scholarly journals Context Aggregation Network for Semantic Labeling in Aerial Images

2019 ◽  
Vol 11 (10) ◽  
pp. 1158 ◽  
Author(s):  
Wensheng Cheng ◽  
Wen Yang ◽  
Min Wang ◽  
Gang Wang ◽  
Jinyong Chen
Keyword(s):  
High Resolution ◽  
Multiple Scales ◽  
Image Data ◽  
Remote Sensing Image ◽  
Object Localization ◽  
Aerial Images ◽  
Localization Accuracy ◽  
Multi Scale ◽  
Semantic Labeling ◽  
Multi Level

Semantic labeling for high resolution aerial images is a fundamental and necessary task in remote sensing image analysis. It is widely used in land-use surveys, change detection, and environmental protection. Recent researches reveal the superiority of Convolutional Neural Networks (CNNs) in this task. However, multi-scale object recognition and accurate object localization are two major problems for semantic labeling methods based on CNNs in high resolution aerial images. To handle these problems, we design a Context Fuse Module, which is composed of parallel convolutional layers with kernels of different sizes and a global pooling branch, to aggregate context information at multiple scales. We propose an Attention Mix Module, which utilizes a channel-wise attention mechanism to combine multi-level features for higher localization accuracy. We further employ a Residual Convolutional Module to refine features in all feature levels. Based on these modules, we construct a new end-to-end network for semantic labeling in aerial images. We evaluate the proposed network on the ISPRS Vaihingen and Potsdam datasets. Experimental results demonstrate that our network outperforms other competitors on both datasets with only raw image data.

scholarly journals Research On The Classification Of High Resolution Image Based On Object-oriented And Class Rule

2015 ◽  
Vol XL-7/W4 ◽  
pp. 75-80 ◽  
Author(s):  
C. K. Li ◽  
W. Fang ◽  
X. J. Dong
Keyword(s):  
Remote Sensing ◽  
High Resolution ◽  
Image Classification ◽  
Object Oriented ◽  
Remote Sensing Data ◽  
Image Data ◽  
Remote Sensing Image ◽  
Classification Method ◽  
Multi Level ◽  
Image Object

With the development of remote sensing technology, the spatial resolution, spectral resolution and time resolution of remote sensing data is greatly improved. How to efficiently process and interpret the massive high resolution remote sensing image data for ground objects, which with spatial geometry and texture information, has become the focus and difficulty in the field of remote sensing research. An object oriented and rule of the classification method of remote sensing data has presents in this paper. Through the discovery and mining the rich knowledge of spectrum and spatial characteristics of high-resolution remote sensing image, establish a multi-level network image object segmentation and classification structure of remote sensing image to achieve accurate and fast ground targets classification and accuracy assessment. Based on worldview-2 image data in the Zangnan area as a study object, using the object-oriented image classification method and rules to verify the experiment which is combination of the mean variance method, the maximum area method and the accuracy comparison to analysis, selected three kinds of optimal segmentation scale and established a multi-level image object network hierarchy for image classification experiments. The results show that the objectoriented rules classification method to classify the high resolution images, enabling the high resolution image classification results similar to the visual interpretation of the results and has higher classification accuracy. The overall accuracy and Kappa coefficient of the object-oriented rules classification method were 97.38%, 0.9673; compared with object-oriented SVM method, respectively higher than 6.23%, 0.078; compared with object-oriented KNN method, respectively more than 7.96%, 0.0996. The extraction precision and user accuracy of the building compared with object-oriented SVM method, respectively higher than 18.39%, 3.98%, respectively better than the object-oriented KNN method 21.27%, 14.97%.

scholarly journals Archaeological Surveying of Subarctic and Arctic Landscapes: Comparing the Performance of Airborne Laser Scanning and Remote Sensing Image Data

2021 ◽  
Vol 13 (4) ◽  
pp. 1917
Author(s):  
Alma Elizabeth Thuestad ◽  
Ole Risbøl ◽  
Jan Ingolf Kleppe ◽  
Stine Barlindhaug ◽  
Elin Rose Myrvoll
Keyword(s):  
Remote Sensing ◽  
Laser Scanning ◽  
Digital Terrain Model ◽  
Image Data ◽  
Remote Sensing Image ◽  
Airborne Laser Scanning ◽  
Aerial Images ◽  
Detection Rates ◽  
Airborne Laser ◽  
Pros And Cons

