Water Regions Extraction From Radar Imagery Using a Neural Network

Abstract

An artificial neural network concept is explored and developed for detecting and extracting water regions from radar imagery. A backpropagation neural network consisting of three layers of processing elements (PEs) is selected for this application. The input layer is composed of nine PEs that are arranged to process a single pixel at the same time. Two PEs in a hidden layer are sufficient for delineating water from other terrain categories. A single output PE with a thresholding function classify effectively all test images into two terrain categories: water and non-water. Large-scale Synthetic Aperture Radar (SAR) images, containing 512 by 512 pixels, were used as the test images for this experiment. Two blocks of water regions totalling 2,048 pixels were extracted for training the network. All the water pixels were classified correctly, while more than 99 percent of the non-water pixels were correctly classified.

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Document Details

Document Type
Technical Report
Publication Date
Dec 16, 1992
Accession Number
ADA259517

Entities

People

  • Pi-fuay Chen
  • Tho Con Tran

Organizations

  • Army Geospatial Center

Tags

Communities of Interest

  • Materials and Manufacturing Processes

DTIC Thesaurus Topics

  • Accuracy
  • Classification
  • Computer Vision
  • Computers
  • Computing System Architectures
  • Data Processing
  • Detection
  • Image Processing
  • Network Architecture
  • Network Topology
  • Neural Networks
  • Pattern Recognition
  • Radar
  • Recognition
  • Remote Sensing
  • Synthetic Aperture Radar
  • Training

Readers

  • Computer Vision.
  • Neural Network Machine Learning.
  • Wetland-Land-Environmental Management.

Technology Areas

  • AI & ML
  • AI & ML - Neural Networks