Land cover classification of GeoEye image based on Convolutional Neural Networks

Chairet, Radhia; Salem, Yassine Ben; Aoun, Mohamed

doi:10.1109/ssd49366.2020.9364254

Cited by 2 publications

(2 citation statements)

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“…Solving the above equation leads to a dense matrix of self-representing coefficients and a valid closed-form solution. We can rewrite (7) as:…”

Section: Methodology Ssakgcscmentioning

confidence: 99%

“…Supervised classification methods were the most commonly used for HSI classification [3,4]. In recent years deep learning models [5,6] and convolutional neural networks (CNNs) [7] have made significant progress in HSI classification. Due to the high cost of labeling training data, supervised classification methods are not available in HSI scenarios.…”

Section: Introductionmentioning

confidence: 99%

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Robust Spectral Clustering Incorporating Statistical Sub-Graph Affinity Model

Lin¹,

Wang²,

Wu³

2022

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Hyperspectral image (HSI) clustering is a challenging work due to its high complexity. Subspace clustering has been proven to successfully excavate the intrinsic relationships between data points, while traditional subspace clustering methods ignore the inherent structural information between data points. This study uses graph convolutional subspace clustering (GCSC) for robust HSI clustering. The model remaps the self-expression of the data to non-Euclidean domains, which can generate a robust graph embedding dictionary. The EKGCSC model can achieve a globally optimal closed-form solution by using a subspace clustering model with the Frobenius norm and a Gaussian kernel function, making it easier to implement, train, and apply. However, the presence of noise can have a noteworthy negative impact on the segmentation performance. To diminish the impact of image noise, the concept of sub-graph affinity is introduced, where each node in the primary graph is modeled as a sub-graph describing the neighborhood around the node. A statistical sub-graph affinity matrix is then constructed based on the statistical relationships between sub-graphs of connected nodes in the primary graph, thus counteracting the uncertainty image noise by using more information. The model used in this work was named statistical sub-graph affinity kernel graph convolutional subspace clustering (SSAKGCSC). Experiment results on Salinas, Indian Pines, Pavia Center, and Pavia University data sets showed that the SSAKGCSC model can achieve improved segmentation performance and better noise resistance ability.

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“…Solving the above equation leads to a dense matrix of self-representing coefficients and a valid closed-form solution. We can rewrite (7) as:…”

Section: Methodology Ssakgcscmentioning

confidence: 99%