scholarly journals BUILDING CLASSIFICATION OF VHR AIRBORNE STEREO IMAGES USING FULLY CONVOLUTIONAL NETWORKS AND FREE TRAINING SAMPLES

2018 ◽  
Vol XLII-4 ◽  
pp. 87-92
Author(s):  
Y. Chen ◽  
W. Gao ◽  
E. Widyaningrum ◽  
M. Zheng ◽  
K. Zhou
Keyword(s):  
Large Scale ◽  
Semantic Segmentation ◽  
Fine Tuning ◽  
Stereo Images ◽  
Convolutional Networks ◽  
Fully Convolutional Networks ◽  
Area Of Interest ◽  
Learning Rates ◽  
Training Samples ◽  
Benchmark Datasets

<p><strong>Abstract.</strong> Semantic segmentation, especially for buildings, from the very high resolution (VHR) airborne images is an important task in urban mapping applications. Nowadays, the deep learning has significantly improved and applied in computer vision applications. Fully Convolutional Networks (FCN) is one of the tops voted method due to their good performance and high computational efficiency. However, the state-of-art results of deep nets depend on the training on large-scale benchmark datasets. Unfortunately, the benchmarks of VHR images are limited and have less generalization capability to another area of interest. As existing high precision base maps are easily available and objects are not changed dramatically in an urban area, the map information can be used to label images for training samples. Apart from object changes between maps and images due to time differences, the maps often cannot perfectly match with images. In this study, the main mislabeling sources are considered and addressed by utilizing stereo images, such as relief displacement, different representation between the base map and the image, and occlusion areas in the image. These free training samples are then fed to a pre-trained FCN. To find the better result, we applied fine-tuning with different learning rates and freezing different layers. We further improved the results by introducing atrous convolution. By using free training samples, we achieve a promising building classification with 85.6<span class="thinspace"></span>% overall accuracy and 83.77<span class="thinspace"></span>% F1 score, while the result from ISPRS benchmark by using manual labels has 92.02<span class="thinspace"></span>% overall accuracy and 84.06<span class="thinspace"></span>% F1 score, due to the building complexities in our study area.</p>

scholarly journals FULLY CONVOLUTIONAL NETWORKS FOR STREET FURNITURE IDENTIFICATION IN PANORAMA IMAGES

2019 ◽  
Vol XLII-2/W13 ◽  
pp. 13-20
Author(s):  
Y. Ao ◽  
J. Wang ◽  
M. Zhou ◽  
R. C. Lindenbergh ◽  
M. Y. Yang
Keyword(s):  
Point Cloud ◽  
Laser Scanning ◽  
Semantic Segmentation ◽  
Fine Tuning ◽  
Point Cloud Data ◽  
Cloud Data ◽  
Convolutional Networks ◽  
Fully Convolutional Networks ◽  
Panoramic Images ◽  
Street Furniture

<p><strong>Abstract.</strong> Panoramic images are widely used in many scenes, especially in virtual reality and street view capture. However, they are new for street furniture identification which is usually based on mobile laser scanning point cloud data or conventional 2D images. This study proposes to perform semantic segmentation on panoramic images and transformed images to separate light poles and traffic signs from background implemented by pre-trained Fully Convolutional Networks (FCN). FCN is the most important model for deep learning applied on semantic segmentation for its end to end training process and pixel-wise prediction. In this study, we use FCN-8s model that pre-trained on cityscape dataset and finetune it by our own data. The results show that in both pre-trained model and fine-tuning, transformed images have better prediction results than panoramic images.</p>

scholarly journals Detecting High-Rise Buildings from Sentinel-2 Data Based on Deep Learning Method

2021 ◽  
Vol 13 (20) ◽  
pp. 4073
Author(s):  
Liwei Li ◽  
Jinming Zhu ◽  
Gang Cheng ◽  
Bing Zhang
Keyword(s):  
Large Scale ◽  
3D Structure ◽  
Image Features ◽  
Convolutional Networks ◽  
High Rise ◽  
Fully Convolutional Networks ◽  
Four Seasons ◽  
Training Samples ◽  
Definition Of ◽  
Sentinel 2

