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Lab #5 Data Fusion Name:

Lab #5: FOR 504 Advanced Topics in Remote Sensing Objectives of this laboratory exercise: Use IDL/ENVI to:

• Learn how to fuse different satellite sensor data sources • Learn how to analyze fused data • Compare the classification of fused and unfused data

The questions provided within this lab are designed to help the student better understand the practical details of programming in IDL and are recommended but are not for assessment. Location: RS/GIS Lab Login: XXXX Password: XXXX

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Before you start: Double click the ENVI icon on the desktop: This starts both ENVI (The Environment for Visualizing Images) and the IDL (Integrated Development Language) programming interface Ignore IDL but don’t close it as this closes ENVI as well. For this lab, we will be using Data from the ENVI online tutorials.

PART A – DATA FUSION Task # 1 Viewing the TM and SPOT images to Fuse Data fusion is a technique that allows multiple layers from different images (satellite, aerial, etc) to be combined into a single composite image. The reason for such an unusual procedure is to allow enhancements of the data (e.g. one such enhancement could be to use the spectral characteristics of one sensor with the spatial-resolution of another). In this example, we will follow the ENVI online tutorial that demonstrates the fusion of TM and SPOT imagery. The goal is to maintain the color information from TM, but to enhance the spatial resolution to that from SPOT (i.e. 30 m to 10 m). Prior to data fusion in ENVI, the data must either be (i) georeferenced; or (ii) cover the same geographic area, with the same pixel size, and image properties. In this ENVI exercise, we have to use route (ii). In general, when resampling to images to the same pixel size, you should always resample the low spatial-resolution image to the same pixel size as the high spatial-resolution image, such that no image data is lost. From the Main ENVI toolbar select File/Open External File/IP Software/ER Mapper and using the Browse select the file lon_tm.ers from the C:\ATRS directory: Repeat this for the file lon_spot.ers. You will notice that in the Available Bands list that the tm image has only R, G, and B bands, whilst the SPOT image has only one band If you display these images side by side, you will notice that although the TM image is in color, the gray-scale SPOT image contains much more detail. What we want to do with is exercise is to keep the color of the TM but use the spatial-resolution of the SPOT image.

ENVI 4.0.lnk

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TM ‘RGB’ image SPOT image Task #2 Resize the TM pixels to SPOT pixel sizes From the Dims window in the Available Bands List, you will notice that the image size of the TM image is 1007 x 560, while the SPOT image has an image size of 2820 x 1569. In addition, in this example the TM has an image size of 28m, while the SPOT image has a spatial resolution of only 10m. Therefore, in this exercise the TM image needs to be resized by a factor of 2.8 to create 10m data equivalent to that of the SPOT data. From the Main ENVI menu bar, select Basic Tools/Resize Data (Spatial/Spectral) and select the lon_tm.ers image. As we want to resample all the areas and spectral bands, just click ok. To resize the data enter a value of 2.8 into the xfac box and a value of 2.8009 into the yfac box:

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The 2.8009 is used to add an extra pixel to the end of the image, such that they match exactly. Choose a sensible filename and press ok. Load you resized image into the display window containing the TM image and then link the TM and SPOT displays. Refer back to Lab 1 on how to link images (HINT: look under Tools in the Main image Window). You will now notice that the TM image is ‘blocky’, in that a couple of pixels are now preset where one was present before. This is because we are now simulating a 10m TM image, but don’t actually have any additional data.

Task #3 Manual Hue Saturation Intensity Data Fusion Hue Saturation Intensity (HSV) or Hue Saturation Value (HSV) transformations are commonly used in image processing as a route to image enhancement. For data fusion, this transformation is used in order that we can ‘switch’ the Intensity or Value band of the TM with the SPOT image. First we need to perform the HSV transformation on the TM image, so from the Main ENVI toolbar, select Transform/Color Transform/ RGB to HSV, then select the resized TM image

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and press ok after providing the computer with a sensible filename for the output in the RGB to HSV Parameters window. The next step is to convert the values of the SPOT image such that they are the same type of data as with the value band of the transformed TM image. To do this we need to stretch the SPOT pixel values between 0.0 and 1.0. From the Main ENVI menu bar, select Basic Tools/Stretch Data, select he SPOT file and press ok. In the Output Data Range boxes, put the minimum as 0.0 and the maximum as 1.0. Finally, give the output a sensible file name and press ok. Okay, now we do the cool bit. From the Main ENVI menu bar, select Transform/Color Transforms/HSV to RGB and select the TM Hue and Saturation bands for H and S; but select the stretched SPOT image as the V band. Now press ok and choose a sensible file name to perform the inverse transformation: As you can now see the ‘modified’ TM image now has the spatial-resolution of the SPOT image. This technique is simple but very useful.

