Land Cover Classification by an Artificial Neural Network with Ancillary Information

Abstract Remote sensing is an important source of land cover data required by many GIS users. Land cover data are typically derived from remotely–sensed data through the application of a conventional statistical classification. Such classification techniques are not, however, always appropriate, particularly as they may make untenable assumptions about the data and their output is hard, comprising only the code of the most likely class of membership. Whilst some deviation from the assumptions may be tolerated and a fuzzy output may be derived, making more information on class membership properties available, alternative classification procedures are sometimes required. Artificial neural networks are an attractive alternative to the statistical classifiers and here one is used to derive a fuzzy classification output from a remotely–sensed data set that may be post–processed with ancillary data available in a GIS to increase the accuracy with which land cover may be mapped. With the aid ancillary informatio...

[1]  J. Raper,et al.  Landscape ecology and GIS: edited by R Haines-Young, D R Green and S H Cousins Taylor and Francis, London, 1993, 296 pp , 1995 .

[2]  R. Lucas,et al.  An evaluation of fuzzy and texture-based classification approaches for mapping regenerating tropical forest classes from Landsat-TM data , 1995 .

[3]  W. B. Yates,et al.  Classification of remotely sensed data by an artificial neural network: issues related to training data characteristics , 1995 .

[4]  D. Peddle,et al.  Multi-Source Image Classification II: An Empirical Comparison of Evidential Reasoning and Neural Network Approaches , 1994 .

[5]  D. W. Mooneyhan,et al.  Of maps and myths , 1994 .

[6]  Russell Reed,et al.  Pruning algorithms-a survey , 1993, IEEE Trans. Neural Networks.

[7]  Etienne Barnard,et al.  Backpropagation uses prior information efficiently , 1993, IEEE Trans. Neural Networks.

[8]  C. G. Miller,et al.  Environment-dealing with the data deluge , 1993 .

[9]  John T. Finn,et al.  Use of the Average Mutual Information Index in Evaluating Classification Error and Consistency , 1993, Int. J. Geogr. Inf. Sci..

[10]  Gerard B. M. Heuvelink,et al.  Error Propagation in Cartographic Modelling Using Boolean Logic and Continuous Classification , 1993, Int. J. Geogr. Inf. Sci..

[11]  Charalambos Kontoes,et al.  An Experimental System for the Integration of GIS Data in Knowledge-Based Image Analysis for Remote Sensing of Agriculture , 1993, Int. J. Geogr. Inf. Sci..

[12]  Daniel L. Civco,et al.  Artificial Neural Networks for Land-Cover Classification and Mapping , 1993, Int. J. Geogr. Inf. Sci..

[13]  Russell G. Congalton,et al.  Mapping old growth forests on National Forest and Park Lands in the Pacific Northwest from remotely sensed data , 1993 .

[14]  Derek R. Peddle,et al.  An Empirical Comparison of Evidential Reasoning, Linear Discriminant Analysis, and Maximum Likelihood Algorithms for Alpine Land Cover Classification , 1993 .

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

[16]  N. Campbell,et al.  Derivation and applications of probabilistic measures of class membership from the maximum-likelihood classification , 1992 .

[17]  Fabio Maselli,et al.  Use of error matrices to improve area estimates with maximum likelihood classification procedures , 1992 .

[18]  I. Kanellopoulos,et al.  Land-cover discrimination in SPOT HRV imagery using an artificial neural network - a 20-class experiment , 1992 .

[19]  S. Quegan,et al.  Inferences on spatial and temporal variability of the backscatter from growing crops using AgriSAR data , 1992 .

[20]  Philip H. Swain,et al.  Improving classification of crop residues using digital land ownership data and Landsat TM imagery , 1991 .

[21]  Russell G. Congalton,et al.  A review of assessing the accuracy of classifications of remotely sensed data , 1991 .

[22]  The radiometric quality of AgriSAR data , 1991 .

[23]  C. Justice,et al.  Global land cover classification by remote sensing: present capabilities and future possibilities , 1991 .

[24]  L. Janssen,et al.  Implementation of temporal relationships in knowledge based classification of satellite images. , 1991 .

[25]  G. GRAY TAPPAN,et al.  Monitoring grasshopper and locust habitats in Sahelian Africa using GIS and remote sensing technology , 1991, Int. J. Geogr. Inf. Sci..

[26]  Robert J. Schalkoff,et al.  Pattern recognition - statistical, structural and neural approaches , 1991 .

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

[28]  Fangju Wang,et al.  Fuzzy supervised classification of remote sensing images , 1990 .

[29]  F. Wang.  Improving remote sensing image analysis through fuzzy information representation , 1990 .

[30]  Igor Aleksander,et al.  Introduction to Neural Computing , 1990 .

[31]  P. Curran,et al.  Multi‐temporal airborne synthetic aperture radar data for crop classification , 1989 .

[32]  Jon Atli Benediktsson,et al.  Neural Network Approaches Versus Statistical Methods in Classification of Multisource Remote Sensing Data , 1989, 12th Canadian Symposium on Remote Sensing Geoscience and Remote Sensing Symposium,.

[33]  Alan F. Murray,et al.  VLSI implementation of neural networks , 1989 .

[34]  P. Pizor.  Principles of Geographical Information Systems for Land Resources Assessment. , 1987 .

[35]  I. L. Thomas,et al.  Classification of remotely sensed images. , 1987 .

[36]  J. L. Smith,et al.  Using classification error matrices to improve the accuracy of weighted land-cover models , 1987 .

[37]  P. Burrough.  Principles of Geographical Information Systems for Land Resources Assessment , 1986 .

[38]  Geoffrey E. Hinton,et al.  Learning internal representations by error propagation , 1986 .

[39]  James L. McClelland,et al.  Parallel distributed processing: explorations in the microstructure of cognition, vol. 1: foundations , 1986 .

[40]  R. Congalton,et al.  Accuracy assessment: a user's perspective , 1986 .

[41]  L. D. Miller,et al.  An automated land-use mapping comparison of the Bayesian maximum likelihood and linear discriminant analysis algorithms , 1984 .

[42]  Christopher O. Justice,et al.  Information extraction from remotely sensed data. , 1981 .

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

[44]  S. Siegel,et al.  Nonparametric Statistics for the Behavioral Sciences , 2022, The SAGE Encyclopedia of Research Design.