Philip H. Swain

Neural Network Approaches Versus Statistical Methods In Classification Of Multisource Remote Sensing Data

Neural network learning procedures and statistical classificaiton methods are applied and compared empirically in classification of multisource remote sensing and geographic data. Statistical multisource classification by means of a method based on Bayesian classification theory is also investigated and modified. The modifications permit control of the influence of the data sources involved in the classification process. Reliability measures are introduced to rank the quality of the data sources. The data sources are then weighted according to these rankings in the statistical multisource classification. Four data sources are used in experiments: Landsat MSS data and three forms of topographic data (elevation, slope, and aspect). Experimental results show that two different approaches have unique advantages and disadvantages in this classification application.

Consensus theoretic classification methods

Consensus theory is adopted as a means of classifying geographic data from multiple sources. The foundations and usefulness of different consensus theoretic methods are discussed in conjunction with pattern recognition. Weight selections for different data sources are considered and modeling of non-Gaussian data is investigated. The application of consensus theory in pattern recognition is tested on two data sets: (1) multisource remote sensing and geographic data, and (2) very-high-dimensional remote sensing data. The results obtained using consensus theoretic methods are found to compare favorably with those obtained using well-known pattern recognition methods. The consensus theoretic methods can be applied in cases where the Gaussian maximum likelihood method cannot. Also, the consensus theoretic methods are computationally less demanding than the Gaussian maximum likelihood method and provide a means for weighting data sources differently. >

Probabilistic and Evidential Approaches for Multisource Data Analysis

Two methods for combining the information contents from multiple sources of remote-sensing image data and spatial data in general are described. One is a probabilistic scheme that employs a global membership function (similar to a joint posterior probability) that is derived from all available data sources. The other is an evidential calculus based upon Dempsters orthogonal sum combination rule. A feature of both methods is that uncertainty regarding data analysis can be incorporated into the process. Both schemes are evaluated in terms of their general applicability and certain equivalences are noted. Moreover, both are shown to perform well on mixed multispectral data.

The decision tree classifier: Design and potential

This paper presents the basic concepts of a multistage classification strategy called the decision tree classifier. Two methods for designing decision trees are discussed and experimental results are reported. The relative advantages and disadvantages of each design method are considered. A spectrum of typical applications in remote sensing is noted.

Parallel consensual neural networks

A new type of a neural-network architecture, the parallel consensual neural network (PCNN), is introduced and applied in classification/data fusion of multisource remote sensing and geographic data. The PCNN architecture is based on statistical consensus theory and involves using stage neural networks with transformed input data. The input data are transformed several times and the different transformed data are used as if they were independent inputs. The independent inputs are first classified using the stage neural networks. The output responses from the stage networks are then weighted and combined to make a consensual decision. In this paper, optimization methods are used in order to weight the outputs from the stage networks. Two approaches are proposed to compute the data transforms for the PCNN, one for binary data and another for analog data. The analog approach uses wavelet packets. The experimental results obtained with the proposed approach show that the PCNN outperforms both a conjugate-gradient backpropagation neural network and conventional statistical methods in terms of overall classification accuracy of test data.

Conjugate-gradient neural networks in classification of multisource and very-high-dimensional remote sensing data

Abstract Application of neural networks to classification of remote sensing data is discussed. Conventional two-layer backpropagation is found to give good results in classification of remote sensing data but is not efficient in training. A more efficient variant, based on conjugate-gradient optimization, is used for classification of multisource remote sensing and geographic data and very-high-dimensional data. The conjugate-gradient neural networks give excellent performance in classification of multisource data but do not compare as well with statistical methods in classification of very-high-dimcnsional data.

Hybrid consensus theoretic classification

Hybrid classification methods based on consensus from several data sources are considered. Each data source is at first treated separately and modeled using statistical methods. Then weighting mechanisms are used to control the influence of each data source in the combined classification. The weights are optimized in order to improve the combined classification accuracies. Both linear and nonlinear optimization methods are considered and used in classification of two multisource remote sensing and geographic data sets. A nonlinear method which utilizes a neural network gives excellent experimental results. The hybrid statistical/neural method outperforms all other methods in terms of test accuracies in the experiments.

A means for utilizing ancillary information in multispectral classification

Abstract A method is presented that allows information from ancillary data sources to be incorporated into the results of an existing classification of remotely sensed data. Based upon probabilistic label relaxation procedures, which are used for imbedding spatial context data in image-labeling problems, the method utilizes the source of ancillary information in the form of a set of probabilities. These are injected into a modified relaxation method called supervised relaxation labeling which, on application, develops a labeling for remotely sensed data that strikes a balance in consistency between spectral, spatial, and ancillary data sources of information. Results are presented of a forestry classification in which accuracy is improved from 68% to 81% by incorporating topographic elevation in the manner outlined.

Contextual classification of multispectral image data

Abstract Compound decision theory is invoked to develop a method for classifying image data using spatial context. Methods for characterizing contextual information in an image are proposed and tested. Experimental results based on both simulated and real multispectral remote sensing data demonstrate the effectiveness of the contextual classifier. A number of practical problems associated with this approach are discussed and possible solutions are explored.

Stochastic programmed grammars for syntactic pattern recognition

Abstract A stochastic version of the Programmed Grammar is proposed as a powerful and convenient formalism for syntactic pattern recognition. An algorithm for parsing strings generated by Stochastic Context-Free Programmed Grammars is described and an example is presented of one such grammar which generates “noisy” squares.