Please use this identifier to cite or link to this item: https://dspace.ctu.edu.vn/jspui/handle/123456789/109141
Title: APPLY MACHINE LEARNING TO LAND USE TRANSITION INVENTORY USING SENTINEL-1 AND SENTINEL-2
Other Titles: ỨNG DỤNG MÁY HỌC VÀO KIỂM KÊ CHUYỂN ĐỔI MỤC ĐÍCH SỬ DỤNG ĐẤT SỬ DỤNG DỮ LIỆU SENTINEL-1 VÀ SENTINEL-2
Authors: Trương, Minh Thái
Hồ, Minh Nhựt
Keywords: CÔNG NGHỆ THÔNG TIN - CHẤT LƯỢNG CAO
Issue Date: 2024
Publisher: Trường Đại Học Cần Thơ
Abstract: The Normalized Difference Vegetation Index (NDVI) is a key metric for assessing vegetation health, derived from the red and near-infrared bands of optical satellite imagery. However, clouds frequently obstruct optical observations, preventing the calculation of NDVI in cloud-covered areas. Radar data, such as the VV (vertical transmit, vertical receive) and VH (vertical transmit, horizontal receive) polarizations from Sentinel-1, can penetrate cloud cover, offering valuable surface information that can be used to estimate NDVI in these obstructed regions. This project addresses the challenge of estimating NDVI for cloud-covered pixels in satellite imagery using machine learning regression techniques. By combining radar backscatter data (VV, VH) from Sentinel-1 with optical NDVI from cloud-free Sentinel-2 imagery, various regression models: Multiple Linear Regression, Gradient Boosting, Random Forest Regressor, KNN Regressor are applied to predict NDVI for cloud-obscured areas. The project leverages satellite image data from the Open Data Cube (ODC) platform under the EASI CSIRO Asia project, taking advantage of multi-sensor data integration. To ensure accurate predictions, the VV and VH radar data are resampled and aligned with NDVI values calculated from cloud-free Sentinel-2 imagery, forming the training dataset for the machine learning models.
Description: 65 Tr
URI: https://dspace.ctu.edu.vn/jspui/handle/123456789/109141
Appears in Collections:Trường Công nghệ Thông tin & Truyền thông

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