What size net gives valid generalization?
Neural Computation
Learning internal representations by error propagation
Parallel distributed processing: explorations in the microstructure of cognition, vol. 1
Pattern recognition: statistical, structural and neural approaches
Pattern recognition: statistical, structural and neural approaches
Adaptive resonance theory (ART)
The handbook of brain theory and neural networks
Practical genetic algorithms
An introduction to support Vector Machines: and other kernel-based learning methods
An introduction to support Vector Machines: and other kernel-based learning methods
ICIS '00 Proceedings of the twenty first international conference on Information systems
Neural Networks for Pattern Recognition
Neural Networks for Pattern Recognition
Handbook of Neural Network Signal Processing
Handbook of Neural Network Signal Processing
Self-Organizing Maps
Neural Nets: Applications in Geography
Neural Nets: Applications in Geography
Detection of land-cover transitions by combining multidate classifiers
Pattern Recognition Letters - Special issue: Pattern recognition for remote sensing (PRRS 2002)
Segmentation of remote-sensing images by incremental neural network
Pattern Recognition Letters
Neural Networks: A Comprehensive Foundation (3rd Edition)
Neural Networks: A Comprehensive Foundation (3rd Edition)
Fast learning in networks of locally-tuned processing units
Neural Computation
Artificial neural networks to predict corn yield from Compact Airborne Spectrographic Imager data
Computers and Electronics in Agriculture
Increasing the accuracy of neural network classification using refined training data
Environmental Modelling & Software
How many hidden layers and nodes?
International Journal of Remote Sensing
Computers and Electronics in Agriculture
Expert Systems with Applications: An International Journal
Inductive pattern-based land use/cover change models: A comparison of four software packages
Environmental Modelling & Software
| Hi-index | 0.00 |
Artificial neural networks (ANNs) have become a popular tool in the analysis of remotely sensed data. Although significant progress has been made in image classification based upon neural networks, a number of issues remain to be resolved. This paper reviews remotely sensed data analysis with neural networks. First, we present an overview of the main concepts underlying ANNs, including the main architectures and learning algorithms. Then, the main tasks that involve ANNs in remote sensing are described. The limitations and crucial issues relating to the application of the neural network approach are discussed. A brief review of the implementation of ANNs in some of the most popular image processing software packages is presented. Finally, we discuss the application perspectives of neural networks in remote sensing image analysis.