A combined fuzzy pixel-based and object-based approach for classification of high-resolution multispectral data over urban areas

By Aaron K. Shackelford, C. Davis. IEEE Transactions on Geoscience and Remote Sensing, 2003

400 citations recorded by the source corpus.

Subfields: Atmospheric Science, Media Technology, Ocean Engineering

View Paper: https://www.semanticscholar.org/paper/cf1ad2c8d7398e0b146b666dd20ca24e3f43bea3

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. Wenzhi Zhao
2. Shihong Du
3. Anil M. Cheriyadat
4. Lorenzo Carlin
5. Lorenzo Bruzzone
6. Geoffrey J. Hay
7. Maggi Kelly
8. Thomas Blaschke

In Media Technology

1. Liangpei Zhang
2. Wenzhi Zhao
3. Shihong Du
4. Thomas Blaschke
5. Anil M. Cheriyadat
6. Lorenzo Carlin
7. Dirk Tiede
8. Lorenzo Bruzzone

In Ocean Engineering

1. Lorenzo Bruzzone
2. Shihong Du
3. Wenzhi Zhao
4. Weimin Huang
5. Brian G. Brisco
6. Anil M. Cheriyadat
7. Sahel Mahdavi
8. Meisam Amani

Citations in the corpus

• Comparison of pixel‐based and object‐oriented image classification approaches—a case study in a coal fire area, Wuda, Inner Mongolia, China (2006, background): “The data vectors typically consist of a pixel’s grey level values from multi‐spectral channels (Shackelford and Davis 2003 ).”
• Unsupervised Feature Learning for Aerial Scene Classification (2014, background): “Similarly, Shackelford and Davis [5] combined both pixel- and objectbased features to generate object-level classification of the image.”
• Geographic Object-Based Image Analysis – Towards a new paradigm (2014, result): “…articles that have shown that object-based classification results (especially of H-resolution imagery) are consistently better than those based on traditional pixel-based approaches (e.g., Shackelford and Davis, 2003; Yu et al, 2006; Blaschke, 2010; Myint et al., 2011; Whiteside et al., 2011).”
• A Multilevel Context-Based System for Classification of Very High Spatial Resolution Images (2006, methodology): “In [16], an object-based approach is proposed for classification”
• Remote sensing for wetland classification: a comprehensive review (2018, background): “…advantages some of which are reviewed below: ● By applying OBIA, it is possible to include many object-based features, such as textural, geometrical, and morphological features into classification in addition to the spectral features (Shackelford and Davis 2003; Reif et al. 2009; Dronova 2015).”
• Scene classification using multi-scale deeply described visual words (2016, background): “Recently, intensive studies have been conducted in remote-sensing image classi fi cation and interpretation (Bellens et al. 2008; Blaschke 2010; Bruzzone and Carlin 2006; Shackelford and Davis 2003).”