The use of backpropagating artificial neural networks in land cover classification

By Taskin Kavzoglu, Paul M. Mather. 2003

434 citations recorded by the source corpus.

Subfields: Artificial Intelligence, Ecology, Media Technology

View Paper: https://www.semanticscholar.org/paper/d2769a3ad69885d454c8de281213b2d2ef23056c

How researchers see this paper

The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.

In Artificial Intelligence

1. Gustau Camps-Valls
2. Jordi Muñoz-Marí
3. José F. Moreno
4. Alexandre Benoit
5. Frank Veroustraete
6. C. Ben Amar
7. Bogdan Zagajewski
8. Fang Fang

In Ecology

1. Gustau Camps-Valls
2. Jan G. P. W. Clevers
3. José F. Moreno
4. Jochem Verrelst
5. Juan Pablo Rivera
6. Jordi Muñoz-Marí
7. Frank Veroustraete
8. Changshan Wu

In Media Technology

1. Gustau Camps-Valls
2. Jordi Muñoz-Marí
3. Alexandre Benoit
4. C. Ben Amar
5. Changshan Wu
6. Bogdan Zagajewski
7. Fang Fang
8. Jochem Verrelst

Citations in the corpus

• A Review of Remote Sensing Image Classification Techniques: the Role of Spatio-contextual Information (2014, methodology): “…have also been developed to refine the knowledge learning process [Mountrakis et al., 2011], and these methods include artificial neural network [Kavzoglu and Mather, 2003], classification tree [Friedl and Brodley, 1997; Mclver and Friedl, 2002; Jiang et al., 2012], random forests [Gislason et…”
• 3-D Deep Learning Approach for Remote Sensing Image Classification (2018, background): “artificial neural networks [1], classification trees [2], and support vector machine [3].”
• Comparison of support vector machine, random forest and neural network classifiers for tree species classification on airborne hyperspectral APEX images (2017, background): “Some studies recommended at least 400 pixels per class as the optimal number of pixels for classification purposes (Kavzoglu & Mather, 2003).”
• Optical remote sensing and the retrieval of terrestrial vegetation bio-geophysical properties - A review (2015, background): “Feed-forward ANNs have a long track record in mapping vegetation properties based on RS data (Kavzoglu and Mather, 2003).”
• Implementation of machine-learning classification in remote sensing: an applied review (2018, background): “For example, the many user-de fi ned parameters of a back-propagation ANN (Table 10) make optimization a challenge (Foody and Arora 1997; Kavzoglu and Mather 2003; Pal, Maxwell, and Warner 2013).”
• Forecasting of meteorological drought using Wavelet-ANFIS hybrid model for different time steps (case study: southeastern part of east Azerbaijan province, Iran) (2013, result): “This result is in contrast to the suggestion that was expressed by Kavzoglu and Mather (2003).”
• Prediction of sweetness and amino acid content in soybean crops from hyperspectral imagery (2007, background): “The use of more than one hidden layer was reported by Kavzoglu and Mather (2003) to provide no significant improvement in the performance of classifiers and many empirical methods have been proposed to determine the optimal number of neurons.”