详情

原文传递 Semantic Segmentation for Aerial Imagery Using U-Nets.

题名：	Semantic Segmentation for Aerial Imagery Using U-Nets.
作者：	Yi, T. J.
关键词：	Image segmentation, Artificial neural networks, Navigation, Global positioning systems, Semantic segmentation, U-nets, Convolution neural networks, Image-aided navigation, Localization
摘要：	In situations where global positioning systems are unavailable, alternative methods of localization must be implemented. A potential step to achieving this is semantic segmentation, or the ability for a model to output class labels by pixel. This research aims to utilize datasets of varying spatial resolutions and locations to train a fully convolutional neural network architecture called the U-Net to perform segmentations of aerial images. Variations of the U-Net architecture are implemented and compared to other existing models in order to determine the best in detecting buildings and roads. A final dataset will also be created combining two datasets to determine the ability of the U-Net to segment classes regardless of location. The final segmentation results will demonstrate the overall efficacy of semantic segmentation for different datasets for potential localization applications.
报告类型：	科技报告