Extraction of Hani terraces based on Sentinel-2 and GF-2 images in Honghe prefecture, Yunnan province

Abstract

Monitoring Hani terraces quickly and accurately using remote sensing technology is crucial for the protecting World Cultural Heritage Sites. However, single remote sensing image is affected by the mutual constraints of temporal and spatial resolution, making it difficult to concurrently integrate key phenological and spatial information for accurate extraction. In this study, GF-2 and Sentinel-2 images are used to extract terraces based on objected-based image analysis. Firstly, the spatial and phenological features of objects were obtained by multi-resolution segmentation using GF-2 image. Secondly, the optimal spatial and key phenological features were optimized by recursive feature elimination cross-validation and separation index, respectively. Finally, all optimized features were adopted to extract terraces using random forest (RF) and support vector machine (SVM) classifiers, respectively. Comparing deep learning and traditional machine learning methods, the proposed method using RF achieved the highest accuracy with Kappa coefficient and overall accuracy of 89.45 and 94.73%, respectively.

Keywords

• Terrace extraction
• objected-based image analysis
• multi-resolution segmentation
• recursive feature elimination cross-validation
• separation index
• deep learning