Journal ArticleDOI
Classification of multispectral remote sensing data using a back-propagation neural network
P.D. Heermann,N. Khazenie +1 more
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TLDR
The suitability of a back-propagation neural network for classification of multispectral image data is explored and a methodology is developed for selection of both training parameters and data sets for the training phase.Abstract:
The suitability of a back-propagation neural network for classification of multispectral image data is explored. A methodology is developed for selection of both training parameters and data sets for the training phase. A new technique is also developed to accelerate the learning phase. To benchmark the network, the results are compared to those obtained using three other algorithms: a statistical contextual technique, a supervised piecewise linear classifier, and an unsupervised multispectral clustering algorithm. All three techniques were applied to simulated and real satellite imagery. Results from the classification of both Monte Carlo simulation and real imagery are summarized. >read more
Citations
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Book
Remote sensing, models, and methods for image processing
TL;DR: The Nature of Remote Sensing: Introduction, Sensor Characteristics and Spectral Stastistics, and Spatial Transforms: Introduction.
Journal ArticleDOI
Machine Learning for the Detection of Oil Spills in Satellite Radar Images
TL;DR: This case study relates issues as problem formulation, selection of evaluation measures, and data preparation to properties of the oil spill application, such as its imbalanced class distribution, that are shown to be common to many applications.
Journal ArticleDOI
Global Data Sets of Vegetation Leaf Area Index (LAI)3g and Fraction of Photosynthetically Active Radiation (FPAR)3g Derived from Global Inventory Modeling and Mapping Studies (GIMMS) Normalized Difference Vegetation Index (NDVI3g) for the Period 1981 to 2011
Zaichun Zhu,Jian Bi,Yaozhong Pan,Sanmay Ganguly,Alessandro Anav,Liang Xu,Arindam Samanta,Shilong Piao,Ramakrishna R. Nemani,Ranga B. Myneni +9 more
TL;DR: Long-term global data sets of vegetation Leaf Area Index (LAI) and Fraction of Photosynthetically Active Radiation absorbed by vegetation (FPAR) are critical to understanding vegetation photosynthesis and its role in climate change.
Journal ArticleDOI
The application of artificial neural networks to the analysis of remotely sensed data
Jean-François Mas,Juan J. Flores +1 more
TL;DR: An overview of the main concepts underlying ANNs, including the main architectures and learning algorithms, are presented, and the main tasks that involve ANNs in remote sensing are described.
Journal ArticleDOI
A review and analysis of backpropagation neural networks for classification of remotely-sensed multi-spectral imagery
TL;DR: The backpropagation algorithm, which is the most common method of training multi-layer networks, is discussed with an emphasis on its application to pattern recognition.
References
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Book
Linear and nonlinear programming
David G. Luenberger,Yinyu Ye +1 more
TL;DR: Strodiot and Zentralblatt as discussed by the authors introduced the concept of unconstrained optimization, which is a generalization of linear programming, and showed that it is possible to obtain convergence properties for both standard and accelerated steepest descent methods.
Journal ArticleDOI
Accelerating the convergence of the back-propagation method
TL;DR: The back-propagation algorithm described by Rumelhart et al. (1986) can greatly accelerate convergence as discussed by the authors, however, in many applications, the number of iterations required before convergence can be large.
Journal ArticleDOI
Neural Network Approaches Versus Statistical Methods In Classification Of Multisource Remote Sensing Data
TL;DR: Experimental results show that two different approaches have unique advantages and disadvantages in this classification application of multisource remote sensing and geographic data.
Proceedings ArticleDOI
A VLSI architecture for high-performance, low-cost, on-chip learning
TL;DR: Using state-of-the-art technology and innovative architectural techniques, the author's architecture approaches the speed and cost of analog systems while retaining much of the flexibility of large, general-purpose parallel machines.