Semi-supervised Learning with Graphs: Covariance Based Superpixels For Hyperspectral Image Classification
| العنوان: | Semi-supervised Learning with Graphs: Covariance Based Superpixels For Hyperspectral Image Classification |
|---|---|
| المؤلفون: | David A. Coomes, Carola-Bibiane Schönlieb, Nicolas Papadakis, Angelica I. Aviles-Rivero, A. C. Faul, Philip Sellars |
| المساهمون: | Department of Applied Mathematics and Theoretical Physics (DAMTP), University of Cambridge [UK] (CAM), Institut de Mathématiques de Bordeaux (IMB), Université Bordeaux Segalen - Bordeaux 2-Université Sciences et Technologies - Bordeaux 1 (UB)-Université de Bordeaux (UB)-Institut Polytechnique de Bordeaux (Bordeaux INP)-Centre National de la Recherche Scientifique (CNRS), Plant Sciences, University of Cambridge, European Project: 777826,NoMADS(2018), Université Bordeaux Segalen - Bordeaux 2-Université Sciences et Technologies - Bordeaux 1-Université de Bordeaux (UB)-Institut Polytechnique de Bordeaux (Bordeaux INP)-Centre National de la Recherche Scientifique (CNRS) |
| المصدر: | IEEE Geoscience and Remote Sensing Symposium (IGARSS'19) IEEE Geoscience and Remote Sensing Symposium (IGARSS'19), Jul 2019, Yokohama, Japan. pp.592-595 IGARSS IEEE Geoscience and Remote Sensing Symposium IEEE Geoscience and Remote Sensing Symposium, Jul 2019, Yokohama, Japan |
| بيانات النشر: | HAL CCSD, 2019. |
| سنة النشر: | 2019 |
| مصطلحات موضوعية: | Computer Science - Machine Learning, Computer science, Feature vector, Semi-Supervised Learning, Superpixels, Computer Science - Computer Vision and Pattern Recognition, 0211 other engineering and technologies, 02 engineering and technology, Semi-supervised learning, [INFO.INFO-TS]Computer Science [cs]/Signal and Image Processing, Statistics - Machine Learning, 0202 electrical engineering, electronic engineering, information engineering, 021101 geological & geomatics engineering, Index Terms-Hyperspectral Imaging, Pixel, Covariance, business.industry, Covariance matrix, Hyperspectral imaging, Pattern recognition, Hyperspectral Imaging, Classification, Graph, ComputingMethodologies_PATTERNRECOGNITION, Computer Science::Computer Vision and Pattern Recognition, 020201 artificial intelligence & image processing, Artificial intelligence, business, Graphs |
| الوصف: | In this paper, we present a graph-based semi-supervised framework for hyperspectral image classification. We first introduce a novel superpixel algorithm based on the spectral covariance matrix representation of pixels to provide a better representation of our data. We then construct a superpixel graph, based on carefully considered feature vectors, before performing classification. We demonstrate, through a set of experimental results using two benchmarking datasets, that our approach outperforms three state-of-the-art classification frameworks, especially when an extremely small amount of labelled data is used. Comment: Four pages with two figures |
| اللغة: | English |
| URL الوصول: | https://explore.openaire.eu/search/publication?articleId=doi_dedup___::b3a2d203d3c15f7f224d423d200927cc https://hal.science/hal-02057730/document |
| Rights: | OPEN |
| رقم الانضمام: | edsair.doi.dedup.....b3a2d203d3c15f7f224d423d200927cc |
| قاعدة البيانات: | OpenAIRE |
| الوصف غير متاح. |