Artificial Neural Networks and Remote Sensing
Bibliographic Data
| ID | 5877628 |
|---|---|
| Authors | Ryan R Jensen (0000-0002-5273-1214, Brigham Young University), Perry J Hardin (Brigham Young University), Genong Yu (0000-0002-3830-4654, George Mason University) |
| Year | 2009 |
| Volume | 3 |
| Issue | 2 |
| Pages | 630-646 |
| Publication date | 2009-03-01 |
| Open Access | Yes |
| Type | ARTICLE |
| Venue | Geography Compass (JOURNAL) |
| Journal identifiers | ISSN: 1749-8198 • E-ISSN: 1749-8198 |
| Publisher | Wiley (PUBLISHER • GB) |
| DOI | 10.1111/j.1749-8198.2008.00215.x |
| OpenAlex | W2026654426 |
| Language | EN |
| References cited | 55 |
Accurate land cover classifications and biophysical estimations derived from remotely sensed data are important to generate map products and provide information about the Earth's changing surface. Statistical classifiers are often used to generate many of these data, but these classifiers rely on assumptions that may limit their utility for many datasets. Conversely, Artificial Neural Networks (ANNs) provide an accurate way for researchers to classify land cover and estimate biophysical properties of earthly phenomena without having to rely on statistical procedures or assumptions. This article describes the role of ANNs in remote sensing from their inception and contrasts ANNs with statistical remote sensing classifiers. We conclude that ANNs will continue to play a crucial role in remote sensing because of their many benefits
Artificial neural network · Geography · Land cover · Land use · Machine learning · Remote sensing · Computer Science · Engineering · Mathematics · Remote Sensing in Agriculture · Remote-Sensing Image Classification · Soil Geostatistics and Mapping · Artificial Intelligence
| Highly cited | No |
|---|