High-rise buildings (HRBs) as a modern and visually distinctive land use play an important role in urbanization. Large-scale monitoring of HRBs is valuable in urban planning and environmental protection and so on. Due to the complex 3D structure and seasonal dynamic image features of HRBs, it is still challenging to monitor large-scale HRBs in a routine way. This paper extends our previous work on the use of the Fully Convolutional Networks (FCN) model to extract HRBs from Sentinel-2 data by studying the influence of seasonal and spatial factors on the performance of the FCN model. 16 Sentinel-2 subset images covering four diverse regions in four seasons were selected for training and validation. Our results indicate the performance of the FCN-based method at the extraction of HRBs from Sentinel-2 data fluctuates among seasons and regions. The seasonal change of accuracy is larger than that of the regional change. If an optimal season can be chosen to get a yearly best result, F1 score of detected HRBs can reach above 0.75 for all regions with most errors located on the boundary of HRBs. FCN model can be trained on seasonally and regionally combined samples to achieve similar or even better overall accuracy than that of the model trained on an optimal combination of season and region. Uncertainties exist on the boundary of detected results and may be relieved by revising the definition of HRBs in a more rigorous way. On the whole, the FCN based method can be largely effective at the extraction of HRBs from Sentinel-2 data in regions with a large diversity in culture, latitude, and landscape. Our results support the possibility to build a powerful FCN model on a larger size of training samples for operational monitoring HRBs at the regional level or even on a country scale.

scholarly journals Learn to Extract Building Outline from Misaligned Annotation through Nearest Feature Selector

2020 ◽  
Vol 12 (17) ◽  
pp. 2722
Author(s):  
Yuxuan Wang ◽  
Guangming Wu ◽  
Yimin Guo ◽  
Yifei Huang ◽  
Ryosuke Shibasaki
Keyword(s):  
Large Scale ◽  
Rapid Development ◽  
Semantic Segmentation ◽  
Loss Functions ◽  
Aerial Image ◽  
Building Extraction ◽  
Convolutional Networks ◽  
Fully Convolutional Networks ◽  
Feature Selector ◽  
Segmentation Task

For efficient building outline extraction, many algorithms, including unsupervised or supervised, have been proposed over the past decades. In recent years, due to the rapid development of the convolutional neural networks, especially fully convolutional networks, building extraction is treated as a semantic segmentation task that deals with the extremely biased positive pixels. The state-of-the-art methods, either through direct or indirect approaches, are mainly focused on better network design. The shifts and rotations, which are coarsely presented in manually created annotations, have long been ignored. Due to the limited number of positive samples, the misalignment will significantly reduce the correctness of pixel-to-pixel loss that might lead to a gradient explosion. To overcome this, we propose a nearest feature selector (NFS) to dynamically re-align the prediction and slightly misaligned annotations. The NFS can be seamlessly appended to existing loss functions and prevent misleading by the errors or misalignment of annotations. Experiments on a large scale aerial image dataset with centered buildings and corresponding building outlines indicate that the additional NFS brings higher performance when compared to existing naive loss functions. In the classic L1 loss, the addition of NFS gains increments of 8.8% of f1-score, 8.9% of kappa coefficient, and 9.8% of Jaccard index, respectively.

scholarly journals LARGE SCALE SEMANTIC SEGMENTATION OF VIRTUAL ENVIRONMENTS TO FACILITATE CORROSION MANAGEMENT

2021 ◽  
Vol XLIII-B2-2021 ◽  
pp. 465-470
Author(s):  
R. L. Garcia ◽  
P. N. Happ ◽  
R. Q. Feitosa
Keyword(s):  
Large Scale ◽  
Semantic Segmentation ◽  
Industrial Plant ◽  
Offshore Platforms ◽  
Prediction Errors ◽  
Convolutional Networks ◽  
Fully Convolutional Networks ◽  
Panoramic Images ◽  
Automatic Methods ◽  
2D Images

