Extended profiles with morphological attribute filters for the analysis of hyperspectral data

By M. Dalla Mura, Jón Atli Benediktsson, Björn Waske, Lorenzo Bruzzone. 2010

389 citations recorded by the source corpus.

Subfields: Atmospheric Science, Media Technology

View Paper: https://www.semanticscholar.org/paper/b1501c9a4fcf6d3e6960fafa3ae8a56f660bfb57

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In Atmospheric Science

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Citations in the corpus

A survey: Deep learning for hyperspectral image classification with few labeled samples (2021, methodology): “Then, a large number of feature extraction methods that integrate the spatial structural and texture information with the spectral features have been developed, including morphological [21, 22, 23], filtering [24, 25], coding [26], etc.”
Generalized Composite Kernel Framework for Hyperspectral Image Classification (2013, methodology): “In our experiments, Ks refers to the kernel-based classification obtained using only the spatial information provided by the EMAPs, i.e., xsi is obtained by applying an EMAP-based approach to the original hyperspectral image.”
Classification of Hyperspectral Images by Exploiting Spectral–Spatial Information of Superpixel via Multiple Kernels (2015, methodology): “In the MLR-GCK, the spatial context of HSI was exploited by the extended multiattribute profile [43] and the generalized composite kernel.”
Multiple Feature Learning for Hyperspectral Image Classification (2015, result): “This is consistent with previous works, revealing the power of EMAP for separating most classes which are nonlinearly separable in the spatial domain [27], [34].”
Deep Learning for Classification of Hyperspectral Data: A Comparative Review (2019, neutral): “, tree-like segmentations, are often used to derive local morphological features, such as in the popular morphological attribute profile approach [30].”
Classification of Hyperspectral Images by Using Extended Morphological Attribute Profiles and Independent Component Analysis (2011, neutral): “vector of features [also called extended multi-AP (EMAP) [6]; see Fig.”
Hyperspectral and LiDAR Data Fusion: Outcome of the 2013 GRSS Data Fusion Contest (2014, background): “The recent work in [38] applied extended morphological attribute profiles (EAPs) [39] to both hyperspectral and LiDAR data for classification tasks, where the EAP features were extracted from both hyperspectral and LiDAR data, and used together with spectral, spatial, and elevation information in a…”
A Review of Remote Sensing Image Classification Techniques: the Role of Spatio-contextual Information (2014, background): “Especially, morphological profiling [Fauvel et al., 2008] and morphological attribute filters [Dalla Mura et al., 2010] have been developed to capture geometrical and multi-scale properties [Moser et al., 2013].”
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-”
Hyperspectral Image Classification With Deep Learning Models (2018, methodology): “In [22], the CK method is generalized using extended multiattribute profiles (EMAPs).”