Airborne LiDAR-assisted deep learning methodology for riparian land cover classification using aerial photographs and its application for flood modelling

Abstract

In response to challenges in land cover classification (LCC), many researchers have experimented recently with classification methods based on artificial intelligence techniques. For LCC mapping of the vegetated Asahi River in Japan, the current study uses deep learning (DL)-based DeepLabV3+ module for image segmentation of aerial photographs. We modified the existing model by concatenating data on its resultant output port to access the airborne laser bathymetry (ALB) dataset, including voxel-based laser points and vegetation height (i.e. digital surface model data minus digital terrain model data). Findings revealed that the modified approach improved the accuracy of LCC greatly compared to our earlier unsupervised ALB-based method, with 25 and 35% improvement, respectively, in overall accuracy and the macro F1-score for November 2017 dataset (no–leaf condition). Finally, by estimating flow-resistance parameters in flood modelling using LCC mapping-derived data, we conclude that the upgraded DL methodology produces better fit between numerically analyzed and observed peak water levels.

A deep inverse convolutional neural network-based semantic classification method for land cover remote sensing images

“…11 , it can be seen that the proposed method obtained a PRI index close to 1 during segmentation performance testing, indicating good segmentation performance. The PRI index of the methods in reference 15 and reference 16 are both below 0.9. Through comparison, it can be seen that the proposed method has significantly better test results than these two methods.…”

“…According to experimental analysis, the proposed method exhibits lower average time consumption, and as the number of images increases, the time consumption of all methods also shows an increasing trend. Compared to the methods in references 15 , 16 , the proposed method has higher computational efficiency and performs well in semantic classification tasks of large-scale image data. Therefore, the proposed method can quickly and effectively process land cover remote sensing images, and has broad application prospects.…”

“…The experiment tested the image segmentation performance of different methods and calculated their respective PRI values. The segmentation performance of the proposed method was compared with the methods in references 15 , 16 . The test results of the image segmentation performance of different methods are shown in Fig.…”

“…A set of remote sensing images of land cover with different resolutions and sizes was selected as the input dataset, including different types of landforms such as urban areas, farmland, and forests. In the experiment, the proposed method, methods from references 15 , 16 were used for testing, and the experimental results are shown in Table 3 .…”

“…Further research is needed in the semantic classification and recognition of multiple objects in the image area. Reference 15 Yoshida et al used a deep learning based method to classify land cover in aerial photography images of the Xuchuan River, improving classification accuracy by connecting data related to the model output. Use the modified deep learning model to segment aerial photography images and classify different types of land cover.…”