A new approach for the morphological segmentation of high-resolution satellite imagery

By Martino Pesaresi, Jon Atli Benediktsson. IEEE Transactions on Geoscience and Remote Sensing, 2001

919 citations recorded by the source corpus.

Subfields: Computer Vision and Pattern Recognition

View Paper: https://www.semanticscholar.org/paper/a9add89c424065562b6cf8c7e9ded69af8429cd8

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In Computer Vision and Pattern Recognition

Citations in the corpus

A comprehensive review of earthquake-induced building damage detection with remote sensing techniques (2013, background): “Morphological features have been a focus for building detection research in urban areas for a long time (Pesaresi and Benediktsson, 2001; Benediktsson et al., 2003; Huang and Zhang, 2012).”
Dimensionality reduction and classification of hyperspectral image data using sequences of extended morphological transformations (2005, methodology): “Morphological profiles for grayscale (monochannel) remotely sensed imagery rely on the sequential application of opening and closing by reconstruction operations using SEs of increasing size [19], [20].”
A Survey on Spectral–Spatial Classification Techniques Based on Attribute Profiles (2015, methodology): “In [13], morphological transformations were used to build an MP.”
Evaluation of Morphological Texture Features for Mangrove Forest Mapping and Species Discrimination Using Multispectral IKONOS Imagery (2009, positive): “Morphological features have been successfully applied to urban VHR images [5], [11].”
Morphological Building/Shadow Index for Building Extraction From High-Resolution Imagery Over Urban Areas (2012, methodology): “The differential morphological profiles (DMP) [2] of the white top-hat are used to represent the local contrast of bright structures.”
Hyperspectral Image Classification—Traditional to Deep Models: A Survey for Future Prospects (2021, methodology): “method based RNN framework was introduced which first extracts low-level features from HSI by using Gabor filter and differential morphological profiles [108] and then fuse these features together to obtain LSS features from the proposed method, these LSS features are further passed to an RNN model ”
A combined fuzzy pixel-based and object-based approach for classification of high-resolution multispectral data over urban areas (2003, methodology): “A segmentation method for high-resolution satellite imagery using residuals of morphological opening and closing transforms is presented in [17], and a neural-network-based approach for classifying urban areas using this morphological segmentation is given in [18].”
Semisupervised Classification of Remote Sensing Images With Active Queries (2012, background): “Moreover, the fixed shape of the structuring elements also produces inaccuracies in the final classification (see [36]).”
Linear Versus Nonlinear PCA for the Classification of Hyperspectral Data Based on the Extended Morphological Profiles (2012, positive): “Morphological profiles (MP), which combine spectral and spatial information, have been shown to be effective tools for classification of remote sensing data [1] [2] [3] [4] [5] [6].”
Remote Sensing Image Retrieval With Global Morphological Texture Descriptors (2014, background): “, differential [32], extended [33], attribute based [3], [34]) have become one of the most suc-”
Advances in mathematical morphology applied to geoscience and remote sensing (2002, background): “For example, morphological profiles obtained by performing opening and closing by reconstruction using square structuring elements of increasing size have been recently proposed in [56] to”
Segmentation and classification of hyperspectral images using watershed transformation (2010, methodology): “In previous studies, the spatial context was included in the classification system by using morphological filters [15], morphological leveling [18] and Markov random fields [19].”
Classification of hyperspectral data from urban areas based on extended morphological profiles (2005, methodology): “Jón Atli Benediktsson (S'86–”
A comparative study on multivariate mathematical morphology (2007, methodology): “Feature extraction is realised with the help of differential morphological profiles (DMP), an approach employed successfully in the field of remote sensing [65] for classifying panchromatic urban data.”
Support Vector Machine Versus Random Forest for Remote Sensing Image Classification: A Meta-Analysis and Systematic Review (2020, methodology): “The successful use of RF or SVM, coupled with a feature extraction approach to model a machine learning framework has been demonstrated intensively in the literature [145], [146].”