Signal Theory Methods in Multispectral Remote Sensing

By David A. Landgrebe. 2003

1,312 citations recorded by the source corpus.

View Paper: https://www.semanticscholar.org/paper/c22e733914de4477c6bd29f32234e01379df650b

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Citations in the corpus

SVM- and MRF-Based Method for Accurate Classification of Hyperspectral Images (2010, methodology): “Another group of methods from this category ﬁrst deﬁnes the objects within the image scene and then classiﬁes each object [2], [5].”
Segmentation and classification of hyperspectral images using watershed transformation (2010, methodology): “The features are used for image classification with a wide range of techniques, such as maximum-likelihood or Bayesian estimation techniques [5, 6, 7], neural networks [8, 9, 10], decision trees [11, 12], genetic algorithms [13] and kernel-based methods [14, 15, 16, 17].”
Classification and feature extraction for remote sensing images from urban areas based on morphological transformations (2003, methodology): “Several feature extraction approaches have been proposed and applied successfully in classification of remote sensing data [10].”
A survey of image classification methods and techniques for improving classification performance (2007, neutral): “…spectral mixture analysis (Myint 2001, Okin et al. 2001, Rashed et al. 2001, Asner and Heidebrecht 2002, Lobell et al. 2002, Neville et al. 2003, Landgrebe 2003, Platt and Goetz 2004) may be used for feature extraction, in order to reduce the data redundancy inherent in remotely sensed data or…”
Feature Extraction for Hyperspectral Imagery: The Evolution From Shallow to Deep: Overview and Toolbox (2020, background): “breaking fact that too many spectral bands might have negative impacts in terms of expected classification performance [7].”
Remote Sensing Image Retrieval With Global Morphological Texture Descriptors (2014, neutral): “mation is often an important issue [31].”
Spectral and Spatial Classification of Hyperspectral Data Using SVMs and Morphological Profiles (2008, methodology): “Based on the spectral signatures of the classes , many advanced pixel-based classifiers have been proposed in cluding advanced statistical classifiers [3] and distribut ion free approaches such as neural networks and support vector machines [4].”
Recent Advances in Techniques for Hyperspectral Image Processing (2009, methodology): “More precisely, let us denote by g x; yð Þ = f1 x; yð Þ; f2 x; yð Þ; N ; fm x; yð Þf g the pixel vector at spatial location (x,y) of the DAFE-reduced version g of an input hyperspectral image f, where m≤n is the number of extracted features.”
Prototype selection for dissimilarity-based classifiers (2006, background): “The first one relies on the spectral angle mapper distance (SAM) [47] defined for the spectra xi and xj as dSAM(xi , xj )= arccos(xi xj /‖xj‖2 ‖xj‖2).”
Multimodal Classification of Remote Sensing Images: A Review and Future Directions (2015, methodology): “A sudden change in its NDVI profile cannot be properly predicted using only the Landsat time series.”
Differences in leaf traits, leaf internal structure, and spectral reflectance between two communities of lianas and trees: Implications for remote sensing in tropical environments (2009, neutral): “The Bhattacharyya test statistic (B) is used as a class separability measure tominimize classification error by providing a bound of classification accuracy (Landgrebe, 2003; Richards, 2005).”