A Gaussian adaptive resonance theory neural network classification algorithm applied to supervised land cover mapping using multitemporal vegetation index data

Neural network classifiers have been shown to provide supervised classification results that significantly improve on traditional classification algorithms such as the Bayesian (maximum likelihood [ML]) classifier. While the predominant neural network architecture has been the feedforward multilayer perceptron known as backpropagation. Adaptive resonance theory (ART) neural networks offer advantages to the classification of optical remote sensing data for vegetation and land cover mapping. A significant advantage is that it does not require prior specification of the neural net structure, creating as many internal nodes as are needed to represent the calibration (training) data. The Gaussian ARTMAP classification algorithm bases the probability that input training samples belong to specific classes on the parameters of its Gaussian distributions: the means, standard deviations, and a priori probabilities. The performance of the Gaussian ARTMAP classification algorithm in terms of classification accuracy using independent validation data indicated was over 70% accurate when applied to an annual series of monthly 1-km advanced very high resolution radiometer (AVHRR) satellite normalized difference vegetation index (NDVI) data. The accuracies were comparable to those of fuzzy ARTMAP and a univariate decision tree, and significantly higher than a Bayesian classification algorithm. Algorithm testing is based on calibration and validation data developed and applied to Central America to map the International Geosphere-Biosphere Programme (IGBP) land cover classification system.

[1]  Alan H. Strahler,et al.  Pixel- and site-based calibration and validation methods for evaluating supervised classification of remotely sensed data , 2002 .

[2]  A. Strahler,et al.  Application of the MODIS global supervised classification model to vegetation and land cover mapping of Central America , 2000 .

[3]  Alan H. Strahler,et al.  A note on procedures used for accuracy assessment in land cover maps derived from AVHRR data , 2000 .

[4]  Alan H. Strahler,et al.  Fuzzy Neural Network Classification of Global Land Cover from a 1° AVHRR Data Set , 1999 .

[5]  Alan H. Strahler,et al.  The IGBP DISCover confidence sites and the system for terrestrial ecosystem parameterization: tools , 1999 .

[6]  Ranga B. Myneni,et al.  Estimation of global leaf area index and absorbed par using radiative transfer models , 1997, IEEE Trans. Geosci. Remote. Sens..

[7]  A. Belward,et al.  The IGBP-DIS global 1km land cover data set, DISCover: First results , 1997 .

[8]  James R. Williamson,et al.  A Constructive, Incremental-Learning Network for Mixture Modeling and Classification , 1997, Neural Computation.

[9]  C. Brodley,et al.  Decision tree classification of land cover from remotely sensed data , 1997 .

[10]  Ramakrishna R. Nemani,et al.  LAND COVER CHARACTERIZATION USING MULTITEMPORAL RED, NEAR‐IR, AND THERMAL‐IR DATA FROM NOAA/AVHRR , 1997 .

[11]  James R. Williamson,et al.  Gaussian ARTMAP: A Neural Network for Fast Incremental Learning of Noisy Multidimensional Maps , 1996, Neural Networks.

[12]  Limin Yang,et al.  Characteristics of the 1 km AVHRR data set for North America , 1996 .

[13]  J. Cihlar.  Identification of contaminated pixels in AVHRR composite images for studies of land biosphere , 1996 .

[14]  Ramakrishna R. Nemani,et al.  Implementation of a hierarchical global vegetation classification in ecosystem function models , 1996 .

[15]  D. Randall,et al.  A Revised Land Surface Parameterization (SiB2) for Atmospheric GCMS. Part I: Model Formulation , 1996 .

[16]  R. DeFries,et al.  Classification trees: an alternative to traditional land cover classifiers , 1996 .

[17]  B. Bonan,et al.  A Land Surface Model (LSM Version 1.0) for Ecological, Hydrological, and Atmospheric Studies: Technical Description and User's Guide , 1996 .

