Detailed comparison of neuro-fuzzy estimation of subpixel land-cover composition from remotely sensed data

Andrea Baraldi; Elisabetta Binaghi; Palma N. Blonda; Pietro Alessandro Brivio; Anna Rampini

doi:10.1117/12.326731

13 October 1998 Detailed comparison of neuro-fuzzy estimation of subpixel land-cover composition from remotely sensed data

Andrea Baraldi, Elisabetta Binaghi, Palma N. Blonda, Pietro Alessandro Brivio, Anna Rampini

Author Affiliations +

Proceedings Volume 3455, Applications and Science of Neural Networks, Fuzzy Systems, and Evolutionary Computation; (1998) https://doi.org/10.1117/12.326731
Event: SPIE's International Symposium on Optical Science, Engineering, and Instrumentation, 1998, San Diego, CA, United States

Abstract

Mixed pixels, which do not follow a known statistical distribution that could be parameterized, are a major source of inconvenience in classification of remote sensing images. This paper reports on an experimental study designed for the in-depth investigation of how and why two neuro-fuzzy classification schemes, whose properties are complementary, estimate sub-pixel land cover composition from remotely sensed data. The first classifier is based on the fuzzy multilayer perceptron proposed by Pal and Mitra: the second classifier consists of a two-stage hybrid (TSH) learning scheme whose unsupervised first stage is based on the fully self- organizing simplified adaptive resonance theory clustering network proposed by Baraldi. Results of the two neuro-fuzzy classifiers are assessed by means of specific evaluation tools designed to extend conventional descriptive and analytical statistical estimators to the case of multi-membership in classes. When a synthetic data set consisting of pure and mixed pixels is processed by the two neuro-fuzzy classifiers, experimental result show that: i) the two neuro- fuzzy classifiers perform better than the traditional MLP; ii) classification accuracies of the two neuro-fuzzy classifiers are comparable; and iii) the TSH classifier requires to train less background knowledge than FMLP.

Citation Download Citation

Andrea Baraldi, Elisabetta Binaghi, Palma N. Blonda, Pietro Alessandro Brivio, and Anna Rampini "Detailed comparison of neuro-fuzzy estimation of subpixel land-cover composition from remotely sensed data", Proc. SPIE 3455, Applications and Science of Neural Networks, Fuzzy Systems, and Evolutionary Computation, (13 October 1998); https://doi.org/10.1117/12.326731

ACCESS THE FULL ARTICLE

INSTITUTIONAL
Select your institution to access the SPIE Digital Library.

SELECT YOUR INSTITUTION

PERSONAL
Sign in with your SPIE account to access your personal subscriptions or to use specific features such as save to my library, sign up for alerts, save searches, etc.

PERSONAL SIGN IN

No SPIE Account? Create one

PURCHASE THIS CONTENT

SUBSCRIBE TO DIGITAL LIBRARY

50 downloads per 1-year subscription

Members: $195

Non-members: $335 ADD TO CART

25 downloads per 1 - year subscription

Members: $145

Non-members: $250 ADD TO CART

PURCHASE SINGLE ARTICLE

Includes PDF, HTML & Video, when available