Remote sensing image analysis is a key scientific and engineering task that can be used in many fields such as monitoring changes in the Earth's surface, resource management, and natural disaster monitoring. In recent years, the rise of deep learning technology has provided new tools and methods for remote sensing image analysis, which can more accurately extract valuable information from large amounts of remote sensing data. This blog will delve into how to use R language and deep learning for remote sensing image analysis and provide specific code examples.
Part One: Data Acquisition and Understanding
Before conducting remote sensing image analysis, it is first necessary to obtain appropriate remote sensing image data. These data usually come from satellites, aircraft or other sensors, including images in different wavebands such as visible light, infrared, radar and so on. In this blog, we will use a publicly available satellite image dataset as an example.
First, load the required R language libraries and download the sample data set.
# 加载所需的库
library(reticulate)
library(keras)
library(imager)
library(tidyr)
# 使用Python下载示例数据集（可替换为自己的数据集）
py_run_string("import urllib.request
urllib.request.urlretrieve('https://example.com/satellite_images.zip', 'satellite_images.zip')")
# 解压数据集
py_run_string("import zipfile
with zipfile.ZipFile('satellite_images.zip', 'r') as zip_ref:
zip_ref.extractall('satellite_images')")
Next, we will read and understand remote sensing image data.
# 读取示例卫星图像
image_path <- "satellit