Lab Instructions Lab 3, due 5: 30 pm September 25, 2007 ees5053/ES4093: Remote Sensing, utsa student Name

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Lab Instructions

Lab 3, due 5:30 pm September 25, 2007
EES5053/ES4093: Remote Sensing,

Student Name: ___________________

Digital Image Processing I: Atmospheric Correction, Radiance, Reflectance, NDVI from Landsat Image

Objective: In this lab, you will learn the basic procedure of digital image processing from creating Region of Interest (ROI), Cloud Mask, Atmospheric correction (DOS)

Part I: Concepts and short questions:

Atmospheric scattering:
Absorption and major atmospheric windows for remote sensing on Earth surface:
Reflectance, spectral reflectance, albedo
Explain why we see blue sky and why we see orange and red sky in sunset
Solid angle and radiance
Explain why we need to do atmospheric correction in remote sensing
Part II
1. Preparation:

(1). Copy the data directory Lab3 in the server (\\\XIE_misc\Fall2007-RS\ ) into c:\Fall2007-RS\YourName\. Please always remember to output your results to your lab directory (i.e. Lab3 for today’s lab) not to the default ENVI directory.

(there is another way to get the lab data from the server without physically copy the data to your local computer. If you want to know how to do that, please ask me).
(2). Today we will use a new image (NE_p27r40_020708_12347). Open this image as R(b7), G(b4), B(b2). This is the image acquired on July 8, 2002, just after the big flood event of 2002. In the succeeding labs, you will classify and compute the flooding areas.

2. Atmospheric Correction

In the lectures, you learn a lot about the interactions between atmosphere and EMR. And you know that the remotely sensed radiance includes two parts: one from the target area (which is what we want), the other one is from the path (path radiance, which is what we do not want). The process to remove the path radiance is called atmospheric correction. There are two types of atmospheric corrections: (1) absolute atmospheric correction: radiative transfer-based atmospheric correction and empirical line calibration and (2) relative radiometric correction: Dark Object Subtraction (DOS) and multiple-data image normalization using regression. In this lab, we will do a simple DOS correction.

The principals of DOS includes (1) find the darkest object in the image; (2) assume that its spectral reflectance should be all zero (target radiance); (3) the measured values above zero are assumed to be the atmospheric noise (or path radiance) and uniformly distributed on the image area; (4) subtract the path radiance from each pixel radiance of the image, then we should get a relatively atmospheric free image. Usually these dark objects are water bodies (see below figure 1, the fresh water has very low reflectance, meaning water absorbing most of light, especially after 0.75 µm).


Figure 1. Spectrum signature of different objects

In ENVI, the DOS is called Dark Subtract, click Basic Tool -> Preprocessing -> General Purpose Utilities -> Dark Subtract -> select the NE_p27r40_020708_12347.tif image as the input file, click OK, select the Band Minimum, which means that the minimum value of each band will be automatically selected, and then this value will be subtracted from all pixels in this band. Output the new image to Memory or your lab directory and name it as DOS_ 020708.tif. In this image, the darkest objects should be the water bodies in lakes in the southern San Antonio.  After this correction, the resulting image is ready for the following steps.

Question 1: Compare change of the statistic results before DOS (NE_p27r40_020708_12347.tif) and after DOS (DOS_020708.tif).

3. ROI tool
When you upload this image (DOS_020708.tif) as R(b7), G(b4), B(b2), you will see there is lots of cloud (white or dark pixels). For these pixels, we must mask them as no data.
Click Basic tools->Region of Interest (ROI)->ROI Tool, then a ROI window popup as figure 2, check Image window as the ROI map window. Click ROI Type, check Polygon, which means your ROI shape is a or many polygons. Move you mouse over the Cloud area, then press down the left key of your mouse, draw a polygon around the cloud, then double right click your mouse, the polygon will be filled. After you mask all of the cloud pixels, click File-> Save ROIs, select your ROI output directory and save as Cloud_020708.roi.

Figure 2. The ROI tool
3. Cloud Mask
We will use ROI in step2 to build our cloud mask. Click Basic tools-> Mask->Build Mask, click Option->import from ROI (Note: check Selected Area Off), and select the Cloud_020708.roi as the input file, then output as mask_band.
Click Basic tools-> Mask->Apply Mask, select DOS_020708.tif as the input file, select mask_band as the mask band, Output to file: Mask_DOS_020708.tif.
Question 2, Compare the statistic results from question 1 with statistics after Cloud mask.

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