Invasive Species Mapping at Lake Okeechobee, Florida - ERDC-TR-05-10071

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High Spatial Resolution Digital Imagery

5.2

Invasive Species Mapping at Lake Okeechobee, Florida

Two different image processing techniques were used to develop vegetation

class maps for each of the two image sources:

The CAMIS airborne mosaics were processed using a supervised

classification algorithm, which requires field data.

The IKONOS satellite image was processed using an unsupervised

cluster routine that did not require the use of coincident ground truth

CAMIS Imagery

The goal of most applications of digital remotely sensed data is to assign

each multispectral pixel to predefined, discrete class (or group). This foundation

for classification techniques begins with statistically based multivariate dis-

criminant functions that determine the most likely group membership for each

pixel. There are a variety of supervised classification algorithms. The maximum-

likelihood classifier is one of the most frequently employed discrimination tech-

niques. This algorithm uses the class-conditional probability density functions to

calculate the likelihood that a given pixel, possessing its unique spectral vector,

belongs to each of the pre-defined classes. The equation used with the maximum

likelihood/Bayesian classifier is:

D = ln(ac) [0.5 ln( |Covc| ) [0.5 (X Mc)T(Covc 1) (X Mc)]

Where

D = calculated weighted distance (i.e., the likelihood estimate)

c = unique class

X = measurement vector of the candidate pixel (i.e., the unique spectral

vector)

Mc = mean vector of class c (calculated from the training statistics for

that class)

ac = percent probability that any candidate pixel is a member of class c

(commonly the analyst accepts the default value of 1.0)

Covc = covariance matrix of the pixels in class c (calculated from the

training statistics for that class)

|Covc| = determinant of Covc matrix

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