Abstract:
Remote sensing data is an important way to reflect the comprehensive information of surface. In this paper, based on the semantic segmentation of high-resolution remote s...Show MoreMetadata
Abstract:
Remote sensing data is an important way to reflect the comprehensive information of surface. In this paper, based on the semantic segmentation of high-resolution remote sensing images, a segmentation method based on full convolutional neural network (FCN) is proposed. The method improves the traditional convolutional neural network (CNN) and replaces the final fully connected layer of the CNN network with a convolutional layer. And then optimize the convolution operation by using the matrix expansion technique. The experimental results show that the FCN network with sufficient training and fine-tuning can effectively perform automatic semantic segmentation of high-resolution remote sensing images. The correct segmentation accuracy is higher than 85%, which improves the efficiency of convolution operations.
Date of Conference: 03-06 December 2018
Date Added to IEEE Xplore: 07 February 2019
ISBN Information:
Keywords assist with retrieval of results and provide a means to discovering other relevant content. Learn more.
- IEEE Keywords
- Index Terms
- Neural Network ,
- Convolutional Network ,
- Convolutional Neural Network ,
- Semantic Segmentation ,
- Convolutional Layers ,
- Convolution Operation ,
- Segmentation Accuracy ,
- Automatic Segmentation ,
- Traditional Convolutional Neural Network ,
- Expansion Technique ,
- Deep Learning ,
- Image Resolution ,
- Input Image ,
- Feature Maps ,
- Image Size ,
- Average Accuracy ,
- Pooling Layer ,
- Matrix Multiplication ,
- Convolutional Neural Network Model ,
- Convolution Kernel ,
- Data Layers ,
- Segmentation Results ,
- Method In This Paper ,
- Spatial Coordinates ,
- Experimental Environment
- Author Keywords
Keywords assist with retrieval of results and provide a means to discovering other relevant content. Learn more.
- IEEE Keywords
- Index Terms
- Neural Network ,
- Convolutional Network ,
- Convolutional Neural Network ,
- Semantic Segmentation ,
- Convolutional Layers ,
- Convolution Operation ,
- Segmentation Accuracy ,
- Automatic Segmentation ,
- Traditional Convolutional Neural Network ,
- Expansion Technique ,
- Deep Learning ,
- Image Resolution ,
- Input Image ,
- Feature Maps ,
- Image Size ,
- Average Accuracy ,
- Pooling Layer ,
- Matrix Multiplication ,
- Convolutional Neural Network Model ,
- Convolution Kernel ,
- Data Layers ,
- Segmentation Results ,
- Method In This Paper ,
- Spatial Coordinates ,
- Experimental Environment
- Author Keywords