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, which did not give satisfactory performance. 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.National Science Foundation (IRI 94-01659, SBR 93-00633); Office of Naval Research (N00014-95-l-0409, N00014-95-0657

ART and ARTMAP Neural Networks for Applications: Self-Organizing Learning, Recognition, and Prediction

ART and ARTMAP neural networks for adaptive recognition and prediction have been applied to a variety of problems. Applications include parts design retrieval at the Boeing Company, automatic mapping from remote sensing satellite measurements, medical database prediction, and robot vision. This chapter features a self-contained introduction to ART and ARTMAP dynamics and a complete algorithm for applications. Computational properties of these networks are illustrated by means of remote sensing and medical database examples. The basic ART and ARTMAP networks feature winner-take-all (WTA) competitive coding, which groups inputs into discrete recognition categories. WTA coding in these networks enables fast learning, that allows the network to encode important rare cases but that may lead to inefficient category proliferation with noisy training inputs. This problem is partially solved by ART-EMAP, which use WTA coding for learning but distributed category representations for test-set prediction. In medical database prediction problems, which often feature inconsistent training input predictions, the ARTMAP-IC network further improves ARTMAP performance with distributed prediction, category instance counting, and a new search algorithm. A recently developed family of ART models (dART and dARTMAP) retains stable coding, recognition, and prediction, but allows arbitrarily distributed category representation during learning as well as performance.National Science Foundation (IRI 94-01659, SBR 93-00633); Office of Naval Research (N00014-95-1-0409, N00014-95-0657

ART Neural Networks for Remote Sensing Image Analysis

ART and ARTMAP neural networks for adaptive recognition and prediction have been applied to a variety of problems, including automatic mapping from remote sensing satellite measurements, parts design retrieval at the Boeing Company, medical database prediction, and robot vision. This paper features a self-contained introduction to ART and ARTMAP dynamics. An application of these networks to image processing is illustrated by means of a remote sensing example. The basic ART and ARTMAP networks feature winner-take-all (WTA) competitive coding, which groups inputs into discrete recognition categories. WTA coding in these networks enables fast learning, which allows the network to encode important rare cases but which may lead to inefficient category proliferation with noisy training inputs. This problem is partially solved by ART-EMAP, which use WTA coding for learning but distributed category representations for test-set prediction. Recently developed ART models (dART and dARTMAP) retain stable coding, recognition, and prediction, but allow arbitrarily distributed category representation during learning as well as performance

Adaptive Resonance Theory: Self-Organizing Networks for Stable Learning, Recognition, and Prediction

Adaptive Resonance Theory (ART) is a neural theory of human and primate information processing and of adaptive pattern recognition and prediction for technology. Biological applications to attentive learning of visual recognition categories by inferotemporal cortex and hippocampal system, medial temporal amnesia, corticogeniculate synchronization, auditory streaming, speech recognition, and eye movement control are noted. ARTMAP systems for technology integrate neural networks, fuzzy logic, and expert production systems to carry out both unsupervised and supervised learning. Fast and slow learning are both stable response to large non stationary databases. Match tracking search conjointly maximizes learned compression while minimizing predictive error. Spatial and temporal evidence accumulation improve accuracy in 3-D object recognition. Other applications are noted.Office of Naval Research (N00014-95-I-0657, N00014-95-1-0409, N00014-92-J-1309, N00014-92-J4015); National Science Foundation (IRI-94-1659

Fuzzy ART

Adaptive Resonance Theory (ART) models are real-time neural networks for category learning, pattern recognition, and prediction. Unsupervised fuzzy ART and supervised fuzzy ARTMAP synthesize fuzzy logic and ART networks by exploiting the formal similarity between the computations of fuzzy subsethood and the dynamics of ART category choice, search, and learning. Fuzzy ART self-organizes stable recognition categories in response to arbitrary sequences of analog or binary input patterns. It generalizes the binary ART 1 model, replacing the set-theoretic: intersection (∩) with the fuzzy intersection (∧), or component-wise minimum. A normalization procedure called complement coding leads to a symmetric: theory in which the fuzzy inter:>ec:tion and the fuzzy union (∨), or component-wise maximum, play complementary roles. Complement coding preserves individual feature amplitudes while normalizing the input vector, and prevents a potential category proliferation problem. Adaptive weights :otart equal to one and can only decrease in time. A geometric interpretation of fuzzy AHT represents each category as a box that increases in size as weights decrease. A matching criterion controls search, determining how close an input and a learned representation must be for a category to accept the input as a new exemplar. A vigilance parameter (p) sets the matching criterion and determines how finely or coarsely an ART system will partition inputs. High vigilance creates fine categories, represented by small boxes. Learning stops when boxes cover the input space. With fast learning, fixed vigilance, and an arbitrary input set, learning stabilizes after just one presentation of each input. A fast-commit slow-recode option allows rapid learning of rare events yet buffers memories against recoding by noisy inputs. Fuzzy ARTMAP unites two fuzzy ART networks to solve supervised learning and prediction problems. A Minimax Learning Rule controls ARTMAP category structure, conjointly minimizing predictive error and maximizing code compression. Low vigilance maximizes compression but may therefore cause very different inputs to make the same prediction. When this coarse grouping strategy causes a predictive error, an internal match tracking control process increases vigilance just enough to correct the error. ARTMAP automatically constructs a minimal number of recognition categories, or "hidden units," to meet accuracy criteria. An ARTMAP voting strategy improves prediction by training the system several times using different orderings of the input set. Voting assigns confidence estimates to competing predictions given small, noisy, or incomplete training sets. ARPA benchmark simulations illustrate fuzzy ARTMAP dynamics. The chapter also compares fuzzy ARTMAP to Salzberg's Nested Generalized Exemplar (NGE) and to Simpson's Fuzzy Min-Max Classifier (FMMC); and concludes with a summary of ART and ARTMAP applications.Advanced Research Projects Agency (ONR N00014-92-J-4015); National Science Foundation (IRI-90-00530); Office of Naval Research (N00014-91-J-4100

