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Artificial Neural Network Application in Remote Sensing. Tim Ren, M.S. Candidate Department of Natural Resources Science University of Rhode Island 04/28/2000. Preview. Introduction to Remote Sensing Objectives Why ANN (Artificial Neural Network) How ANN works
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Artificial Neural Network Application in Remote Sensing Tim Ren, M.S. Candidate Department of Natural Resources Science University of Rhode Island 04/28/2000
Preview • Introduction to Remote Sensing • Objectives • Why ANN (Artificial Neural Network) • How ANN works • ANN Application in Remote Sensing
Remote Sensing Data Collection • Satellite Multispectral Data of EMR • Analog/Digital Transformation • Multi-band Digital Image (False color image)
Remote Sensing of the Earth Surface Improve classification performance: How to achieve an accurate land cover map?
Multi-source Spatial Data Band1 Band2 … … Band N GIS Aerial photo ... Multisource spatial data provide information from different perspectives in data modeling and information extraction.
Water Forest Agri. Urban Wetland Digital Image Processing Multi-source data visualize A picture Classification result classify
Traditional method of classification • Assumption • Methodology • - Unsupervised Classification • - Supervised Classification • Accuracy of Classification • Limits
Objectives • Develop artificial neural network algorithms to handle multispectral and multitemporal remote sensing and multisource spatial data • Build efficient ANN architecture, establish learning rules to train and refine ANN paradigms • Apply the trained ANNs in remote sensing data modeling (classification and change detection)
Why ANN ? • No need for the Gaussian (Normal) distribution about the input data (as required by Bayesian classifier) • No need for the prior knowledge about the input data before the classification process • No restrictions about the format of input data (More flexible and robust in multi-source spatial data classification; A Promising alternative to Bayesian classification)
Classification Process Landsat TM Band1 Band2 Band3 Band4 Band5 Band6 Band7 Observation space Solution space Mapping Relationship 0~255 0~255 0~255 0~255 0~255 0~255 0~255 Category 1 Category .. Category … Category … Category … Category … Category N Water wetland Forest Agri. Urban Residential Methods: Linear Non-linear Statistical ANN 40 45 61 193 80 112 25 (Pattern) Forest
Questions to Answer - Does ANN algorithm perform better than traditional statistical method? - Which ANN paradigm is better (Backpropagation? Modularized ANN?...) - How effective an ANN can do in multisource spatial data analysis and modeling?
Part II: Introduction to Artificial Neural Networks … … … … … … … … …
… … … … … … … … … Artificial Neural Network Is Defined by ... • Processing elements • Organized topological structure • Learning rules
Artificial Neural Network Is Defined by ... • Processing elements • Organized topological structure • Learning rules
Processing elements (PE) Artificial counterparts of neurons in a brain PE Wj1 Wj2 Wj3 output path f(x) input Wj4 Wj5
ANN Architecture -Processing Elements … … … … … … … … … PE Output PE Input unitj wj1 o1 wj2 o2 oj Σ, f o3 wj3
Artificial Neural Network Is Defined by ... • Processing elements • Organized topological structure • Learning rules
ANN Architecture -Topological Structure Input layer Hidden layer Output layer … … … … … … … … … Input vector i(x1, x2, … xn) Output vector i(o1, o2, … om)
Organized topological structure --Back-Propagation ANN Architecture Land-cover Categories Output layer Hidden layer Input layer Landsat TM, GIS...
Artificial Neural Network Is Defined by ... • Processing elements • Organized topological structure • Learning rules
ANN Architecture - Learning Rules Input layer Hidden layer Output layer … … … … … … … … … Input vector i(x1, x1, … xn) Output vector i(o1, o1, … on) How the ANN learns ?
Supervised Learning with a Teacher - Paired training set ( Input and Output)
Unsupervised Learning - Self-Organize
Reinforcement Learning - Learning with Critic
Part III ANN application in Remote Sensing … … … … … … … … … ?
ANN application in Remote Sensing - Multi-source Spatial Data Classification - Change Detection - Land Cover Change and Prediction
Water Forest Agri. Urban Wetland ANN applied in the Remote Sensing - Multi-source Spatial Data Classification Remote Sensed Data Classification Result: land cover map
- Multi-source Spatial Data Classification Input layer Hidden layer Output layer … … … … … … … … … … Remote Sensed Data Grassland Woodland … … … … … Wetland … … … … Other source GIS, Airphoto ……. Urban
Water Forest Agri. Urban Wetland Forest - Urban Agri. - Urban Urban Unchanged ANN Applied in Remote Sensing - Change Detection Changes between 1985 - 1997 1985 1997
- Change Detection (2m:n1:o) network Chang Map with Complete Land Cover Change Information Land cover change extractor Image A Image B
ANN Application in Remote Sensing - Land Cover Change and Prediction 1980 1990 2010 ?
Plan of Research • - Study Area • - Data • Landsat TM • GIS • Field Observation • (USGS EROS Data Center) • - Design of Artificial Neural Network
+ Summary Data from Other sources Remote Sensing Data
Acknowledgement Dr. Y.Q.Wang Dr. Yong Wang NASA Grant No. NAG58829 Apr. 28 2000