scholarly journals Road Extraction from High-Resolution Remote Sensing Imagery Using Deep Learning

2018 ◽  
Vol 10 (9) ◽  
pp. 1461 ◽  
Author(s):  
Yongyang Xu ◽  
Zhong Xie ◽  
Yaxing Feng ◽  
Zhanlong Chen
Keyword(s):  
Neural Network ◽  
Remote Sensing ◽  
Deep Learning ◽  
High Resolution ◽  
Convolutional Neural Network ◽  
Road Network ◽  
Deep Convolutional Neural Network ◽  
Road Extraction ◽  
Remote Sensing Imagery ◽  
The Road

The road network plays an important role in the modern traffic system; as development occurs, the road structure changes frequently. Owing to the advancements in the field of high-resolution remote sensing, and the success of semantic segmentation success using deep learning in computer version, extracting the road network from high-resolution remote sensing imagery is becoming increasingly popular, and has become a new tool to update the geospatial database. Considering that the training dataset of the deep convolutional neural network will be clipped to a fixed size, which lead to the roads run through each sample, and that different kinds of road types have different widths, this work provides a segmentation model that was designed based on densely connected convolutional networks (DenseNet) and introduces the local and global attention units. The aim of this work is to propose a novel road extraction method that can efficiently extract the road network from remote sensing imagery with local and global information. A dataset from Google Earth was used to validate the method, and experiments showed that the proposed deep convolutional neural network can extract the road network accurately and effectively. This method also achieves a harmonic mean of precision and recall higher than other machine learning and deep learning methods.

scholarly journals A Deep Convolutional Neural Network to Detect the Existence of Geospatial Elements in High-Resolution Aerial Imagery

Proceedings ◽  
2019 ◽  
Vol 19 (1) ◽  
pp. 17 ◽  
Author(s):  
Calimanut-Ionut Cira ◽  
Ramón Alcarria ◽  
Miguel-Ángel Manso-Callejo ◽  
Francisco Serradilla
Keyword(s):  
Neural Network ◽  
Deep Learning ◽  
Object Recognition ◽  
High Resolution ◽  
Convolutional Neural Network ◽  
Road Network ◽  
Deep Convolutional Neural Network ◽  
Aerial Imagery ◽  
Learning Techniques

This paper tackles the problem of object recognition in high-resolution aerial imagery and addresses the application of Deep Learning techniques to solve a challenge related to detecting the existence of geospatial elements (road network) in the available cartographic support. This challenge is addressed by building a convolutional neural network (CNN) trained to detect roads in high resolution aerial orthophotos divided in tiles (256 × 256 pixels) using manually labelled data.

Edge-Reinforced Convolutional Neural Network for Road Detection in Very-High-Resolution Remote Sensing Imagery

2020 ◽  
Vol 86 (3) ◽  
pp. 153-160
Author(s):  
Xiaoyan Lu ◽  
Yanfei Zhong ◽  
Zhuo Zheng ◽  
Ji Zhao ◽  
Liangpei Zhang
Keyword(s):  
Neural Network ◽  
Remote Sensing ◽  
Feature Extraction ◽  
High Resolution ◽  
Convolutional Neural Network ◽  
Complex Data ◽  
Road Detection ◽  
Remote Sensing Imagery ◽  
The Road ◽  
Very High

Road detection in very-high-resolution remote sensing imagery is a hot research topic. However, the high resolution results in highly complex data distributions, which lead to much noise for road detection—for example, shadows and occlusions caused by disturbance on the roadside make it difficult to accurately recognize road. In this article, a novel edge-reinforced convolutional neural network, combined with multiscale feature extraction and edge reinforcement, is proposed to alleviate this problem. First, multiscale feature extraction is used in the center part of the proposed network to extract multiscale context information. Then edge reinforcement, applying a simplified U-Net to learn additional edge information, is used to restore the road information. The two operations can be used with different convolutional neural networks. Finally, two public road data sets are adopted to verify the effectiveness of the proposed approach, with experimental results demonstrating its superiority.

scholarly journals A Framework Based on Nesting of Convolutional Neural Networks to Classify Secondary Roads in High Resolution Aerial Orthoimages

2020 ◽  
Vol 12 (5) ◽  
pp. 765 ◽  
Author(s):  
Calimanut-Ionut Cira ◽  
Ramon Alcarria ◽  
Miguel-Ángel Manso-Callejo ◽  
Francisco Serradilla
Keyword(s):  
Remote Sensing ◽  
Neural Networks ◽  
Deep Learning ◽  
High Resolution ◽  
Learning Strategies ◽  
Convolutional Neural Networks ◽  
Test Data ◽  
Road Network ◽  
Ideal Solution ◽  
The Road

Remote sensing imagery combined with deep learning strategies is often regarded as an ideal solution for interpreting scenes and monitoring infrastructures with remarkable performance levels. In addition, the road network plays an important part in transportation, and currently one of the main related challenges is detecting and monitoring the occurring changes in order to update the existent cartography. This task is challenging due to the nature of the object (continuous and often with no clearly defined borders) and the nature of remotely sensed images (noise, obstructions). In this paper, we propose a novel framework based on convolutional neural networks (CNNs) to classify secondary roads in high-resolution aerial orthoimages divided in tiles of 256 × 256 pixels. We will evaluate the framework’s performance on unseen test data and compare the results with those obtained by other popular CNNs trained from scratch.

Unsupervised Representation High-Resolution Remote Sensing Image Scene Classification via Contrastive Learning Convolutional Neural Network

2021 ◽  
Vol 87 (8) ◽  
pp. 577-591
Author(s):  
Fengpeng Li ◽  
Jiabao Li ◽  
Wei Han ◽  
Ruyi Feng ◽  
Lizhe Wang
Keyword(s):  
Neural Network ◽  
Remote Sensing ◽  
Deep Learning ◽  
High Resolution ◽  
Convolutional Neural Network ◽  
State Of The Art ◽  
Remote Sensing Image ◽  
Scene Classification ◽  
Data Set ◽  
Unsupervised Deep Learning

Inspired by the outstanding achievement of deep learning, supervised deep learning representation methods for high-spatial-resolution remote sensing image scene classification obtained state-of-the-art performance. However, supervised deep learning representation methods need a considerable amount of labeled data to capture class-specific features, limiting the application of deep learning-based methods while there are a few labeled training samples. An unsupervised deep learning representation, high-resolution remote sensing image scene classification method is proposed in this work to address this issue. The proposed method, called contrastive learning, narrows the distance between positive views: color channels belonging to the same images widens the gaps between negative view pairs consisting of color channels from different images to obtain class-specific data representations of the input data without any supervised information. The classifier uses extracted features by the convolutional neural network (CNN)-based feature extractor with labeled information of training data to set space of each category and then, using linear regression, makes predictions in the testing procedure. Comparing with existing unsupervised deep learning representation high-resolution remote sensing image scene classification methods, contrastive learning CNN achieves state-of-the-art performance on three different scale benchmark data sets: small scale RSSCN7 data set, midscale aerial image data set, and large-scale NWPU-RESISC45 data set.

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