Abstract. This paper reports the results of a study that aims to develop semi-automatic methods for assessing the degree of corrosion in industrial plant. We evaluated two fully convolutional networks (U-Net and DeepLab v3 +) to segment corroded areas in panoramic images of offshore platforms. The experimental analysis was based on two datasets built for this study. The datasets comprise 9,112 2D images and 3,732 panoramic images. Both FCNs trained on 2D images were tested on 2D images and cubic projections of panoramic images. In addition to pointing out encouraging results, the experiments indicated that most prediction errors concentrated in corrosion defects with a small pixel area.

scholarly journals Deep Convolutional Neural Network for Large-Scale Date Palm Tree Mapping from UAV-Based Images

2021 ◽  
Vol 13 (14) ◽  
pp. 2787
Author(s):  
Mohamed Barakat A. Gibril ◽  
Helmi Zulhaidi Mohd Shafri ◽  
Abdallah Shanableh ◽  
Rami Al-Ruzouq ◽  
Aimrun Wayayok ◽  
...  
Keyword(s):  
Neural Network ◽  
Deep Learning ◽  
Convolutional Neural Network ◽  
Large Scale ◽  
Date Palm ◽  
Semantic Segmentation ◽  
Palm Tree ◽  
Convolutional Networks ◽  
Fully Convolutional Networks ◽  
Palm Trees

Large-scale mapping of date palm trees is vital for their consistent monitoring and sustainable management, considering their substantial commercial, environmental, and cultural value. This study presents an automatic approach for the large-scale mapping of date palm trees from very-high-spatial-resolution (VHSR) unmanned aerial vehicle (UAV) datasets, based on a deep learning approach. A U-Shape convolutional neural network (U-Net), based on a deep residual learning framework, was developed for the semantic segmentation of date palm trees. A comprehensive set of labeled data was established to enable the training and evaluation of the proposed segmentation model and increase its generalization capability. The performance of the proposed approach was compared with those of various state-of-the-art fully convolutional networks (FCNs) with different encoder architectures, including U-Net (based on VGG-16 backbone), pyramid scene parsing network, and two variants of DeepLab V3+. Experimental results showed that the proposed model outperformed other FCNs in the validation and testing datasets. The generalizability evaluation of the proposed approach on a comprehensive and complex testing dataset exhibited higher classification accuracy and showed that date palm trees could be automatically mapped from VHSR UAV images with an F-score, mean intersection over union, precision, and recall of 91%, 85%, 0.91, and 0.92, respectively. The proposed approach provides an efficient deep learning architecture for the automatic mapping of date palm trees from VHSR UAV-based images.

scholarly journals Automatic Deep Learning Semantic Segmentation of Ultrasound Thyroid Cineclips using Recurrent Fully Convolutional Networks

IEEE Access ◽  
2020 ◽  
pp. 1-1
Author(s):  
Jeremy M. Webb ◽  
Duane D. Meixner ◽  
Shaheeda A. Adusei ◽  
Eric C. Polley ◽  
Mostafa Fatemi ◽  
...  
Keyword(s):  
Deep Learning ◽  
Semantic Segmentation ◽  
Convolutional Networks ◽  
Fully Convolutional Networks

scholarly journals Domain Adaptation for Semantic Segmentation of Historical Panchromatic Orthomosaics in Central Africa

2021 ◽  
Vol 10 (8) ◽  
pp. 523
Author(s):  
Nicholus Mboga ◽  
Stefano D’Aronco ◽  
Tais Grippa ◽  
Charlotte Pelletier ◽  
Stefanos Georganos ◽  
...  
Keyword(s):  
Land Cover ◽  
Domain Adaptation ◽  
Central Africa ◽  
Semantic Segmentation ◽  
Target Domain ◽  
Convolutional Networks ◽  
Fully Convolutional Networks ◽  
The Cost ◽  
Performance Gains

