No Lo Se

Digital Image Classification
Geography 4354 – Remote Sensing

Lab 11
Dr. James Campbell
December 10, 2001

Group #4
Mark Dougherty
Paul Bartholomew
Akisha Williams
Dave Trible
Seth McCoy

Table of Contents:
Table of Contents: ............................................................................................................... 2
Introduction......................................................................................................................... 3
Overview of Procedure........................................................................................................ 3
Preliminary procedures ....................................................................................................... 4
Training Sets................................................................................................................... 4
Signature Editor............................................................................................................... 5
CLASSIFICATION ............................................................................................................ 6
Unsupervised Classification............................................................................................ 7
Supervised Classification ................................................................................................ 7
Non-Parametric Classification Techniques: ................................................................ 7
Results............................................................................................................................... 10
Error MatricesLand-use Percentages As Classified ...................................................... 12
Land-use Percentages As Classified ............................................................................. 13Conclusion......................................................................................................................... 13

2

Introduction
The purpose of this lab was to allow students an opportunity to gain hands-on experience
in digital image classification. Government and private agencies use image
classification, such as that presented here, as a tool in urban planning, in policy
development, and in implementing laws dealing with present and future land use
The objective was to classify the SWquarter of a Landsat TM image that approximately
covers the area of the USGS 15-minute Radford Quadrangle (Figures 1 and 2).

Figure 1 - Landsat scene of area approximating
much of the area represented in the USGS
Radford 15 minute quadrangle. Image is 1024
x1024 pixels.

Figure 2 - Subset of original nrv.img image. The
area covers the 512 x 512 area of pixiels in the SW
quarter of thenrv.img.

Overview of Procedure
The image subset was classified into four distinct classifications; 1) urban, suburban or
bare (fallow), 2) Agriculture, 3) Water, and 4) Forested. ERDAS Imagine 8.4 was used
in manipulating the image and creating the classification and analysis. Areas that
appeared to be related to the land types of interest where digitized using the Area Of
Interest Tool (AOI)and added to a signature file. Each digitized area was added to the
program’s signature editor and checked to determine if the area had a unimodal leptonic
distribution for its brightness signature. A unimodal distribution indicates that the
reflectance values for the AOI likely comes from one type of land feature, such as forest
or cropland. Once various AOIs were chosen and checkedstatistically, the signatures for
the various AOI’s were merged into the four classification types as described. These
classifications used for training sets for the final supervised classification procedure.
Various algorithms were run for the final supervised classification and compared to one
another for accuracy. Microsoft Excel was used in the analysis of Error matrices used to
check the...