Abstract
How to acquire landslide disaster information quickly and accurately has become the focus and difficulty of disaster prevention and relief by remote sensing. Landslide disasters are generally featured by sudden occurrence, proposing high demand for emergency data acquisition. The low-altitude Unmanned Aerial Vehicle (UAV) remote sensing technology is widely applied to acquire landslide disaster data, due to its convenience, high efficiency, and ability to fly at low altitude under cloud. However, the spectrum information of UAV images is generally deficient and manual interpretation is difficult for meeting the need of quick acquisition of emergency data. Based on this, UAV images of high-occurrence areas of landslide disaster in Wenchuan County and Baoxing County in Sichuan Province, China were selected for research in the paper. Firstly, the acquired UAV images were pre-processed to generate orthoimages. Subsequently, multi-resolution segmentation was carried out to obtain image objects, and the barycenter of each object was calculated to generate a landslide sample database (including positive and negative samples) for deep learning. Next, four landslide feature models of deep learning and transfer learning, namely Histograms of Oriented Gradients (HOG), Bag of Visual Word (BOVW), Convolutional Neural Network (CNN), and Transfer Learning (TL) were compared, and it was found that the TL model possesses the best feature extraction effect, so a landslide extraction method based on the TL model and object-oriented image analysis (TLOEL) was proposed; finally, the TLOEL method was compared with the object-oriented nearest neighbor classification (NNC) method. The research results show that the accuracy of the TLOEL method is higher than the NNC method, which can not only achieve the edge extraction of large landslides, but also detect and extract middle and small landslides accurately that are scatteredly distributed.
Highlights
Human beings are facing, and will continue to face, challenges that affect the harmony and sustainable development of the society for a long time, such as population, resources, and environment.Among all environmental problems, geological environment is one of the most prominent [1,2,3]
It is found that there is a lot of work to manually build a landslide sample library with positive and negative samples, so this paper studies how to realize automatic batch cutting of image blocks that are based on ArcGIS Python secondary development package (ArcPy)
Wide hazardous areas of landslides in mountainous areas in southwestern China were researched, a landslide sample database was set up, the optimal feature extraction model, namely the transfer learning model, is selected by comparison, and a high-resolution remote sensing image landslide extraction method is proposed by combining this model with the object-oriented image analysis method
Summary
Geological environment is one of the most prominent [1,2,3]. The geological environment is a necessary carrier and a basic environment for all human life and engineering activities. It is fragile and difficult, or even impossible to restore. Geological disasters very frequently occur in China and cause tremendous loss, especially in the western mountainous areas with complex topographic and geological conditions. Landslides in these areas are generally characterized by suddenness. Quick and automatic information extraction of sudden landslides has become a hot topic, a hot potato, of the day in the landslide research in the world [7,8]
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