Classification of hyperspectral data from urban areas based on extended morphological profiles

By Jon Atli Benediktsson, Jon Aevar Palmason, Johannes R. Sveinsson. IEEE Transactions on Geoscience and Remote Sensing, 2005

1,373 citations recorded by the source corpus.

Subfields: Analytical Chemistry, Atmospheric Science, Media Technology

View Paper: https://www.semanticscholar.org/paper/c5389f0d9751ce9cd39160320ef17ae968a79edf

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Citations in the corpus

Kernel Principal Component Analysis for the Classification of Hyperspectral Remote Sensing Data over Urban Areas (2009, background): “In [26], it was suggested to use several principal components (PCs) of the hyperspectral data for such a purpose.”
Spatial-Spectral Transformer for Hyperspectral Image Classification (2021, background): “Numerous morphological operations have been developed to extract the spatial features of HSI for following spatial–spectral classification, such as morphological profiles (MPs) [15], extended MPs (EMPs) [16], extended multi-attribute profile (EMAP) [17], and extinction profiles (EPs) [18].”
A Survey on Spectral–Spatial Classification Techniques Based on Attribute Profiles (2015, methodology): “In [21], the concept of MPs was successfully extended to handle hyperspectral images by using PCA for reducing the dimensionality of the hyperspectral data.”
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-”
Land-cover classification with high-resolution remote sensing images using transferable deep models (2018, background): “Speciﬁcally, a set of four diﬀerent features are exploited, including spectral feature, gray-level co-occurrence matrix (GLCM) [99], diﬀerential morphological proﬁles (DMP) [100], and local binary patterns (LBP) [101].”
MambaHSI: Spatial–Spectral Mamba for Hyperspectral Image Classification (2025, methodology): “For example, the superpixel segmentation [34], the extended morphological profiles [35], and multiple kernel learning [36] are adopted to generate discriminative spectral–spatial features for classification.”
Multiple Feature Learning for Hyperspectral Image Classification (2015, background): “Other nonlinear approaches are focused on adequately exploiting the spatial information contained in the scene, e.g., using morphological analysis [13], [14].”
A comparative study on multivariate mathematical morphology (2007, background): “Or in [14], where hyperspectral remote sensing images are classified by means of morphological profiles computed marginally from the first two principal components of the input.”
Recent Advances on Spectral–Spatial Hyperspectral Image Classification: An Overview and New Guidelines (2018, methodology): “Another representative collection of fixed dependency instantiations is related to MP-based methods [6], [13]–[15], [40], [41], which utilized mathematical morphology operators built on preset structural elements to obtain extended morphological MPs (EMAPs) that are used as features for classificati”
Spectral–Spatial Classification of Hyperspectral Data Based on Deep Belief Network (2015, neutral): “2) Comparing With Other Spatial Method: Extended morphological profile (EMP) has been developed in recent years, which integrates spatial information into spectral-based classifiers [53].”
Ensemble manifold regularized sparse low-rank approximation for multiview feature embedding (2015, methodology): “The input three kinds of features are extracted by the following approaches [37,38]: (1) 191-D spectral feature, by arranging a pixel's digital number (DN) in all of the spectral bands, (2) 60-D Gabor wavelet based texture feature over the top principal component of HSI (scale1⁄45, direction1⁄412), ”
A Global Human Settlement Layer From Optical HR/VHR RS Data: Concept and First Results (2013, background): “Moreover, the capacity to detect and describe the characteristics of the single BU structures using DMP and DMP-derived image features were also previously tested in a number of experiments including optical data [31]–[33], hyperspectral data [34], and radar data [35].”
Recent Advances in Techniques for Hyperspectral Image Processing (2009, methodology): “It was suggested in ( Benediktsson et al., 2005 ) that the flrst principal components (PCs) of the hyperspectral data could be used for this purpose.”