A Neural Network Method for Mixture Estimation for Vegetation Mapping
Open Access
- 24 November 1999
- journal article
- Published by Elsevier in Remote Sensing of Environment
- Vol. 70 (2), 138-152
- https://doi.org/10.1016/s0034-4257(99)00027-9
Abstract
No abstract availableKeywords
This publication has 24 references indexed in Scilit:
- Topographic Normalization of Landsat TM Images of Forest Based on Subpixel Sun–Canopy–Sensor GeometryRemote Sensing of Environment, 1998
- Non-linear mixture modelling without end-members using an artificial neural networkInternational Journal of Remote Sensing, 1997
- ART neural networks for remote sensing: vegetation classification from Landsat TM and terrain dataIEEE Transactions on Geoscience and Remote Sensing, 1997
- Vegetation canopy reflectance modeling—recent developments and remote sensing perspectives∗Remote Sensing Reviews, 1997
- Artificial neural network response to mixed pixels in coarse-resolution satellite dataRemote Sensing of Environment, 1996
- Nonlinear spectral mixing in desert vegetationRemote Sensing of Environment, 1996
- Mapping forest vegetation using Landsat TM imagery and a canopy reflectance modelRemote Sensing of Environment, 1994
- Geometric-optical bidirectional reflectance modeling of the discrete crown vegetation canopy: effect of crown shape and mutual shadowingIEEE Transactions on Geoscience and Remote Sensing, 1992
- Fuzzy ARTMAP: A neural network architecture for incremental supervised learning of analog multidimensional mapsIEEE Transactions on Neural Networks, 1992
- I. Problems and Designs of Cross-Validation 1Educational and Psychological Measurement, 1951