Learning Deconvolution Network for Semantic Segmentation

Deep learning is transforming most areas of science and technology, including electron microscopy. This review paper offers a practical perspective aimed at developers with limited familiarity. For context, we review popular applications of deep learning in electron microscopy. Following, we discuss hardware and software needed to get started with deep learning and interface with electron microscopes. We then review neural network components, popular architectures, and their optimization. Finally, we discuss future directions of deep learning in electron microscopy.

Review: Deep Learning in Electron Microscopy

Image Segmentation Techniques: Statistical, Comprehensive, Semi-Automated Analysis and an Application Perspective Analysis of Mathematical Expressions

As for the classification network that is constantly emerging with each passing day, different classification network as the backbone of the semantic segmentation network may show different performance. This paper selected the road extraction data set of CVPR DeepGlobe, and compared the performance differences of VGG-16 as the backbone of Unet, ResNet34, ResNet101 and Xception as the backbone of AD-LinkNet. When VGG-16 is used as the backbone of the semantic segmentation network, it performs better in the face of long and wide road extraction. As the backbone of the semantic segmentation network, ResNet has a higher ability to extract small roads. When Xception is used as the backbone of the semantic segmentation network, it not only retains the characteristics of ResNet34, but also can effectively deal with the complex situation of extracting target covered by occlusions.

Comparison of Backbones for Semantic Segmentation Network

Distributed photovoltaic power stations are an effective way to develop and utilize solar energy resources. Using high-resolution remote sensing images to obtain the locations, distribution, and areas of distributed photovoltaic power stations over a large region is important to energy companies, government departments, and investors. In this paper, a deep convolutional neural network was used to extract distributed photovoltaic power stations from high-resolution remote sensing images automatically, accurately, and efficiently. Based on a semantic segmentation model with an encoder-decoder structure, a gated fusion module was introduced to address the problem that small photovoltaic panels are difficult to identify. Further, to solve the problems of blurred edges in the segmentation results and that adjacent photovoltaic panels can easily be adhered, this work combines an edge detection network and a semantic segmentation network for multi-task learning to extract the boundaries of photovoltaic panels in a refined manner. Comparative experiments conducted on the Duke California Solar Array data set and a self-constructed Shanghai Distributed Photovoltaic Power Station data set show that, compared with SegNet, LinkNet, UNet, and FPN, the proposed method obtained the highest identification accuracy on both data sets, and its F1-scores reached 84.79% and 94.03%, respectively. These results indicate that effectively combining multi-layer features with a gated fusion module and introducing an edge detection network to refine the segmentation improves the accuracy of distributed photovoltaic power station identification.

Combined Multi-Layer Feature Fusion and Edge Detection Method for Distributed Photovoltaic Power Station Identification

Satellite image semantic segmentation, including extracting road, detecting building, and identifying land cover types, is essential for sustainable development, agriculture, forestry, urban planning, and climate change research. Nevertheless, it is still unclear how to develop a refined semantic segmentation model in an efficient and elegant way. In this paper, we propose attention dilation-LinkNet (AD-LinkNet) neural network that adopts encoder–decoder structure, serial–parallel combination dilated convolution, channel-wise attention mechanism, and pretrained encoder for semantic segmentation. Serial–parallel combination dilated convolution enlarges receptive field as well as assemble multi-scale features for multi-scale objects, such as long-span road and small pool. The channel-wise attention mechanism is designed to advantage the context information in the satellite image. The experimental results on road extraction and surface classification data sets prove that the AD-LinkNet shows a significant effect on improving the segmentation accuracy. We defeated the D-Linknet algorithm that won the first place in the CVPR 2018 DeepGlobe road extraction competition.

/pdf/towards-accurate-high-resolution-satellite-image-semantic-4gnn1w7ciy.pdf

Towards Accurate High Resolution Satellite Image Semantic Segmentation

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Towards Accurate High Resolution Satellite Image Semantic Segmentation