Multitemporal environmental and urban studies are essential to guide policy making to ultimately improve human wellbeing in the Global South. Land-cover products derived from historical aerial orthomosaics acquired decades ago can provide important evidence to inform long-term studies. To reduce the manual labelling effort by human experts and to scale to large, meaningful regions, we investigate in this study how domain adaptation techniques and deep learning can help to efficiently map land cover in Central Africa. We propose and evaluate a methodology that is based on unsupervised adaptation to reduce the cost of generating reference data for several cities and across different dates. We present the first application of domain adaptation based on fully convolutional networks for semantic segmentation of a dataset of historical panchromatic orthomosaics for land-cover generation for two focus cities Goma-Gisenyi and Bukavu. Our experimental evaluation shows that the domain adaptation methods can reach an overall accuracy between 60% and 70% for different regions. If we add a small amount of labelled data from the target domain, too, further performance gains can be achieved.

scholarly journals Encoding Time Series as Multi-Scale Signed Recurrence Plots for Classification Using Fully Convolutional Networks

Sensors ◽  
2020 ◽  
Vol 20 (14) ◽  
pp. 3818
Author(s):  
Ye Zhang ◽  
Yi Hou ◽  
Shilin Zhou ◽  
Kewei Ouyang
Keyword(s):  
Time Series ◽  
State Of The Art ◽  
Recurrence Plot ◽  
The State ◽  
Recurrence Plots ◽  
Convolutional Networks ◽  
Multi Scale ◽  
Fully Convolutional Networks ◽  
Benchmark Datasets ◽  
Visualization Evaluation

Recent advances in time series classification (TSC) have exploited deep neural networks (DNN) to improve the performance. One promising approach encodes time series as recurrence plot (RP) images for the sake of leveraging the state-of-the-art DNN to achieve accuracy. Such an approach has been shown to achieve impressive results, raising the interest of the community in it. However, it remains unsolved how to handle not only the variability in the distinctive region scale and the length of sequences but also the tendency confusion problem. In this paper, we tackle the problem using Multi-scale Signed Recurrence Plots (MS-RP), an improvement of RP, and propose a novel method based on MS-RP images and Fully Convolutional Networks (FCN) for TSC. This method first introduces phase space dimension and time delay embedding of RP to produce multi-scale RP images; then, with the use of asymmetrical structure, constructed RP images can represent very long sequences (>700 points). Next, MS-RP images are obtained by multiplying designed sign masks in order to remove the tendency confusion. Finally, FCN is trained with MS-RP images to perform classification. Experimental results on 45 benchmark datasets demonstrate that our method improves the state-of-the-art in terms of classification accuracy and visualization evaluation.

scholarly journals Dual Path Attention Net for Remote Sensing Semantic Image Segmentation

2020 ◽  
Vol 9 (10) ◽  
pp. 571
Author(s):  
Jinglun Li ◽  
Jiapeng Xiu ◽  
Zhengqiu Yang ◽  
Chen Liu
Keyword(s):  
Remote Sensing ◽  
Spatial Information ◽  
Semantic Segmentation ◽  
Remote Sensing Images ◽  
Convolutional Networks ◽  
Fully Convolutional Networks ◽  
Augmentation Strategies ◽  
Rich Information ◽  
The Mean ◽  
The Rich

Semantic segmentation plays an important role in being able to understand the content of remote sensing images. In recent years, deep learning methods based on Fully Convolutional Networks (FCNs) have proved to be effective for the sematic segmentation of remote sensing images. However, the rich information and complex content makes the training of networks for segmentation challenging, and the datasets are necessarily constrained. In this paper, we propose a Convolutional Neural Network (CNN) model called Dual Path Attention Network (DPA-Net) that has a simple modular structure and can be added to any segmentation model to enhance its ability to learn features. Two types of attention module are appended to the segmentation model, one focusing on spatial information the other focusing upon the channel. Then, the outputs of these two attention modules are fused to further improve the network’s ability to extract features, thus contributing to more precise segmentation results. Finally, data pre-processing and augmentation strategies are used to compensate for the small number of datasets and uneven distribution. The proposed network was tested on the Gaofen Image Dataset (GID). The results show that the network outperformed U-Net, PSP-Net, and DeepLab V3+ in terms of the mean IoU by 0.84%, 2.54%, and 1.32%, respectively.

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