Classifying vegetation communities karst wetland synergistic use of image fusion and object-based machine learning algorithm with Jilin-1 and UAV multispectral images

Abstract

Fine classification of wetland vegetation communities using machine learning algorithm and high spatial resolution images have attracted increased attention. However, there exist several challenges in image fusion, data dimension reduction and algorithm tuning. To resolve these issues, this paper attempts to fuse Unmanned Aerial Vehicle (UAV) images with spaceborne Jilin-1 (JL101K) multispectral images for classifying vegetation communities of karst wetland using the optimized Random Forest (RF), Extreme gradient boosting (XGBoost) and Light Gradient Boosting (LightGBM) algorithms. This study also quantitatively evaluates image fusion quality from spatial detail and spectral fidelity, and explores the effects of different image feature combinations and classifiers on mapping vegetation communities by variable selection and dimensionality reduction. Finally, this paper further evaluates and quantifies the importance and contribution rate of feature variables for typical vegetation communities using Recursive feature elimination (RFE) algorithm. The results showed that: (1) the Gram-Schmidt (GS)algorithm produced the high-quality fusion image of JL101K and UAV, and the fusion image achieved higher overall accuracy (82.8%) than the original JL101K multispectral image; (2) UAV multispectral image and its derivatives (scheme 3) achieved the highest overall accuracy (87.8%) in all classification schemes; (3) The optimized object-based LightGBM algorithm outperformed XGBoost and RF algorithm, which provided an improvement of 0.6%∼3.5% in overall accuracy (OA). McNemar's test indicated that there existed significant differences in vegetation communities’ classification between the three algorithms. (4) The average accuracy (AA) of vegetation communities in karst wetlands was mainly ranged from 60% to 90%. The water hyacinth and herbaceous vegetation were sensitive to the Mean Digital Surface Model (DSM) and Standard RedEdge band.