High Spatial Resolution Digital Imagery

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⎧

⎫

∑ ti ri

⎪

⎪

⎪

⎪

α = cos- 1⎨

1/ 2 ⎬

1/ 2

⎛ nb 2 ⎞ ⎛ nb 2 ⎞ ⎪

⎪

⎪ ⎜ ∑ ti ⎟ ⎜ ∑ ri ⎟ ⎪

⎩ ⎝ i=1 ⎠ ⎝ i=1 ⎠ ⎭

where

α = spectral angle

The spectral angle is then calculated 37 times for each image pixel (*t*i) be-

cause there are 37 *r*i reference signatures or classes. The pixel is then assigned to

the class where the spectral angle is the smallest. If all 37 angular estimates are

pixel is assigned to an "unclassified" group.

The results of the initial classification using the SAM algorithm showed

some confusion among the vegetation classes. Also, some pixels (approximately

10% in both mosaics) were not classified. Additional SAM runs using larger

spectral angles (0.090.12) successfully increased the number of pixels that were

classified. However, the confusion among classes was also increased. Therefore,

the results from the 0.08 spectral angle were retained for the final class maps.

then applied to the unclassified image pixels. This algorithm uses the class-

conditional probability density functions to calculate the likelihood that a given

pixel, with its unique spectral vector (i.e., variable ti from the SAM equation),

belongs to each of the reference classes (i.e., variable ri). Every pixel is then as-

signed to the class with the maximum probability of membership. This classifi-

cation technique is used extensively with both multispectral and hyperspectral

imagery.

was also applied to the full thematic resolution class maps. This filtering

algorithm first delineates all of the raster polygons throughout the thematic

image. A raster polygon is defined as a group of adjacent (i.e., connected) pixels

with the same thematic class value. The adjacency criterion defines the polygon

using pixels joined along the four flat edges of the square and those pixels joined