Advances in mathematical morphology applied to geoscience and remote sensing

By Pierre Soille, Martino Pesaresi. IEEE Transactions on Geoscience and Remote Sensing, 2002

360 citations recorded by the source corpus.

View Paper: https://www.semanticscholar.org/paper/2c5d1eeade42c67c2f9c8e4e0a279850a5a7b088

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Citations in the corpus

A comparative study on multivariate mathematical morphology (2007, background): “On the other hand, according to [80] the main inconvenience of a total ordering obtained in this fashion is its lack of physical interpretation.”
A Survey on Spectral–Spatial Classification Techniques Based on Attribute Profiles (2015, methodology): “In more detail, one MP is built using disk-shaped SEs for extracting the smallest size of the structures, whereas the other employs linear SEs (which generate directional profiles [23]) for characterizing the object’s maximum size (along with the orientation of the SE).”
Classification of hyperspectral data from urban areas based on extended morphological profiles (2005, methodology): “The proposed approach is based on mathematical morphology [8], [9], [19] and has recently been used successfully in classification of panchromatic urban data.”
Dimensionality reduction and classification of hyperspectral image data using sequences of extended morphological transformations (2005, unknown): “Morphological directional profiles were defined in [30] by performing openings and closings with line segment SEs of varying orientation [31].”
Remote Sensing Image Retrieval With Global Morphological Texture Descriptors (2014, background): “The shape-oriented processing capacity of the morphological toolbox has rendered it particularly popular with remote sensing images [30], where the integration of spatial and spectral infor-”
A Novel Framework for the Design of Change-Detection Systems for Very-High-Resolution Remote Sensing Images (2013, background): “Examples of geometric primitives are lines, regions with specific size or shape extracted by segmentation, morphological filters [21] and attribute filters [68].”
Morphological Attribute Profiles for the Analysis of Very High Resolution Images (2010, background): “while the other employs linear SEs (which generate directional profiles [12]) for characterizing the objects maximum size (along the orientation of the SE).”
Evaluation of Morphological Texture Features for Mangrove Forest Mapping and Species Discrimination Using Multispectral IKONOS Imagery (2009, methodology): “multiscale representations of the two operators and are obtained using a series of SE sizes [13].”
Spectral and Spatial Classification of Hyperspectral Data Using SVMs and Morphological Profiles (2008, background): “An overview of operators can be found in [18].”
Classification and feature extraction for remote sensing images from urban areas based on morphological transformations (2003, background): “In fact, reconstruction filters introduce nominally no shape noise, since the shape of the structuring element used in the filtering are adaptive with respect to the structures present in the image itself [5].”