Task #4 Automated HSV Fusion In ENVI, this task can be automated as long as you have the two images resized to the same pixel size. First Display the RGB TM resized pixel file in Display 1. From the Main ENVI menu bar, select Transform/Image Sharpening/HSV. In the Select Input RGB window select Display # 1 and press ok. Then in the next window select the SPOT un-stretched image and press ok. Finally, choose a sensible filename and press ok. If you compare the manual to the automated outputs, you will see that they are the same. When possible use he automated technique, as we only used the manual in order that you could ‘see’ what was happening within the automatic ‘black box’. Now for an example closer to home – We are going to fuse TM and IKONOS data from Moscow Mountain. Task # 5 Fusion of Landsat ETM+ and Panchromatic data In this task you will fuse Landsat ETM+ (30m) data with the Landsat ETM+ panchromatic (15m) band and evaluate how the spectral properties of the different land surface classes change. First, from the Available Flies List select File/Close all Files. Next select File/Open Image File and select two images: Landsat_tm_fuse AND Landsat_pan_fuse As you can see this is imagery of burned area in southern Africa. From the Image Menu, select tools/region of interest/ROI toolbox. As you have done in previous labs create regions of interest for the main cover types of:

A

B

C

E

D

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- A: Urban - B: Burned Area - C: Green Healthy Vegetation - D: Senesced Vegetation - E: Soil

From the ROI Menu select Options/Stats for All Regions. For each class Type the Means and standard deviation values into excel and calculate the M-Statistic. Next from the ENVI Main Menu select Transfrom/Image Sharpening/Gram-Schmidt Spectral Sharpening For the Low Resolution Multi-band File select the Landsat_tm_fuse file and press ok. For the High Resolution Pan Input File select the Landsat_pan_fuse file and press ok. At the Gram-Schmidt dialog box use the defaults, choose a suitable filename, and press ok. Wait a couple of minutes… After the image is produced – load it into a new display and create some new ROIs (of the same areas as before) As before from the ROI Menu select Options/Stats for All Regions and for each class Type the Means and standard deviation values into excel and calculate the M-Statistic. Finally, plot each spectra against its fused partner with the standard deviation as the Y-axis error bars. Now please answer the following question: Has the Grams-Schmidt Process improved or degraded the quality of the Landsat image? Please explain and give potential reasons for your answer (Hint: consider both spectral fidelity and image detail):

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Task # 6 Classification and comparison of fused and unfused Landsat ETM+ data First, make sure you have both the unfused Landsat_tm_fuse file and the fused file open in viewers. We are going to use the regions of interest you previously created to produce a supervised classification. From the Main ENVI menu bar select Classification/Supervised/Maximum Likelihood. From the Classification Input File Dialogue Box select the Landsat_tm_fuse image and press ok. Then press ‘select all items’, put No for rule images, and choose a suitable file name. Then press ok. This image is now your ‘classified unfused image’ Now, so that we can easily compare this image to the Pan-based classification, from the ENVI menu bar select Basic Tools/Resize Data choose the image you just created and press ok.

In the Resize Data Parameters Dialogue Box double the number of x and y pixels to 5000. This makes this classified image the same size as the fused dataset. Now re-run the supervised classification but this time on the fused Landsat image using the second set of ROIs you created. For the last stage of our analysis we must create a ‘truth’ image or ‘truth set of ROIs’. To do this view the fused Landsat image and on the ROI toolbox delete all current ROIs. Next as best as you can select a set of new ROIs from completely different areas than the first set were collected.

Next, from the Main ENVI menu bar select Classification/Post Classification/Confusion Matrix/Using Ground Truth ROIs.

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In the Classification Input File Dialog Box select the classified unfused image and press ok. Next match-up each ROIs and press ok. Repeat this for the fused tm image and answer the following question: Has the Grams-Schmidt Process improved or degraded the quality of the Landsat-based classification image? Please explain and give potential reasons for your answer: