A comparison of the classification of wetland characteristics by linear spectral mixture modelling and traditional hard classifiers on multispectral remotely sensed imagery in southern India

By P. Shanmugam, Y. Ahn, S. Sanjeevi. 2006

129 citations recorded by the source corpus.

Subfields: Atmospheric Science, Ecology, Media Technology

View Paper: https://www.semanticscholar.org/paper/c7bf75c7b8a00ecf124410a12c751c69e0e9ae23

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. Markus Metz
2. Giles M. Foody
3. Changshan Wu
4. Miao Li
5. Shuying Zang
6. Miska Luoto
7. Bing Zhang
8. Shanshan Li

In Ecology

1. Duccio Rocchini
2. Thomas W. Gillespie
3. Markus Metz
4. A. Gutierrez
5. Giles M. Foody
6. Changshan Wu
7. Miao Li
8. Harini Nagendra

In Media Technology

1. Duccio Rocchini
2. Thomas W. Gillespie
3. Giles M. Foody
4. Changshan Wu
5. Miao Li
6. Shuying Zang
7. Bing Zhang
8. Shanshan Li

Citations in the corpus

• A Review of Remote Sensing Image Classification Techniques: the Role of Spatio-contextual Information (2014, background): “As a result, the applications of pixel-wise hard classifications decrease the classification accuracy of land use land cover maps [Zhang and Foody, 1998; Pu et al., 2003; Shanmugam et al., 2006].”
• Are landscapes as crisp as we may think (2007, background): “…which are at the same time both spatially continuous and hierarchically organized (Woodcock and Strahler, 1987) no matter how accurately map classes are defined, the uncertainty associated to class mixtures will be never completely eliminated (Costa Fonte and Lodwick, 2004; Shanmugam et al., 2006).”
• Potential of remote sensing to predict species invasions (2015, methodology): “…classification that aggregates pixels into classes based on spectral values with numerous clustering algorithms, including Isodata and K-means (Shanmugam et al., 2006); ii) maximum likelihood (e.g. Carter et al., 2009), which simply estimates each class mean and variation from training data;…”
• Remotely sensed spectral heterogeneity as a proxy of species diversity: Recent advances and open challenges (2010, methodology): “This has inevitably led to the application of several techniques based on robust theoretical backgrounds for classifying images while avoiding Boolean memberships, relying essentially on mixturemodeling (Small, 2005;Okeke andKarnieli, 2006; Shanmugam et al., 2006; Nichol and Wong, 2007; Bino et al.,”