Building segmentation is an important step in urban planning and development. In this work, we propose a new deep learning model, namely Multidimension Attention U-Net (MDAU-Net), to accurately segment building pixels and nonbuilding pixels in remote sensing images. Furthermore, we introduce a novel Multidimension Modified Efficient Channel Attention (MD-MECA) model to enhance the network discriminative ability through considering the interdependence between feature maps. Through deepening the U-Net model to a seven-story structure, the ability to identify the building is enhanced. We apply MD-MECA to the “skip connections” in traditional U-Net, instead of simply copying the feature mapping of the contraction path to the matching extension path, to optimize the feature transfer more efficiently. The obtained results show that our proposed MDAU-Net framework achieves the most advanced performance on publicly available building data sets (i.e. the precision over the Massachusetts buildings data set and WHU data set are 97.04% and 95.68%, respectively). Furthermore, we observed that the proposed framework outperforms several state-of-the-art approaches.
Aiming at the problem that the accuracy of traditional remote sensing image classification model is not ideal, a classification method based on improved attention mechanism and residual network is proposed. In order to prevent overfitting, we choose the network structure of ResNet18 as the framework. Meanwhile, ResNet18 has a small number of parameters and a fast calculation speed. Then, a parallel attention mechanism, ProCBAM, is proposed and added to the BasicBlock of the residual network. By optimizing the representation of the feature map from the spatial dimension and channel dimension of the feature map, more detailed image information is learned and image recognition errors are reduced. The experimental results on the open source dataset show that the accuracy is 93.86%, and a more accurate image classification model is successfully trained.
In recent ages, the use of deep learning approaches to extract ground object information from remote sensing high-resolution images has attracted extensive attention in many fields. Nevertheless, due to the high similarity of features between roads, prevailing deep learning semantic segmentation networks commonly demonstrate reduced continuity in road segmentation. Besides this, the role of advanced computing technologies including cloud and edge infrastructures has also become very important due to large number of images and their storage requirements. In order to better study the road details in images related to remote sensing, this paper suggests a road extraction technique which is basically founded on Dimensional U-Net (DU-Net) network. At the deepening level of the U-Net network, a parallel attention mechanism, known as ProCBAM, is added and implemented to the feature transmission step of the classical U-Net network. Moreover, we use and implement the edge-cloud architecture to develop and construct a unique remote sensing image service system that integrates several datacenters and their related edge infrastructure. In the proposed system, the edge network is primarily used for caching and distributing the processed remote sensing images, while the remote datacenter serves as the cloud platform and is responsible for the storage and processing of original remote sensing images. The results show that the proposed cloud enabled DU-Net model has achieved good performance in road segmentation. We observed that it can achieve improved road segmentation and resolve the issue of reduced continuity of road segmentation when compared with other state-of-the-art learning networks. Moreover, our empirical evaluations suggest that the proposed system not only distributes the workload of processing tasks across the edges but also achieves data efficiency among them, which enhances image processing efficiency and reduces data transmission costs.
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