Spectral–Spatial Classification of Hyperspectral Imagery Based on Partitional Clustering Techniques

By Yuliya Tarabalka, Jon Atli Benediktsson, Jocelyn Chanussot. IEEE Transactions on Geoscience and Remote Sensing, 2009

754 citations recorded by the source corpus.

Subfields: Atmospheric Science, Media Technology

View Paper: https://www.semanticscholar.org/paper/f19122b082188e626ff8355cfcaa432e68509272

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Citations in the corpus

Deep Learning for Classification of Hyperspectral Data: A Comparative Review (2019, methodology): “This technique will then be largely adopted in later works [34], [35], [36].”
SVM-Based Fuzzy Decision Trees for Classification of High Spatial Resolution Remote Sensing Images (2012, methodology): “More accurate results are attained from object-based methods, which operate on spatially homogeneous regions formed by applying different segmentation algorithms such as the adaptive mean-shift approach [12] and expectation maximization clustering [13].”
Deep Few-Shot Learning for Hyperspectral Image Classification (2019, methodology): “…a large number of spectral–spatial feature extraction methods have been developed for HSI classiﬁcation, such as extended morphological proﬁles [11], Markov random ﬁeld [12], local binary pattern [13], Gabor wavelet ﬁlters [14], discriminant analysis [15], [16], and segmentation approaches [17].”
How can Big Data and machine learning benefit environment and water management: a survey of methods, applications, and future directions (2019, methodology): “used to differentiate materials with only slightly different spectral characteristics [64].”
Support vector machines in remote sensing: A review (2011, neutral): “Tarabalka et al. (2009) proposed a new classification scheme emphasizing both spectral and spatial characteristics of hyperspectral images.”
Deep Learning-Based Classification of Hyperspectral Data (2014, background): “To deal with spatial variability of spectral signature, some recent approaches try to incorporate spatial information into consideration [31]–[33].”
Disease Diagnosis in Smart Healthcare: Innovation, Technologies and Applications (2017, background): “It can be further divided into hierarchical clustering (divisive or agglomerative) [39], partitional clustering (centroid, model based, graph theoretic, or spectral) [40] and Bayesian clustering (decision based or non-parametric) [41].”
Hyperspectral Image Classification With Independent Component Discriminant Analysis (2011, methodology): “Moreover, the proposed method presents several other advantages: 1) Its Bayesian nature allows the integration of any kind of prior information in the classification process, as long as they can be stated as a probability function, and 2) it is suitable to be used jointly with spectral‐spatial techn”
Hyperspectral image classification via kernel sparse representation (2011, background): “in [7], in which about 9% of labeled data are used as training a d the rest are used for testing, as shown”
Spectral–Spatial Hyperspectral Image Classification via Multiscale Adaptive Sparse Representation (2014, methodology): “In [14], the spatial coherence of pixels within a local region is utilized by including a postprocessing procedure for each individual label.”
Hyperspectral Image Classification Based on Structured Sparse Logistic Regression and Three-Dimensional Wavelet Texture Features (2013, methodology): “The segmentation map is built by the use of both a clustering algorithm and Gaussian mixtures [3] and by the use of both”
Classification of Hyperspectral Images by Exploiting Spectral–Spatial Information of Superpixel via Multiple Kernels (2015, methodology): “In [25], the spatial dependence of pixels within a local region is exploited by a postprocessing procedure on each individual pixel label.”
Spectral–Spatial Hyperspectral Image Classification With Edge-Preserving Filtering (2014, positive): “A hyperspectral image is first segmented into different regions based on the homogeneity of either intensity or texture [27] so that all the pixels within the same region can be considered as a spatial neighborhood.”
Classification of Hyperspectral Images by Using Extended Morphological Attribute Profiles and Independent Component Analysis (2011, result): “As far as we know, these accuracies are higher than all those reported in the literature for this data set without postprocessing [1], [13].”
Deep Convolutional Neural Networks for Hyperspectral Image Classification (2015, methodology): “Recently, some more effective feature extraction methods as well as advanced classifiers were proposed, such as spectral-spatial classification [3] and local Fisher discriminant analysis [4].”