[18]  J. Townshend,et al.  Global discrimination of land cover types from metrics derived from AVHRR pathfinder data , 1995 .

[19]  Robert A. Schowengerdt,et al.  A detailed comparison of backpropagation neural network and maximum-likelihood classifiers for urban land use classification , 1995, IEEE Trans. Geosci. Remote. Sens..

[20]  金田 重郎,et al.  C4.5: Programs for Machine Learning (書評) , 1995 .

[21]  Ramakrishna R. Nemani,et al.  A remote sensing based vegetation classification logic for global land cover analysis , 1995 .

[22]  Giles M. Foody,et al.  Using prior knowledge in artificial neural network classification with a minimal training set , 1995 .

[23]  J. Faundeen,et al.  The 1 km AVHRR global land data set: first stages in implementation , 1994 .

[24]  J. Michaelsen,et al.  Regression Tree Analysis of satellite and terrain data to guide vegetation sampling and surveys , 1994 .

[25]  A. Strahler.  MODIS Land Cover Product Algorithm Theoretical Basis Document (ATBD) Version 5.0 , 1994 .

[26]  Steven W. Running,et al.  A vegetation classification logic-based on remote-sensing for use in global biogeochemical models , 1994 .

[27]  Alberto Maria Segre,et al.  Programs for Machine Learning , 1994 .

[28]  R. Dickinson,et al.  Biosphere-Atmosphere Transfer Scheme (BATS) version le as coupled to the NCAR community climate model. Technical note. [NCAR (National Center for Atmospheric Research)] , 1993 .

[29]  Donald E. Brown,et al.  A comparison of decision tree classifiers with backpropagation neural networks for multimodal classification problems , 1992, Pattern Recognit..

[30]  Ann Henderson-Sellers,et al.  Biosphere-atmosphere transfer scheme(BATS) version 1e as coupled to the NCAR community climate model , 1993 .

[31]  J. Ross Quinlan,et al.  C4.5: Programs for Machine Learning , 1992 .

[32]  Jim Piper,et al.  Variability and bias in experimentally measured classifier error rates , 1992, Pattern Recognit. Lett..

[33]  Stephen Grossberg,et al.  Fuzzy ARTMAP: A neural network architecture for incremental supervised learning of analog multidimensional maps , 1992, IEEE Trans. Neural Networks.

[34]  Stephen Grossberg,et al.  Fuzzy ART: Fast stable learning and categorization of analog patterns by an adaptive resonance system , 1991, Neural Networks.

[35]  Stephen Grossberg,et al.  ARTMAP: supervised real-time learning and classification of nonstationary data by a self-organizing neural network , 1991, [1991 Proceedings] IEEE Conference on Neural Networks for Ocean Engineering.

[36]  George F. Hepner,et al.  Artificial neural network classification using a minimal training set - Comparison to conventional supervised classification , 1990 .

[37]  Craig T. Harston.  The neurological basis for neural computations , 1990 .

[38]  R. Lippmann.  Pattern classification using neural networks , 1989, IEEE Communications Magazine.

[39]  A. Dijk,et al.  Smoothing vegetation index profiles: an alternative method for reducing radiometric disturbance in NOAA/AVHRR data , 1987 .

[40]  B. Holben.  Characteristics of maximum-value composite images from temporal AVHRR data , 1986 .

[41]  A. Dalcher,et al.  A Simple Biosphere Model (SIB) for Use within General Circulation Models , 1986 .

[42]  M. Duggin,et al.  Dependence of NOAA-AVHRR recorded radiance on scan angle, atmospheric turbidity and unresolved cloud , 1984 .

[43]  Leo Breiman,et al.  Classification and Regression Trees , 1984 .

[44]  M. J. Duggin,et al.  Scan Angle Dependence Of Radiance Recorded By The NOAA-AVHRR , 1983, Optics & Photonics.

[45]  Alan H. Strahler,et al.  The Use of Prior Probabilities in Maximum Likelihood Classification , 1980 .