Material Classification in Multispectral Remote Sensing Image Using Multiple Convolutional Neural Network Architectures
| Autor: | Ting-You Wang, 王亭幼 |
|---|---|
| Rok vydání: | 2019 |
| Druh dokumentu: | 學位論文 ; thesis |
| Popis: | 107 In order to achieve the effect of real-world simulation, terrain models must combine various material and texture information, so that terrain reconstruction may play an important role in the three-dimensional numerical simulation of terrain. However, if the model is built in the traditional way, it will often cost a lot in terms of manpower and time. Therefore, this study uses a convolutional neural network (CNN) architecture to classify material in multispectral remote sensing images to simplify the construction of future models. The multispectral remote sensing image of this study includes RGB visible light, near infrared (NIR), normalized difference vegetation index (NDVI) and digital surface model (DSM) images. This paper proposes the use of the RUNet model of multiple convolutional neural network architectures for material classification. The RUNet model is based on an improved U-Net architecture combined with the Shortcut Connections approach of the ResNet model to preserve the features of shallow network extraction. The architecture is divided into an encoding layer and a decoding layer. The encoding layer includes 10 convolution layers and 4 pooling layers. The decoding layer has 4 upsampling layers, 8 convolution layers, and one classified convolution layer. The material classification process in this paper includes the training and testing of the RUNet model. Due to the large size of the remote sensing image, the training process randomly cuts sub-images of the same size from the training set and then inputs them into the RUNet model for training. In order to consider the spatial information of the material, the test process cuts multiple test sub-images from the test set by mirror padding and overlap cropping, the RUNet then classifies the sub-images, and finally merges the sub-image classification results back into the original test image. In order to evaluate the effectiveness of the method, the Inria, Inria-2 and ISPRS remote sensing image datasets were used, and the RUNet model was used for material classification experiments. The effects of the mirror padding and overlap cropping methods were also analyzed, as well as the impact of sub-image size on material classification. The results showed that in the Inria dataset experiment, after the morphological optimization of RUNet, the overall IoU reached about 70.82%, and the accuracy rate was about 95.66%, which was better than the results in other research methods. The classification results of the Inria-2 dataset experiment showed that the overall IoU was about 75.5% and the accuracy was about 95.71% after optimization. Although the improved FCN has better results, the RUNet model takes less training time. In the ISPRS dataset experiment, the overall accuracy of combining multispectral, NDVI and DSM images reached approximately 89.71%, which is superior to the classification results using RGB images. NIR and DSM can provide more material features information, effectively improving the classification confusion caused by the same color, shape or texture features in RGB images. Experiments have prove that the material classification of our method in remote sensing images achieves better results than other research methods do, and it is expected to be applied to the model construction of the simulation system, land use monitoring, and disaster assessment in the future. |
| Databáze: | Networked Digital Library of Theses & Dissertations |
| Externí odkaz: |
|