Dépôt numérique

A computationally efficient multi-domain active learning method for crop mapping using satellite image time-series.

Niazmardi, Saeid; Homayouni, Saeid; Safari, Abdolreza (2019). A computationally efficient multi-domain active learning method for crop mapping using satellite image time-series. International Journal of Remote Sensing , vol. 40 , nº 16. p. 6383-6394. DOI: 10.1080/01431161.2019.1591648.

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Crop mapping through classification of Satellite Image Time-Series (SITS) data can provide precious information for several agricultural applications, such as crop monitoring, yield forecasting, and crop inventory. However, several issues affect the classification performance of SITS data. As one of the most challenging problems, constituent images of time-series provide different levels of information about crops. These differences are the result of dynamic spectral responses of crops and also the variable atmospheric and sensor conditions. The second issue is the unavailability of adequate high-quality samples for training the classifier. In this study, we proposed a novel computationally efficient Multi-Domain Active Learning (MDAL) method which takes advantage of Multiple Kernel Learning (MKL) and Active Learning (AL) algorithms to address these two issues. The proposed method uses MKL algorithms to address the issues associated with different information level of the data, which generally cannot be modelled using the well-known classification algorithms. AL algorithms were also used for semi-automatic selection of training samples. However, most of the MKL algorithms are very computationally demanding. Consequently, using them in the MDAL method can dramatically increase the computational costs. Thus, in this paper, we presented the similarity-based MKL algorithms. Thanks to their low computational complexities, these algorithms are the most suitable MKL algorithms that can be used in the MDAL method. We evaluated the proposed method using two multispectral SITS datasets, acquired by RapidEye and SPOT sensors. The obtained results of the MDAL method for these datasets respectively showed 8.2% and 5.87% increase in the overall accuracy of classification as compared to the accuracy of the standard AL algorithm. The results also showed that in the case of adopting the SimpleMKL algorithm (a common MKL algorithm in the literature) the computational time of the MDAL method is 577 and 474 seconds for RapidEye and SPOT datasets, respectively. However, in the case of adopting the similarity-based MKL algorithms, these computational times respectively decreases to 4 and 2 seconds.

Type de document: Article
Mots-clés libres: artificial intelligence; classification (of information); computational efficiency; crops; mapping; time series
Centre: Centre Eau Terre Environnement
Date de dépôt: 04 déc. 2019 15:12
Dernière modification: 04 déc. 2019 15:12
URI: http://espace.inrs.ca/id/eprint/9616

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