Abstract
Landslides are a type of frequent and widespread natural disaster. It is of great significance to extract location information from the landslide in time. At present, most articles still select single band or RGB bands as the feature for landslide recognition. To improve the efficiency of landslide recognition, this study proposed a remote sensing recognition method based on the convolutional neural network of the mixed spectral characteristics. Firstly, this paper tried to add NDVI (normalized difference vegetation index) and NIRS (near‐infrared spectroscopy) to enhance the features. Then, remote sensing images (predisaster and postdisaster images) with same spatial information but different time series information regarding landslide are taken directly from GF‐1 satellite as input images. By combining the 4 bands (red + green + blue + near‐infrared) of the prelandslide remote sensing images with the 4 bands of the postlandslide images and NDVI images, images with 9 bands were obtained, and the band values reflecting the changing characteristics of the landslide were determined. Finally, a deep learning convolutional neural network (CNN) was introduced to solve the problem. The proposed method was tested and verified with remote sensing data from the 2015 large‐scale landslide event in Shanxi, China, and 2016 large‐scale landslide event in Fujian, China. The results showed that the accuracy of the method was high. Compared with the traditional methods, the recognition efficiency was improved, proving the effectiveness and feasibility of the method.
Highlights
In recent decades, disaster detection has been one of the major research goals in the modern remote sensing field
Since more bands are introduced, this paper chose CNN, which has advantages in big data processing. e landslide recognition could be completed after the convolutional neural network learning process
Satellite Data and Network Structure. e experimental data used in this study were acquired from GF-1 satellite remote sensing images
Summary
Disaster detection has been one of the major research goals in the modern remote sensing field. Researchers have studied the effects of changes occurring due to disasters using sensors [1] and simple image processing techniques [2]. In the early stage of disaster detection at home and abroad, traditional machine learning methods have been mainly used to solve these problems. Ningning et al [6] aimed at the shortcomings of support vector machines and introduced a fuzzy support vector machine to identify landslides in remote sensing images. Traditional machine learning methods are relatively mature in the disaster detection field, but both SVM and MLC are shallow learning algorithms because the cell is finite. With an increase in sample size and sample diversity, shallow models gradually fail to catch up with complex samples
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