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A Comparative Study of 1D-Convolutional Neural Networks with Modified Possibilistic c-Mean Algorithm for Mapping Transplanted Paddy Fields Using Temporal Data.
- Source :
- Journal of the Indian Society of Remote Sensing; Feb2022, Vol. 50 Issue 2, p227-238, 12p
- Publication Year :
- 2022
-
Abstract
- With increasing availability of satellite data of high temporal resolution, a more robust classifier is needed which can exploit the temporal information along with the spectral information of the remote sensing images. Specific fuzzy-based and learning-based algorithms are two broad categories and have the potential to perform well in spectral–temporal domain. In the present study, for mapping paddy fields as a specific class two classification algorithms, viz. fuzzy-based modified possibilistic c-mean (MPCM) algorithm and learning-based 1D-convolutional neural networks (CNN), were tested using Sentinel-2A/2B temporal data. The overall accuracy for learning-based 1D-CNN and fuzzy-based MPCM classifiers was found to be 96% and 93%, respectively. The F-measure values were found to be 0.95 and 0.92 for 1D-CNN- and MPCM-based classifier, respectively. Thus, it can be inferred from this study that the 1D-CNN classifier performed better than the traditional fuzzy-based classifier and can handle heterogeneity within class. [ABSTRACT FROM AUTHOR]
Details
- Language :
- English
- ISSN :
- 0255660X
- Volume :
- 50
- Issue :
- 2
- Database :
- Complementary Index
- Journal :
- Journal of the Indian Society of Remote Sensing
- Publication Type :
- Academic Journal
- Accession number :
- 155911424
- Full Text :
- https://doi.org/10.1007/s12524-020-01303-4