Automatic BTGA Land Cover Classification


To develop an automatic landcover classification algorithm for the state of Missouri using multispectral Landsat TM data and additional information collected by MoRAP. Objectives include:



Recent Progress:

Conferences, Meetings and Presentations:


Future Work

  1. Improving classification accuracy using non-linear classifiers, hierarchical classification, handling reject regions, expanded feature set (such as image texture features), image space segmentation, feature space clustering.
  2. Use the Bayesian minimum error classifier for baseline comparisons
  3. Expanding the size of the training data (require additional computational capabilities).
  4. Testing the classifier on the entire Missouri dataset.
  5. Testing the classifier using ecological region information.

Satellite Imagery and Landcover Data Characteristics

Landsat TM imagery with MORAP Phase II classification data two regions in Spring 1992 (05/03/92) and Fall 1992 (09/24/92) acquired and preprocessed for algorithm development and testing.

Each of the four scenes are 7226 x 6524 pixels in image size, with 30 m x 30 m ground resolution and have 6 TM spectral bands which are used as the feature set. These regions are in the vicinity of Columbia, Missouri. Approximately fifteen overlapping TM scenes are required to cover the entire state of Missouri with the mosaic image size being 19,398 x 17,004 pixels.

Testing and training has been done using a single scene which contains 33,784,355 Phase II classified pixels. The ith labelled sample test data is a row vector: (f1,i, f2,i, ...., f12,i, ci)

Selected Reports: