Abstract
Abstract. To monitor and manage the changes in the land use and land cover, is vital the process of classification; machine learning offers the potential for effective and efficient classification of remotely sensed imagery. However, not many articles have explicitly dealt with the effects of image fusion on land-cover classification accuracy. Although some studies have compared thematic mapping accuracy produced using different classification algorithms, there are no currently many studies that utilize image fusion for assessing different machine learning algorithms for classification purposes. The main aim of this study is to compare different machine learning algorithm for pixel classification of imagery fused with sensors Sentinel-2A and PlanetScope. The method used for image fusion is a variational model, the high spectral resolution of Sentinel-2A imagery and the high spatial resolution of PlanetScope imagery was fused; the machine learning algorithms evaluated are six that have been widely used in the remote sensing community: DT (Decision Tree), Boosted DT, RF (Random Forest), SVM radial base (Support Vector Machine), ANN (Artificial Neural Networks), KNN (k-Nearest Neighbors), for the classification four spectral indices (NDVI, NDMI, NDBI, MSAVI) were included, derived of the image fusion. The results show that the highest accuracy was produced by SVM radial base (OA: 87.8%, Kappa: 87%) respect to the other methods, nevertheless the methods RF, Boosted DT and KNN shown to be very powerful methods for classification of the study area.
Highlights
The classification for land use and land cover (LULC) constitutes a key variable for the monitoring earth that in general have shown a close correlation with human activities and the physical environment, the fast development of human societies has intensified different types of activities that have resulted in a continuous and noticeable influence on LULC, (Petropoulos, Partsinevelos, & Mitraka, 2013).In the last years remote sensing scientists are increasingly adopting machine learning classification algorithms for LCLU mapping, (Shih, Stow, & Tsai, 2018)
The main aim of this paper is to compare different machine learning algorithm for pixel classification of imagery fused from sensors Sentinel-2A and PlanetScope
For the spatial evaluation each band of the fused image was compared with the band Planetscope (PS) image employed in the process, while for the spectral evaluation each band of the fused image was compared with each band of the Sentinel-2A (S2A) image used
Summary
In the last years remote sensing scientists are increasingly adopting machine learning classification algorithms for LCLU mapping, (Shih, Stow, & Tsai, 2018). Compared with products derived by traditional statistical classifiers (e.g. minimum distance, maximum likelihood, and parallelepiped classifiers), previous research results generally show that machine learning classifiers yield more accurate and reliable products, if abundant training data are available, (Schneider, 2012). Machine learning offers the potential for effective and efficient classification of remotely sensed imagery. The strengths of machine learning include the capacity to handle data of high dimensionality and to map classes with very complex characteristics, it can accept a variety of input predictor data, and do not make assumptions about the data distribution (i.e. are nonparametric), (Maxwell, Warner, & Fang, 2018). A multiplepoint spatially weighted k-NN classifier for remote sensing.
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