A Survey on Spectral–Spatial Classification Techniques Based on Attribute Profiles

By Pedram Ghamisi, M. Dalla Mura, Jon Atli Benediktsson. IEEE Transactions on Geoscience and Remote Sensing, 2015

382 citations recorded by the source corpus.

Subfields: Atmospheric Science, Biomedical Engineering, Media Technology

View Paper: https://www.semanticscholar.org/paper/d9020502e0a1e6d5a213d93bcb198ff822cdad39

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Citations in the corpus

A survey: Deep learning for hyperspectral image classification with few labeled samples (2021, background): “Since deep learning-based methods are mainly concerned in this paper, the readers are referred to [27] for more details on these conventional techniques.”
Advances in Hyperspectral Image and Signal Processing: A Comprehensive Overview of the State of the Art (2017, background): “A comprehensive survey on the use of APs for HSI classification can be found in [113] and [4].”
PCA-Based Edge-Preserving Features for Hyperspectral Image Classification (2017, methodology): “tion methods have been studied extensively in recent years [20], [22], [23].”
Deep Recurrent Neural Networks for Hyperspectral Image Classification (2017, background): “To benefit from this type of data, supervised hyperspectral image classification plays a significant role and has been investigated in many applications, including urban development [2]–[5], the monitoring of land changes [6]–[9], scene interpretation [10]–[13], and resource management [14], [15].”
Deep Few-Shot Learning for Hyperspectral Image Classification (2019, background): “Recent studies have also demonstrated that incorporating the spatial information into pixelwise classiﬁers is helpful to improve the classiﬁcation performance [10].”
Feature Extraction for Hyperspectral Imagery: The Evolution From Shallow to Deep: Overview and Toolbox (2020, background): “The third group includes an impressive number of approaches based on mathematical morphology, which hierarchically extract spatial and contextual information from the input image and usually leads to a significant increase in the number of features [18].”
Fully Convolutional Networks for Dense Semantic Labelling of High-Resolution Aerial Imagery (2016, background): “There has been a signiﬁcant amount of past work on classiﬁcation and segmentation of remote sensing imagery, for a recent review see [1, 10].”
Hyperspectral Anomaly Detection With Attribute and Edge-Preserving Filters (2017, methodology): “successfully applied in the hyperspectral image classification field [19], [22].”