Hyperspectral image classification via kernel sparse representation

By Yi Chen, Nasser M. Nasrabadi, Trac Duy Tran. 2011 18th IEEE International Conference on Image Processing, 2011

474 citations recorded by the source corpus.

Subfields: Computational Mechanics, Media Technology

View Paper: https://www.semanticscholar.org/paper/4f9c359b60e2e3efeed37836f4f5839edaa78b54

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Citations in the corpus

Anomaly Detection in Hyperspectral Images Based on Low-Rank and Sparse Representation (2016, background): “The sparse-representation-based detector (SRD) is introduced in supervised hyperspectral target detection [19], [20] and assumes that each sample can be represented by a few atoms in the dictionary.”
Strathprints Institutional Repository Shuhui and Ren, Jinchang (2015) Effective and Efficient Midlevel Visual Elements-oriented Land-use Classification Using Vhr Remote Sensing Images. Ieee Transactions on Geoscience and Remote Sensing, 53 (8). Pp. 4238-4249. Issn 0196-2892 Effective and Efficient M (unknown): “Most previous works [8] [9] [10] [11] [12] [13] [14] [15] [16] [17] mainly focus on classifying pixels or objects (or rather, grouping of local homogeneous pixels) into their thematic class by extracting low-level image features (e.g. texture feature [9] [10], color feature [8], spatial and…”
CNN-Enhanced Graph Convolutional Network With Pixel- and Superpixel-Level Feature Fusion for Hyperspectral Image Classification (2020, methodology): “To make full use of spectral information, typical representation-based methods have been exploited, such as sparse representation [9]–[11], low-rank representation [12], [13], and collaborative representation [14]–[16].”
Advances in Hyperspectral Image Classification: Earth Monitoring with Statistical Learning Methods (2013, methodology): “3, we compare the performance of the linear (SP, OMP) and kernel (KSP, KOMP) sparse HSI classifiers introduced in [18].”
Hyperspectral Image Classification With Deep Learning Models (2018, methodology): “For example, the logistic regression via variable splitting and augmented Lagrangian (LORSAL) algorithm [26] is developed to tackle larger data sets efficiently.”
Collaborative Representation for Hyperspectral Anomaly Detection (2015, unknown): “(13)”
Classification of Hyperspectral Images by Exploiting Spectral–Spatial Information of Superpixel via Multiple Kernels (2015, background): “The sparse representation [13-15], which can sparsely decompose the input pixel on an over-complete dictionary, is another widely used classifier.”
Spectral–Spatial Hyperspectral Image Classification With Edge-Preserving Filtering (2014, background): “[10], AdaBoost [11], support vector machines (SVMs) [12], sparse representation [13], [14], and active learning [15]–[17] methods.”