Decision tree regression for soft classification of remote sensing data

By Min Xu, P. Watanachaturaporn, Pramod K. Varshney, M. Arora. 2005

589 citations recorded by the source corpus.

Subfields: Atmospheric Science, Computer Vision and Pattern Recognition, Media Technology

View Paper: https://www.semanticscholar.org/paper/4f303b9c26121f8dc630170f1029be77c2c6b11b

How researchers see this paper

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

In Atmospheric Science

1. E. Kutlug Sahin
2. Taskin Kavzoglu
3. Ismail Colkesen
4. José Manuel Peñá-Barragán
5. Johan Six
6. Changshan Wu
7. Miao Li
8. Shuying Zang

In Computer Vision and Pattern Recognition

1. José Manuel Peñá-Barragán
2. Johan Six
3. Changshan Wu
4. Taskin Kavzoglu
5. Miao Li
6. Shuying Zang
7. Ismail Colkesen
8. Richard E. Plant

In Media Technology

1. E. Kutlug Sahin
2. Ismail Colkesen
3. Taskin Kavzoglu
4. José Manuel Peñá-Barragán
5. Mei Yu
6. Johan Six
7. Changshan Wu
8. Miao Li

Citations in the corpus

• Object-based crop identification using multiple vegetation indices, textural features and crop phenology (2011, methodology): “We used themeasure performance over separate validation dataset to prevent overfitting by post-pruning of the full tree and avoiding splits not statistically significant (Schaffer, 1993; Xu et al., 2005).”
• Remote sensing imagery in vegetation mapping: a review (2008, methodology): “Unlike implementing per-pixel-based classifier to produce crisp or hard classification, Xu et al. (2005) employed a decision tree (DT) derived from the regression approach to determine class proportions within a pixel so as to produce a soft classification .”
• An assessment of multivariate and bivariate approaches in landslide susceptibility mapping: a case study of Duzkoy district (2015, background): “The process continues until each node reaches a user-specified minimum node size (i.e., the number of training samples at the node) (Xu et al. 2005).”
• A Review of Remote Sensing Image Classification Techniques: the Role of Spatio-contextual Information (2014, methodology): “As the classic remote sensing image classification technique, pixel-wise classification methods assume each pixel is pure and typically labeled as a single land use land cover type [Fisher, 1997; Xu et al., 2005] (see Tab.”
• State-of-the-art review of soft computing applications in underground excavations (2020, background): “The process continues until each node reaches a user-speci fi ed minimum node size (Xu et al., 2005).”