Land Cover Mapping Using Sentinel-1 Time-Series Data and Machine-Learning Classifiers in Agricultural Sub-Saharan Landscape

被引:12
|
作者
Dahhani, Sara [1 ]
Raji, Mohamed [1 ]
Hakdaoui, Mustapha [1 ]
Lhissou, Rachid [2 ]
机构
[1] Hassan II Univ Casablanca, Fac Sci Ben Msik, POB 7955, Casablanca, Morocco
[2] Inst Natl Rech Sci, Ctr ETE, 490 Couronne, Quebec City, PQ GIK 9A9, Canada
关键词
SAR data; PCA; K-D tree KNN; time series; land use; random forest; synthetic aperture radar; CLASSIFICATION; FOREST;
D O I
10.3390/rs15010065
中图分类号
X [环境科学、安全科学];
学科分类号
08 ; 0830 ;
摘要
This paper shows the efficiency of machine learning for improving land use/cover classification from synthetic aperture radar (SAR) satellite imagery as a tool that can be used in some sub-Saharan countries that experience frequent clouds. Indeed, we aimed to map the land use and land cover, especially in agricultural areas, using SAR C-band Sentinel-1 (S-1) time-series data over our study area, located in the Kaffrine region of Senegal. We assessed the performance and the processing time of three machine-learning classifiers applied on two inputs. In fact, we applied the random forest (RF), K-D tree K-nearest neighbor (KDtKNN), and maximum likelihood (MLL) classifiers using two separate inputs, namely a set of monthly S-1 time-series data acquired during 2020 and the principal components (PCs) of the time-series dataset. In addition, the RF and KDtKNN classifiers were processed using different tree numbers for RF (10, 15, 50, and 100) and different neighbor numbers for KDtKNN (5, 10, and 15). The retrieved land cover classes included water, shrubs and scrubs, trees, bare soil, built-up areas, and cropland. The RF classification using the S-1 time-series data gave the best performance in terms of accuracy (overall accuracy = 0.84, kappa = 0.73) with 50 trees. However, the processing time was relatively slower compared to KDtKNN, which also gave a good accuracy (overall accuracy = 0.82, kappa = 0.68). Our results were compared to the FROM-GLC, ESRI, and ESA world cover maps and showed significant improvements in some land use and land cover classes.
引用
收藏
页数:17
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