Fuzzy ARTMAP Classification Research Articles

Monitoring of wetland changes affected by drought using four Landsat satellite data and Fuzzy ARTMAP classification method (case study Hamoun wetland, Iran)

Monitoring of wetland changes affected by drought using four Landsat satellite data and Fuzzy ARTMAP classification method (case study Hamoun wetland, Iran)

The Assessment and Predicting of Land Use Changes to Urban Area Using Multi-Temporal Satellite Imagery and GIS: A Case Study on Zanjan, IRAN (1984-2011)

Due to inappropriate planning and management, accelerated urban growth and tremendous loss in land, especially cropland, have become a great challenge for sustainable urban development [1]. Detection of such changes may help decision makers and planners to understand the factors in land use and land cover changes in order to take effective and useful measures. Remote sensing and GIS techniques may be used as efficient tools to detect and assess land use changes.In recent years, a considerable land use changes have occurred in the greater Zanjan area. In order to understand the type and rate of changes in this area, Landsat TM images captured in 1984 and 2011 have been selected for comparison.First, geometric correction and contrast stretch are applied. In order to detect and evaluate land use changes, image differencing, principal component analyses and Fuzzy ARTMAP classification method are applied. Finally, the results of land cover classification for three different times are compared to reveal land use changes.Then, combined Cellular Automata with Markov Chain analysis is employed to forecast of human impacts on land use change until 2020 in Zanjan area. The results of the present study disclose that about 44 percents of the total area changed their land use, e.g., changing agricultural land, orchard and bare land to settlements, construction of industrial areas and highways. The crop pattern also changes, such as orchard land to agricultural land and vice versa. The mentioned changes have occurred within last 27 years in Zanjan city and its surrounding area.

Unsupervised Fuzzy ARTMAP Classification of Hyperspectral Hyperion Data for Savanna and Agriculture Discrimination in the Brazilian Cerrado

The Brazilian Cerrado is threatened by agricultural land use conversion. Accurate quantification of overall and subtype Cerrado distributions is essential for regional monitoring. In this research, unsupervised fuzzy ARTMAP was compared against conventional k-means classification of Cerrado and agriculture, based on Hyperion satellite data. We systematically tested a range of fuzzy ARTMAP parameters, determining the best parameter combinations. The effect of an additional surface liquid-water input vector was also tested. Similar results were obtained when only Hyperion apparent surface reflectance data were used; fuzzy ARTMAP, however, was generally markedly more accurate than k-means when the additional surface liquid-water input was included.

Using genetic algorithm to select the presentation order of training patterns that improves simplified fuzzy ARTMAP classification performance

The presentation order of training patterns to a simplified fuzzy ARTMAP (SFAM) neural network affects the classification performance. The common method to solve this problem is to use several simulations with training patterns presented in random order, where voting strategy is used to compute the final performance. Recently, an ordering method based on min–max clustering was introduced to select the presentation order of training patterns based on a single simulation. In this paper, another single simulation method based on genetic algorithm is proposed to obtain the presentation order of training patterns for improving the performance of SFAM. The proposed method is applied to a 40-class individual classification problem using visual evoked potential signals and three other datasets from UCI repository. The proposed method has the advantages of improved classification performance, smaller network size and lower training time compared to the random ordering and min–max methods. When compared to the random ordering method, the new ordering scheme has the additional advantage of requiring only a single simulation. As the proposed method is general, it can also be applied to a fuzzy ARTMAP neural network when it is used as a classifier.

Simplified fuzzy ARTMAP classification of individuals using optimal VEP channels

In previous studies, identification of individuals using 61 channel Visual Evoked Potential (VEP) signals from the brain has been shown to be feasible. These studies used neural network classification of gamma band spectral power of VEP signals from

Fuzzy ARTMAP classification of invariant features derived using angle of rotation from a neural network

Conventional regular moment functions have been proposed as pattern sensitive features in image classification and recognition applications. But conventional regular moments are only invariant to translation, rotation and equal scaling. It is shown that the conventional regular moment invariants remain no longer invariant when the image is scaled unequally in the x- and y-axis directions. We address this problem by presenting a technique to make the regular moment functions invariant to unequal scaling. However, the technique produces a set of features that are only invariant to translation, unequal/equal scaling and reflection. They are not invariant to rotation. To make them invariant to rotation, moments are calculated with respect to the principal axis of the image. To perform this, the exact angle of rotation must be known. But the method of using the second-order moments to determine this angle will also be inclusive of an undesired tilt angle. Therefore, in order to correctly determine the amount of rotation, the tilt angle which differs for different scaling factors in the x- and y-axis directions for the particular image must be obtained. In order to solve this problem, a neural network using the back-propagation learning algorithm is trained to estimate the tilt angle of the image and from this the amount of rotation for the image can be determined. Next, the new moments are derived and a Fuzzy ARTMAP network is used to classify these images into their respective classes. Sets of experiments involving images rotated and scaled unequally in the x- and y-axis directions are carried out to demonstrate the validity of the proposed technique.

Ultrasonic transducer characterization by neural networks

This paper presents neural network-based system for the characterization of ultrasonic transducers. An automated system for characterizing ultrasonic transducers was designed and built. Different characterizing algorithms were applied and their performance was investigated and compared. It was found that artificial neural network (ANN) techniques, in general, provide better classification as compared to the pattern recognition techniques we applied earlier (M.S. Obaidat, J.W. Ekis, IEEE Transactions on Instrumentation and Measurements, 40 (5) (1991) 847–850). The Moody-Darken Radial Basis Function network (MD-RBFN), Learning Vector Quantization (LVQ) with 52 kohonen neurons, and Fuzzy ARTMAP classification network are the neural networks (NNs) that provided us with a classification accuracy of 100%. Several variants of Backpropagation neural network (BPNN) were tested for this application, and the classification results were seen to vary in the range 6.55%–98.35%. The best performing paradigm among several variants of the Modular Neural Network (MNN), Reinforcement Neural Network (RNN), Probabilistic Neural Network (PNN), and Counterpropagation Neural Network (CPNN) produced a classification accuracy of 78.85%, 31.42%, 45.21%, and 79.28%, respectively. The competitive learning (CL) technique provided poor results as compared to the Self-Organizing-Map (SOM) for preclustering.

ART neural networks for remote sensing: vegetation classification from Landsat TM and terrain data

A new methodology for automatic mapping from Landsat thematic mapper (TM) and terrain data, based on the fuzzy ARTMAP neural network, is developed. System capabilities are tested on a challenging remote sensing classification problem, using spectral and terrain features for vegetation classification in the Cleveland National Forest. After training at the pixel level, system performance is tested at the stand level, using sites not seen during training. Results are compared to those of maximum likelihood classifiers, as well as back propagation neural networks and K nearest neighbor algorithms. ARTMAP dynamics are fast, stable, and scalable, overcoming common limitations of back propagation. Best results are obtained using a hybrid system based on a convex combination of fuzzy ARTMAP and maximum likelihood predictions. A prototype remote sensing example introduces each aspect of data processing and fuzzy ARTMAP classification. The example shows how the network automatically constructs a minimal number of recognition categories to meet accuracy criteria. A voting strategy improves prediction and assigns confidence estimates by training the system several times on different orderings of an input set.