Applications of neural networks to radar image classification

Journal Article · Sat Jan 01 04:00:00 EST 1994 · IEEE Transactions on Geoscience and Remote Sensing (Institute of Electrical and Electronics Engineers); (United States)

DOI:https://doi.org/10.1109/36.285193· OSTI ID:7036445

Hara, Yoshihisa; Atkins, R G; Yueh, S H; Shin, R T; Kong, J A ^[1]

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1. Massachusetts Inst. of Tech., Cambridge, MA (United States)

Classification of terrain cover using polarimetric radar is an area of considerable current interest and research. A number of methods have been developed to classify ground terrain types from fully polarimetric synthetic aperture radar (SAR) images, and these techniques are often grouped into supervised and unsupervised approaches. Supervised methods have yielded higher accuracy than unsupervised techniques, but suffer from the need for human interaction to determine classes and training regions. In contrast, unsupervised methods determine classes automatically, but generally show limited ability to accurately divide terrain into natural classes. In this paper, a new terrain classification technique is introduced to determine terrain classes in polarimetric SAR images, utilizing unsupervised neural networks to provide automatic classification, and employing an iterative algorithm to improve the performance. Several types of unsupervised neural networks are first applied to the classification of SAR images, and the results are compared to those of more conventional unsupervised methods. Results show that one neural network method--Learning Vector Quantization (LVQ)--outperforms the conventional unsupervised classifiers, but is still inferior to supervised methods. To overcome this poor accuracy, an iterative algorithm is proposed where the SAR image is reclassified using Maximum Likelihood (ML) classifier. It is shown that this algorithm converges, and significantly improves classification accuracy. Performance after convergence is seen to be comparable to that obtained with a supervised ML classifier, while maintaining the advantages of an unsupervised technique.

• OSTI ID: 7036445
• Journal Information: IEEE Transactions on Geoscience and Remote Sensing (Institute of Electrical and Electronics Engineers); (United States), Journal Name: IEEE Transactions on Geoscience and Remote Sensing (Institute of Electrical and Electronics Engineers); (United States) Vol. 32:1; ISSN 0196-2892; ISSN IGRSD2
• Country of Publication: United States
• Language: English

Related Subjects

54 ENVIRONMENTAL SCIENCES
540210* -- Environment
Terrestrial-- Basic Studies-- (1990-)
ALGORITHMS
CLASSIFICATION
GROUND COVER
IMAGE PROCESSING
MATHEMATICAL LOGIC
MEASURING INSTRUMENTS
NEURAL NETWORKS
POLARIMETRY
PROCESSING
RADAR
RANGE FINDERS
REMOTE SENSING
SYNTHETIC-APERTURE RADAR