- Steering Committee - Dr. Andrew Blanchard, Dept. of Electrical Engineering,
Dr. Xinhua Zhuang, Dept. of Computer Engineering and Computer Science,
Dr. K. Palaniappan, Dept. of Computer Engineering and Computer Science,
Dr. Curt Davis, Dept. of Electrical Engineering,
Dr. James D. Hipple, Dept. of Geography,
Dr. Aderbal C. Correa, Dept. of CEE,
Dr. Dave Diamond, Director MoRAP
- Datasets - primarily for the state of Missouri
- Survey - state and local government use of remote sensing datasets
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
Reduce amount of manual supervision in the classification procedure (ideally
Reduce the total time needed to provide a statewide classification.
Develop tools for high performance visualization and compression of large
remote sensing datasets.
Avoid or minimize overlapping scene clustering.
Determine generalizability of approach beyond the state of Missouri.
Determine accuracy versus performance versus cost tradeoffs.
Develop automatic landcover analysis algorithm using tree classifiers,
mixture density class probability modeling, robust relaxation algorithms,
and hierarchical classification methods.
Develop analysis and visualization tools:
Progressive image (SLCCA) and video (VSLCCA) compression
Volumetric compression of multispectral data
Portable OpenGL version of DISS
Terrain geometry compression and landcover visualization using FlyBy
Automatic clustering result for Landsat scenes covering a single ecological
Comparing performance of automatic algorithm with MoRAP?s final classification
result evaluated in terms of landcover categories and ecological regions
Quantify performance (i.e., confusion matrices) with or without ground
Evaluate algorithm performance on simulated 1m data using aerial photo
Joshua Fraser: DISS visualization tool development for manipulating remote
sensing landcover data. Missouri DEM data access and creation. Algorithm
development for remote sensing data analysis.
Feng Zhu: Algorithm development for remote sensing landcover data analysis.
Jozsef Vass: Image, video, and volume data compression using wavelets.
Algorithm development for remote sensing landcover data analysis.
Jia Yao: Progressive transmission of image data. Content-based searching
of image databases.
Scott Musler: Geometry compression of DEM data. Visualization and rendering
4/30/99: Acquired two scenes of original Landsat data, phase 1 and phase
2 intermediate steps, and ground verification points from MoRAP.
5/10/99: Meeting to discuss Gaussian density approach for training. Delegated
work for preparing datasets and coding the algorithm.
5/11/-5/17/99: Learned format of ERDAS GIS files (.lan, .sta, .pro). Read
scenes acquired from MoRAP. Got phase 1 cluster label meanings.
5/11/99: Hired Feng Zhu.
5/24/99: Acquired MoRAP ground truth observations in Access format. Feng
and Joshua met with Kan He to further clarify the MoRAP landcover and landuse
6/1/99: NASA Stennis Space Center site visit
11/4/99: Feng completed implementation
of Bayesian BTGA and begin testing on the program with data from scene
Conferences, Meetings and Presentations:
April 19-20, 1999, attended by Joshua Fraser. GPS training using Z-Surveyor
May 13-17, 1999, attended by Joshua Fraser. NASA presentation pertaining
to visualization of Earth and Space Science High Resolution Imagery and
HDTV for Space Week at James Lovell Discovery World Museum,
June 21-25, 1999, attended by K. Palaniappan and Joshua Fraser. "Construction
and Visualization Techniques for Weather and Climate Models", NSF Geospace
Environment Modeling Workshop (GEM?99), Snowmass, CO, Dr. John Freeman,
July 12-16, 1999, attended by X. Zhuang. MPEG-4 Workshop, Vancouver, BC.
Landsat TM data with Phase II landcover classification data (four scenes
total) acquired and preprocessed for automatic classification algorithm
training and testing
BTGA implementation capable of using 12 features and classifying 47 classes
completed and in the process of being tested and improved
Piecewise linear classification boundaries organized in a binary tree structure
with weights learned using a genetic algorithm
Two scenes (one spatial region, two temporal observations) with 33.7million
observations used to design and test classifier
Entire Missouri dataset (overlapping scenes) has about 15 times more
Classification accuracy of 72.7% achieved using 4.6% of the data for training,
12 features, 8 landcover information categories (with 47 distinct classes)
Classification accuracy increases as amount of training data increases
Classifier training time increases linearly with the number of observations
and is becoming prohibitive - more than 10 hours using 4.1% (1.36 million)
of the data
Size of the classification tree increases as amount of training data increases
(classifier adaptation and complexity with more data)
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.
Use the Bayesian minimum error classifier for baseline comparisons
Expanding the size of the training data (require additional computational
Testing the classifier on the entire Missouri dataset.
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)