Instructor:
Office:
Phone:
Email:
Dr. Y.Q. Wang
Coastal Institute Building - Kingston, Room 027
(401) 874-4345
yqwang@uri.edu
___________________________________________________________________________
NRS516 Remote Sensing in Natural Resources Mapping
Lab 5. Supervised Image Classification
Introduction
Classification is the process of sorting pixels into a finite number of individual classes, or
categories, of data based on their data file values. Unsupervised and supervised classifications
are two general approaches in digital image classification and thematic mapping process. In
supervised classification, image analyst has more control of the land cover classes than
unsupervised classification. By selected training signatures, the software system will be able to
identify and group pixels that share similar spectral characteristics as classes of land cover types.
Objectives
1. Understand the concepts of thematic information extraction from remotely sensed data by
selected training signatures.
2. Learn how to create and examine training signatures.
3. Practice “supervised classification” using ERDAS Imagine maximum likelihood algorithm.
Procedures:
Creating Signatures
1. Use the same dataset as the unsupervised classification lab exercise
(etm_1999oct27_multispectral.img), which is a subset of Landsat ETM+ image acquired in
October 27, 1999 that covers a portion of RI. Display the image in a preferred band-color
combination from which training signatures can be easily identified and selected.
2. In ERDAS Imagine, select “Raster-Supervised-Signature Editor” tool and the Signature
Editor dialog will show up.
3. In the Raster-Drawing panel, select the “Polygon” tool and define a small polygon over a
lake water area. Upon finishing the polygon (by double click the mouse button during the
digitizing) the defined polygon will be high-lighted. In the Signature Editor, click the
“Add” tool to add the selected pixels within the polygon just defined to be a candidate
training signature. The statistics, such as Count as the number of pixels included in the
polygon, will be displayed in the Signature Editor. Give a name and a color to this category
(e.g., Water with a blue color).
4. In the AOI-Drawing panel, select the “Grow” tool, then move the mouse cursor to a
location where a relatively homogeneous group of pixels displayed for the type of land cover
category that you can identify, and click the mouse button as to put in a seed location, the
Grow result will be displayed surrounding the location that you selected. The growing area
(pixels involved) could be too large or too small (too many or too few pixels selected). You
may change or control the growing property by redefining the region growing properties. To
do so, select AOI-Drawing-Growing Properties” to start the Growing Properties dialog.
Redefining the Area with less number of pixels (e.g., from 10000 to 300) and the spectral
Distance (e.g., from 1000 to 10 or more or less) will provide the control of the number of
pixels involved in signature selection. When finish the growing process, in Signature
Editor, click the Redo button, examine the new selection of pixels, and click Add tool to
add the selected pixels into the Signature Editor as a candidate signature. Examine the
statistics of the selected group of pixels to make sure that the group is appropriate as a
training signature. Then offer a name and color for the category selected (e.g., Forest in a
green color).
5. Repeat the same seed growing process to select at least 10 signatures for different types of
land cover categories. Save your signature files ( .aoi and .sig).
Signature Evaluation
6. In the Signature Editor, select the signature(s) for evaluation. Examine the histograms of any
individual signature and all the signatures as group in Histogram Plot. Examine the
signature overlaps and the separability of all signatures.
7. Examine the Signature Separability using Euclidean Distance measure in the summary
report.
8. If applicable, you may consider modifying your signatures and re-do the supervised
classification.
9. Save the signature file. Create hardcopy of the signature plots with the summary of the
signature separability as part of the lab report.
Run the Supervised Classification
10. Run the classification by either 1. in Signature Editor, select all signatures generated and
click “Classify” and provide an output file name; or by “Raster-Supervised-Supervised
Classification” function by providing file names of input image, output image and signature
file. Then proceed.
11. Examine the classification result of supervised.img from a separate viewer. After examining
the result, modify signatures by the procedures above and re-run the classification process,
save the file in a new name and examine the classification result. Repeat the same process
until a final classification result is obtained.
12. Create a hardcopy map of the final “supervised.img” with the summary of the names and
areas of land cover categories as part of the lab report.
Answer the following questions in the lab report.
1. When and why we conduct supervised imagery classification?
2. What are the advantages of supervised classification (vs. unsupervised classification)?
Lab report due on 03/03/2016 at the beginning of the class.