Lab 1: Image quality assessment and statistical analysis

Introduction:

In order to properly use multispectral data for land use/land cover classification the data must first be preprocessed to identify and remove sources of redundancy. I used two different methods to identify problematic band combinations, feature space imaging and correlation analysis.

Methods:

Feature space imaging illustrates the pixel values of two images by plotting them on a scatterplot graph. Band combinations with high association will appear as a cohesive line across the graph (Figure 1). Band combinations with low association will display points widely spread across the surface of the scatterplot graph (Figure 2). I used feature space imaging to assess the covariance values of Landsat imagery of Eau Claire, WI.

Figure 1: High covariance between bands 2 and 3

Figure 2: Low covariance between bands 4 and 5

Correlation analysis compares bands with one another and assesses the extent of association between each band combination. The output of correlation analysis is typically displayed as a correlation matrix. The cell values indicate the extent of interrelationship, with higher values (-1 and +1) indicating extremely high association, and lower values (~0) indicating low association. If two bands are highly correlated, one of them should be removed, to preserve the integrity of the analysis. I used correlation analysis to assess covariance of bands in Landsat imagery for Eau Claire, WI, and Quickbird imagery for the Florida Keys and Sundarbans, Bangladesh.

Results:

After creating the feature space plots, I deemed the removal of bands 2 and 7 necessary in order for proper analysis to be performed. Band 2 had high covariance with both band 1 and band 3 (Figures 1,3). Band 7 had high covariance with band 5, indicating one of them needed to be removed. Band 5 and band 4 had the lowest covariance of any band combination, which convinced me to eliminate band 7 rather than band 5.

Figure 3: Bands 1 and 2 have high covariance

The Eau Claire correlation matrix verified my band removal assessment from the feature space plots, as it indicated bands 2 and 7 have the highest correlation values (Table 1).   The correlation matrix for the Florida Keys revealed high correlations between the bands 1&2 … suggesting the removal of band 2 (Table 2). The correlation matrix for Sundarbans, Bangladesh revealed a similar pattern, with band combinations 1&2 having high correlations (Table 3).

Table 1: Eau Claire Correlation Matrix

Table 2: Florida Keys Correlation Matrix

Table 3: Sundarbans Correlation Matrix

Sources

Landsat satellite image is from Earth Resources Observation and Science Center, United States Geological Survey. Quickbird high resolution images are from Global Land Cover Facility at www.landcover.org