Accession Number:

ADA273615

Title:

Hyperspectral Imagery Classification Using a Backpropagation Neural Network

Descriptive Note:

Scientific paper

Corporate Author:

TEXCOM EXPERIMENTATION CENTER FORT HUNTER LIGGETT CA

Personal Author(s):

Report Date:

1993-12-01

Pagination or Media Count:

7.0

Abstract:

A backpropagation neural network was developed and implemented for classifying AVIRIS Airborne VisibleInfrared Imaging Spectrometer hyperspectral imagery. It is a fully interconnected linkage of three layers of neural network. Fifty input layer neurons take in signals from Bands 41 to 90 of the AVIRIS spectral data in parallel. Test images are classified into four terrain categories of water, grassland, golf courses and built-up areas using four output neurons. A hidden layer consisting of 12 neurons is used. A training set containing 1,700 pixels for each of the four desired terrain categories is extracted and created from the first test image. Good classification accuracies of 81.8 percent to 95.5 percent are achieved despite the moderate AVIRIS pixel resolution of 20 meters by 20 meters. Backpropagation neural network, Hyperspectral imagery

Subject Categories:

  • Cybernetics
  • Infrared Detection and Detectors

Distribution Statement:

APPROVED FOR PUBLIC RELEASE