Ιδρυματικό Αποθετήριο
Πολυτεχνείο Κρήτης
Πολυτεχνείο Κρήτης
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| URI | http://purl.tuc.gr/dl/dias/D3CB96C9-3B7C-4CD3-B1E9-AB576FA31E05 | - |
| Αναγνωριστικό | https://ieeexplore.ieee.org/document/7410025/ | - |
| Αναγνωριστικό | https://doi.org/10.1109/TGRS.2016.2520203 | - |
| Γλώσσα | en | - |
| Μέγεθος | 16 pages | en |
| Τίτλος | Land classification using remotely sensed data: going multilabel | en |
| Δημιουργός | Karalas Konstantinos | en |
| Δημιουργός | Καραλας Κωνσταντινος | el |
| Δημιουργός | Tsagkatakis, Grigorios | en |
| Δημιουργός | Zervakis Michail | en |
| Δημιουργός | Ζερβακης Μιχαηλ | el |
| Δημιουργός | Tsakalides, Panagiotis | en |
| Εκδότης | Institute of Electrical and Electronics Engineers | en |
| Περίληψη | Obtaining an up-to-date high-resolution description of land cover is a challenging task due to the high cost and labor-intensive process of human annotation through field studies. This work introduces a radically novel approach for achieving this goal by exploiting the proliferation of remote sensing satellite imagery, allowing for the up-to-date generation of global-scale land cover maps. We propose the application of multilabel classification, a powerful framework in machine learning, for inferring the complex relationships between the acquired satellite images and the spectral profiles of different types of surface materials. Introducing a drastically different approach compared to unsupervised spectral unmixing, we employ contemporary ground-collected data from the European Environment Agency to generate the label set and multispectral images from the MODIS sensor to generate the spectral features, under a supervised classification framework. To validate the merits of our approach, we present results using several state-of-the-art multilabel learning classifiers and evaluate their predictive performance with respect to the number of annotated training examples, as well as their capability to exploit examples from neighboring regions or different time instances. We also demonstrate the application of our method on hyperspectral data from the Hyperion sensor for the urban land cover estimation of New York City. Experimental results suggest that the proposed framework can achieve excellent prediction accuracy, even from a limited number of diverse training examples, surpassing state-of-the-art spectral unmixing methods. | en |
| Τύπος | Peer-Reviewed Journal Publication | en |
| Τύπος | Δημοσίευση σε Περιοδικό με Κριτές | el |
| Άδεια Χρήσης | http://creativecommons.org/licenses/by/4.0/ | en |
| Ημερομηνία | 2018-10-12 | - |
| Ημερομηνία Δημοσίευσης | 2016 | - |
| Θεματική Κατηγορία | CORINE | en |
| Θεματική Κατηγορία | Data processing | en |
| Θεματική Κατηγορία | Land cover | en |
| Θεματική Κατηγορία | MODIS | en |
| Θεματική Κατηγορία | Pattern classification | en |
| Θεματική Κατηγορία | Remote sensing | en |
| Θεματική Κατηγορία | Satellite applications | en |
| Θεματική Κατηγορία | Time series | en |
| Θεματική Κατηγορία | Unmixing | en |
| Βιβλιογραφική Αναφορά | K. Karalas, G. Tsagkatakis, M. Zervakis and P. Tsakalides, "Land classification using remotely sensed data: going multilabel," IEEE Trans. Geosci. Remote Sens., vol. 54, no. 6, pp. 3548-3563, Jun. 2016. doi: 10.1109/TGRS.2016.2520203 | en |
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