Road Segmentation on Remotely-Sensed Images Using Deep Convolutional Neural Networks with Landscape Metrics and Conditional Random Fields

In 2014, through the World-Class Tanker Safety System (WCTSS) initiative, the Government of Canada launched the Northern Marine Transportation Corridors (NMTC) concept. The corridors were created as a strategic framework to guide Federal investments in marine transportation in the Arctic. With new government investment, under the Oceans Protection Plan (OPP), the corridors initiative, known as the Northern Low-Impact Shipping Corridors, will continue to be developed. Since 2016, the Canadian Hydrographic Service (CHS) has been using the corridors as a key layer in a geographic information system (GIS) model known as the CHS Priority Planning Tool (CPPT). The CPPT helps CHS prioritize its survey and charting efforts in Canada’s key traffic areas. Even with these latest efforts, important gaps in the surveys still need to be filled in order to cover the Canadian waterways. To help further develop the safety to navigation and improve survey mission planning, CHS has also been exploring new technologies within remote sensing. Under the Government Related Initiatives Program (GRIP) of the Canadian Space Agency (CSA), CHS has been investigating the potential use of Earth observation (EO) data to identify potential hazards to navigation that are not currently charted on CHS products. Through visual interpretation of satellite imagery, and automatic detection using artificial intelligence (AI), CHS identified several potential hazards to navigation that had previously gone uncharted. As a result, five notices to mariners (NTMs) were issued and the corresponding updates were applied to the charts. In this study, two AI approaches are explored using deep learning and machine learning techniques: the convolution neural network (CNN) and random forest (RF) classification. The study investigates the effectiveness of the two models in identifying shoals in Sentinel-2 and WorldView-2 satellite imagery. The results show that both CNN and RF models can detect shoals with accuracies ranging between 79 and 94% over two study sites; however, WorldView-2 images deliver results with higher accuracy and lower omission errors. The high processing times of using high-resolution imagery and training a deep learning model may not be necessary in order to quickly scan images for shoals; but training a CNN model with a large training set may lead to faster processing times without the need to train individual images.

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HA-RoadFormer: Hybrid Attention Transformer with Multi-Branch for Large-Scale High-Resolution Dense Road Segmentation

Zhang

Miao

Liu

et al. 2022

Mathematics

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Road segmentation is one of the essential tasks in remote sensing. Large-scale high-resolution remote sensing images originally have larger pixel sizes than natural images, while the existing models based on Transformer have the high computational cost of square complexity, leading to more extended model training and inference time. Inspired by the long text Transformer model, this paper proposes a novel hybrid attention mechanism to improve the inference speed of the model. By calculating several diagonals and random blocks of the attention matrix, hybrid attention achieves linear time complexity in the token sequence. Using the superposition of adjacent and random attention, hybrid attention introduces the inductive bias similar to convolutional neural networks (CNNs) and retains the ability to acquire long-distance dependence. In addition, the dense road segmentation result of remote sensing image still has the problem of insufficient continuity. However, multiscale feature representation is an effective means in the network based on CNNs. Inspired by this, we propose a multi-scale patch embedding module, which divides images by patches with different scales to obtain coarse-to-fine feature representations. Experiments on the Massachusetts dataset show that the proposed HA-RoadFormer could effectively preserve the integrity of the road segmentation results, achieving a higher Intersection over Union (IoU) 67.36% of road segmentation compared to other state-of-the-art (SOTA) methods. At the same time, the inference speed has also been greatly improved compared with other Transformer based models.

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Road Segmentation on Remotely-Sensed Images Using Deep Convolutional Neural Networks with Landscape Metrics and Conditional Random Fields

Cited by 9 publications

References 7 publications

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Integration of Deep Learning Algorithms and Bilateral Filters with the Purpose of Building Extraction from Mono Optical Aerial Imagery

Earth Observation and Artificial Intelligence for Improving Safety to Navigation in Canada Low-Impact Shipping Corridors

HA-RoadFormer: Hybrid Attention Transformer with Multi-Branch for Large-Scale High-Resolution Dense Road Segmentation

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