Integrating Symbolic and Neural Processing in a Self-Organizing Architechture for Pattern Recognition and Prediction

British Petroleum (89A-1204); Defense Advanced Research Projects Agency (N00014-92-J-4015); National Science Foundation (IRI-90-00530); Office of Naval Research (N00014-91-J-4100); Air Force Office of Scientific Research (F49620-92-J-0225

Adaptive fuzzy system for 3-D vision

An adaptive fuzzy system using the concept of the Adaptive Resonance Theory (ART) type neural network architecture and incorporating fuzzy c-means (FCM) system equations for reclassification of cluster centers was developed. The Adaptive Fuzzy Leader Clustering (AFLC) architecture is a hybrid neural-fuzzy system which learns on-line in a stable and efficient manner. The system uses a control structure similar to that found in the Adaptive Resonance Theory (ART-1) network to identify the cluster centers initially. The initial classification of an input takes place in a two stage process; a simple competitive stage and a distance metric comparison stage. The cluster prototypes are then incrementally updated by relocating the centroid positions from Fuzzy c-Means (FCM) system equations for the centroids and the membership values. The operational characteristics of AFLC and the critical parameters involved in its operation are discussed. The performance of the AFLC algorithm is presented through application of the algorithm to the Anderson Iris data, and laser-luminescent fingerprint image data. The AFLC algorithm successfully classifies features extracted from real data, discrete or continuous, indicating the potential strength of this new clustering algorithm in analyzing complex data sets. The hybrid neuro-fuzzy AFLC algorithm will enhance analysis of a number of difficult recognition and control problems involved with Tethered Satellite Systems and on-orbit space shuttle attitude controller

Fuzzy ART: Fast Stable Learning and Categorization of Analog Patterns by an Adaptive Resonance System

A Fuzzy ART model capable of rapid stable learning of recognition categories in response to arbitrary sequences of analog or binary input patterns is described. Fuzzy ART incorporates computations from fuzzy set theory into the ART 1 neural network, which learns to categorize only binary input patterns. The generalization to learning both analog and binary input patterns is achieved by replacing appearances of the intersection operator (n) in AHT 1 by the MIN operator (Λ) of fuzzy set theory. The MIN operator reduces to the intersection operator in the binary case. Category proliferation is prevented by normalizing input vectors at a preprocessing stage. A normalization procedure called complement coding leads to a symmetric theory in which the MIN operator (Λ) and the MAX operator (v) of fuzzy set theory play complementary roles. Complement coding uses on-cells and off-cells to represent the input pattern, and preserves individual feature amplitudes while normalizing the total on-cell/off-cell vector. Learning is stable because all adaptive weights can only decrease in time. Decreasing weights correspond to increasing sizes of category "boxes". Smaller vigilance values lead to larger category boxes. Learning stops when the input space is covered by boxes. With fast learning and a finite input set of arbitrary size and composition, learning stabilizes after just one presentation of each input pattern. A fast-commit slow-recode option combines fast learning with a forgetting rule that buffers system memory against noise. Using this option, rare events can be rapidly learned, yet previously learned memories are not rapidly erased in response to statistically unreliable input fluctuations.British Petroleum (89-A-1204); Defense Advanced Research Projects Agency (90-0083); National Science Foundation (IRI-90-00530); Air Force Office of Scientific Research (90-0175

Fuzzy clustering with volume prototypes and adaptive cluster merging

Two extensions to the objective function-based fuzzy clustering are proposed. First, the (point) prototypes are extended to hypervolumes, whose size can be fixed or can be determined automatically from the data being clustered. It is shown that clustering with hypervolume prototypes can be formulated as the minimization of an objective function. Second, a heuristic cluster merging step is introduced where the similarity among the clusters is assessed during optimization. Starting with an overestimation of the number of clusters in the data, similar clusters are merged in order to obtain a suitable partitioning. An adaptive threshold for merging is proposed. The extensions proposed are applied to Gustafson–Kessel and fuzzy c-means algorithms, and the resulting extended algorithm is given. The properties of the new algorithm are illustrated by various examples