist for every band. (Figure 4) |
Multidate Image Normalization:
Multidate image normalization is a relative atmospheric correction method that is normally used when it is impossible to obtain in situ measurements to perform atmospheric correction or when metadata isn't available for an image. It is used to normalize interference between two different images taken at different dates. Multidate image normalization is mainly used for image change detection.
This process was run on images from the Chicago area. One of the images was from 2000, while the other was taken in 2009. The first step was to open up spectral profile plots to gather pseudo-invariant features (PIFs) which are like ground control points in a way. These PIFs were gathered in each image, in the same spot, and only over features that experienced very little change such as airports or water (Figure 5). The spectral reflectance of the different points can then be viewed in the spectral profile windows (Figure 6). A total of fifteen PIFs were gathered in this case.
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| All of the points were selected in the same spot in both images. A total of fifteen points were selected over features that would've experienced little to no change between 2000 and 2009, such as airports and water features. (Figure 5) |
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| The spectral signatures for the fifteen PIFs in both images can be seen in the two spectral profile viewers here. (Figure 6) |
At this point, the data from the bands of each of the fifteen PIFs was extrapolated and brought into Excel (Figure 7) to graph (Figure 8) and find the equations to normalize the two images. Image normalization correction models (Figure 9) were then developed to generate the final products. The band layers were then stacked to complete the process.
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| This is data on the reflectance of all of the PIFs taken in the various bands. From here graphs were made in order to find the necessary equations to convert each band. (Figure 7) |
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| This is an example of one of the graphs generated using the PIF data. This graph is of band one and the line equation was used to run the model to create the normalized image. (Figure 8) |
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| This model was made in order to finish the image normalization. The equations in the models were created from the equations generated in the Excel graphs. (Figure 9) |
Conclusion:
Most processes and analysis in remote sensing applications cannot be performed without first ensuring that the images to be used have low error. This means that atmospheric correction is a hugely important topic that applies to almost all remote sensing applications. This lab was an excellent way to introduce and compare several techniques of performing atmospheric corrections. The most robust seemed to be DOS, however it also requires the most in situ data. In the end, the atmospheric correction technique used depends on data available and of course, the task at hand.
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