The Maximum Likelihood Classifier (MLC), used here, quantitatively evaluates both the variance and covariance of a set of feature’s spectral response patterns when classifying an unknown pixel. For the MLC to evaluate both the variance and covariance of a class of spectral patterns an assumption has to be made, that the distribution of the training data point cloud (in the measurement space) is Gaussian (normally distributed), which is a reasonable assumption for common spectral response distributions (Lillesand et al., 2004).
Under this assumption the distribution of a set of pixels can be described by the mean vector and covariance matrix. These parameters enable the maximum like-lihood algorithm to calculate the statistical probability of a pixel belonging to a certain class. Each spectral class has it’s own probability density function see figure 4.6 . For each pixel, the probability density function is used to calculate the probability of belonging to each spectral class, and the unidentified pixel is assigned to the spectral class with the highest probability. A threshold can also be set by the analyst so that pixels with a probability value under that threshold will be classified as ”unknown” (Lillesand et al., 2004).
A maximum-likelihood classification was run on the topographically c-corrected image from 24.07.94. Envi was used to run the classification. The training data was chosen by visual inspection of the satellite image as well as aerial photos and comparisons with the unsupervised classification (60 clusters), to choose the most representative pixels for each class. The supervised classification was run for the 3 grazing classes; Less Good (LG), Good (G), and Very Good (VG).
Before the classification was run, additional map data was brought in and certain areas were masked out of the satellite image. The image had already been masked so that only the areas covered by the 3 NIJOS grazing quality classes LG, G and VG were available. The agricultural areas, lakes and non-permeable areas had
4.5 Supervised Classification 55
Figure 4.6: Illustrations of how the maximum likelihood classifier calculates probability dis-tributions for a class, and assigns a pixel to the distribution with the highest probability.
Copied from Hashimoto et al. (1993)
been masked out (process described in the section 4.2.4). Outlines for the stream network, road network, tractor road network, and houses (points), were added to the image and masked out. These areas greatly affected the classification results as they appeared in the middle of polygons classed as vegetation types. The layers brought in were from the N50 series over Norway and in the SOSI standard. They had UTM zone33, EUREF89 / WGS84 coordinate system and were converted by ENVI4.2 to zone 32 when they were put together with the satellite image.
Error Assessment
A confusion matrix was drawn to assess the accuracy of the classification. The confusion matrix expresses the number of sample units (i.e. pixels) assigned to a particular class relative to ground truth. The columns represent the ground truth data and the rows represent the classification generated from the remotely sensed data. The table indicates the accuracies of each category as well as the errors of inclusion (commission errors) and errors of exclusion (omission errors). The Producer’s and User’s accuracy were calculated. The producers error divides the total number of correct pixels in a class by the total number of ground truth pixels in that class (i.e. column number). The user’s accuracy is calculated by dividing the total number of correct pixels in a class by the total number of pixels that were classified in that class (i.e. row) Congalton (1991).
Chapter 5 Results
This chapter is organised into the following sections; initial class separation, im-age variation, classification, and the visual inspection of aerial photographs. Flow diagrams presented at the beginning of each section illustrate the procedure taken for that group of analyses. The objective behind all analyses was to find a pattern that could relate the brightness values appearing in a Landsat image, to the grazing quality classes defined by the Norwegian Institute for Land Inventory (NIJOS).
5.1 Satellite Images Across Growing Seasons
Different plants dominate at different times of the year because of variations in the growing seasons of individual species. Having images from different parts of the growing season is therefore an asset when assessing vegetation spectral separability and can improve classification (de Colstoun et al., 2003; Pax-Lenny and Woodcock, 1997; Wolter et al., 1995).
The 6 satellite images used in this thesis are shown in figure 5.1. Three spec-tral bands from each image are visualised in red, green and blue. The distinc-tion between the different land cover types (trees, low vegetadistinc-tion, bare rock, and snow) are clearest when shown in a false colour band composite including the Mid Infrared (MIR), near infrared (NIR), and the red band. Varying the band combina-tion is a visual tool and does not affect the data in any way. The dark areas in the images represent small lakes. They appear black because the water both absorbs and specularly reflects all visible wavelengths away from the satellite sensor. The August image has a few scattered clouds which also appear as black in the image.
There are additional dark areas in the October image representing shadow due to
a lower sun angle.
A: 23.05.2004 B: 25.06.1995 C: 24.07.1994 Landsat TM 5,4,3 Landsat TM 5,4,3 Landsat TM 5,4,3
D: 29.07.1999 E: 29.07.1999 F: 17.08.1997 G: 18.10.1999
Landsat TM 5,4,3 Landsat TM 3,2,1 Landsat TM 5,4,3 Landsat ETM+ 5,4,3 Figure 5.1: The Landsat images used ranged from spring to autumn. Images A, B, C, D, F
& G are displayed in a band 5,4,3 combination which means MIR=red, NIR=green and red band=blue. Image E is shown in a true colour combination. Image E & F have the same image data but are displayed in different band combinations. Notice how the thin cloud in the image is more visible in a true colour combination. Notice also the change in vegetation coverage from May - October (i.e. increase in green area from May - July, and decrease in green area from July - October).
The blue colour is represented by the red spectral band in all the images in figure 5.1, except E. The chlorophyll in healthy plants absorbs most of electromagnetic (EM) radiation in the red region. This means that little radiation is reflected back and recorded at the satellite sensor (see figure 2.11) (Lillesand et al., 2004). This is why there is very little blue in most of the images in figure 5.1. Those small areas in the images (e.g. image G) that are blue, however, are from areas that are either covered in bare rock or by snow or ice.
Green is represented by band 4, the NIR band. Healthy plants in this wavelength region have high reflection. The pixels that are strongly green in the false colour