Semi-Supervised Learning with Graphs: Covariance Based Superpixels For Hyperspectral Image Classification

Sellars, Philip; Avilés-Rivero, Angelica I.; Papadakis, Nicolas; Coomes, David A.; Faul, A. C.; Schönlieb, Carola‐Bibiane

doi:10.1109/igarss.2019.8898189

Cited by 4 publications

(8 citation statements)

References 12 publications

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“…factors affecting the classification results. In existing superpixel-based, pixel-wise HSI classifiers and four superpixel-level HSI classification approaches [35][36][37]46], the optimal number of superpixels is determined using the classification results of reference data. However, the hyperspectral data obtained in practice have little reference data.…”

Section: Impact Of the Number Of Updates On The Classification Resultsmentioning

confidence: 99%

“…To our best knowledge, there are four superpixel-level HSI classification methods [35][36][37]46]. Compared with the approaches proposed in References [35,46], the advantage of the proposed SSC-GDP classification framework is that it is nonparametric and easy to calculate. The use of a local connection strategy in the graph construction, as well as the linear complexity of the ISWH algorithm, makes our proposal superior to the method developed by Xie et al [37].…”

Section: Impact Of the Number Of Updates On The Classification Resultsmentioning

confidence: 99%

“…A large amount of work has proved that graph-based methods are popular and effective techniques in HSI classification [38][39][40][41][42][43][44][45][46] because graphs have the advantage of an amazing and flexible data presentation. In the graph-based HSI classification methods, nodes in the generated graph can represent pixels [38][39][40][41][42][43][44][45] or superpixels [46]. Considering superpixels as nodes in a graph implies a sharp reduction in the volume of the constructed graph.…”

Section: The Construction Of the Weighted Connectivity Graphmentioning

confidence: 99%

“…This method adopted a graph-based label propagation algorithm to predict the pixel labels and modified some mislabeled pixels using superpixels. Taking each superpixel as a node, Sellars et al [46] proposed a graph-based learning framework for HSI classification. The final classification was completed using a local and global consistency algorithm.…”

Section: Introductionmentioning

confidence: 99%

“…The final classification was completed using a local and global consistency algorithm. In most of the existing graph-based classification methods, the construction of graphs is at the pixel level, except for the method proposed by Sellars et al [46]. Generally speaking, the classification result and speed of these graph-based methods depend largely on the construction and partitioning of the graph.…”

Section: Introductionmentioning

confidence: 99%

See 4 more Smart Citations

Novel Semi-Supervised Hyperspectral Image Classification Based on a Superpixel Graph and Discrete Potential Method

Zhao

Yan

2020

Remote Sensing

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Hyperspectral image (HSI) classification plays an important role in the automatic interpretation of the remotely sensed data. However, it is a non-trivial task to classify HSI accurately and rapidly due to its characteristics of having a large amount of data and massive noise points. To address this problem, in this work, a novel, semi-supervised, superpixel-level classification method for an HSI was proposed based on a graph and discrete potential (SSC-GDP). The key idea of the proposed scheme is the construction of the weighted connectivity graph and the division of the weighted graph. Based on the superpixel segmentation, a weighted connectivity graph is constructed usingthe weighted connection between a superpixel and its spatial neighbors. The generated graph is then divided into different communities/sub-graphs by using a discrete potential and the improved semi-supervised Wu–Huberman (ISWH) algorithm. Each community in the weighted connectivity graph represents a class in the HSI. The local connection strategy, together with the linear complexity of the ISWH algorithm, ensures the fast implementation of the suggested SSC-GDP method. To prove the effectiveness of the proposed spectral–spatial method, two public benchmarks, Indian Pines and Salinas, were utilized to test the performance of our proposal. The comparative test results confirmed that the proposed method was superior to several other state-of-the-art methods.

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Section: Impact Of the Number Of Updates On The Classification Resultsmentioning

confidence: 99%

Section: Impact Of the Number Of Updates On The Classification Resultsmentioning

confidence: 99%

Section: The Construction Of the Weighted Connectivity Graphmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

See 3 more Smart Citations

Novel Semi-Supervised Hyperspectral Image Classification Based on a Superpixel Graph and Discrete Potential Method

Zhao

Yan

2020

Remote Sensing

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Visual Classification of Lettuce Growth Stage based on Morphological Attributes using Unsupervised Machine Learning models

Alejandrino

Concepcion

Lauguico

et al. 2020

2020 Ieee Region 10 Conference (Tencon)

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Superpixel Contracted Graph-Based Learning for Hyperspectral Image Classification

Sellars

Avilés-Rivero

Schönlieb

2020

IEEE Trans. Geosci. Remote Sensing

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A central problem in hyperspectral image classification is obtaining high classification accuracy when using a limited amount of labelled data. In this paper we present a novel graph-based framework, which aims to tackle this problem in the presence of large scale data input. Our approach utilises a novel superpixel method, specifically designed for hyperspectral data, to define meaningful local regions in an image, which with high probability share the same classification label. We then extract spectral and spatial features from these regions and use these to produce a contracted weighted graph-representation, where each node represents a region rather than a pixel. Our graph is then fed into a graph-based semi-supervised classifier which gives the final classification. We show that using superpixels in a graph representation is an effective tool for speeding up graphical classifiers applied to hyperspectral images. We demonstrate through exhaustive quantitative and qualitative results that our proposed method produces accurate classifications when an incredibly small amount of labelled data is used. We show that our approach mitigates the major drawbacks of existing approaches, resulting in our approach outperforming several comparative state-of-theart techniques.

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Semi-Supervised Learning with Graphs: Covariance Based Superpixels For Hyperspectral Image Classification

Cited by 4 publications

References 12 publications

Novel Semi-Supervised Hyperspectral Image Classification Based on a Superpixel Graph and Discrete Potential Method

Novel Semi-Supervised Hyperspectral Image Classification Based on a Superpixel Graph and Discrete Potential Method

Visual Classification of Lettuce Growth Stage based on Morphological Attributes using Unsupervised Machine Learning models

Superpixel Contracted Graph-Based Learning for Hyperspectral Image Classification

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