What can remote sensing contribute to archaeological surveying in subarctic and arctic landscapes? The pros and cons of remote sensing data vary as do areas of utilization and methodological approaches. We assessed the applicability of remote sensing for archaeological surveying of northern landscapes using airborne laser scanning (LiDAR) and satellite and aerial images to map archaeological features as a basis for (a) assessing the pros and cons of the different approaches and (b) assessing the potential detection rate of remote sensing. Interpretation of images and a LiDAR-based bare-earth digital terrain model (DTM) was based on visual analyses aided by processing and visualizing techniques. 368 features were identified in the aerial images, 437 in the satellite images and 1186 in the DTM. LiDAR yielded the better result, especially for hunting pits. Image data proved suitable for dwellings and settlement sites. Feature characteristics proved a key factor for detectability, both in LiDAR and image data. This study has shown that LiDAR and remote sensing image data are highly applicable for archaeological surveying in northern landscapes. It showed that a multi-sensor approach contributes to high detection rates. Our results have improved the inventory of archaeological sites in a non-destructive and minimally invasive manner.

scholarly journals A Multi-Task Network with Distance–Mask–Boundary Consistency Constraints for Building Extraction from Aerial Images

2021 ◽  
Vol 13 (14) ◽  
pp. 2656
Author(s):  
Furong Shi ◽  
Tong Zhang
Keyword(s):  
Distance Estimation ◽  
Image Data ◽  
Learning Technologies ◽  
Aerial Images ◽  
Superior Performance ◽  
Aerial Image ◽  
Great Success ◽  
Building Extraction ◽  
Shape Information ◽  
Multi Scale

Deep-learning technologies, especially convolutional neural networks (CNNs), have achieved great success in building extraction from areal images. However, shape details are often lost during the down-sampling process, which results in discontinuous segmentation or inaccurate segmentation boundary. In order to compensate for the loss of shape information, two shape-related auxiliary tasks (i.e., boundary prediction and distance estimation) were jointly learned with building segmentation task in our proposed network. Meanwhile, two consistency constraint losses were designed based on the multi-task network to exploit the duality between the mask prediction and two shape-related information predictions. Specifically, an atrous spatial pyramid pooling (ASPP) module was appended to the top of the encoder of a U-shaped network to obtain multi-scale features. Based on the multi-scale features, one regression loss and two classification losses were used for predicting the distance-transform map, segmentation, and boundary. Two inter-task consistency-loss functions were constructed to ensure the consistency between distance maps and masks, and the consistency between masks and boundary maps. Experimental results on three public aerial image data sets showed that our method achieved superior performance over the recent state-of-the-art models.

scholarly journals U-net Network for Building Information Extraction of Remote-Sensing Imagery

2018 ◽  
Vol 14 (12) ◽  
pp. 179
Author(s):  
Jingtan Li ◽  
Maolin Xu ◽  
Hongling Xiu
Keyword(s):  
Remote Sensing ◽  
High Resolution ◽  
Information Extraction ◽  
Image Data ◽  
Semantic Segmentation ◽  
Remote Sensing Image ◽  
Remote Sensing Images ◽  
Training Set ◽  
Building Information ◽  
The Face

With the resolution of remote sensing images is getting higher and higher, high-resolution remote sensing images are widely used in many areas. Among them, image information extraction is one of the basic applications of remote sensing images. In the face of massive high-resolution remote sensing image data, the traditional method of target recognition is difficult to cope with. Therefore, this paper proposes a remote sensing image extraction based on U-net network. Firstly, the U-net semantic segmentation network is used to train the training set, and the validation set is used to verify the training set at the same time, and finally the test set is used for testing. The experimental results show that U-net can be applied to the extraction of buildings.

scholarly journals A Multi-Scale Filtering Building Index for Building Extraction in Very High-Resolution Satellite Imagery

2019 ◽  
Vol 11 (5) ◽  
pp. 482 ◽  
Author(s):  
Qi Bi ◽  
Kun Qin ◽  
Han Zhang ◽  
Ye Zhang ◽  
Zhili Li ◽  
...  
Keyword(s):  
Remote Sensing ◽  
High Resolution ◽  
Common Knowledge ◽  
Remote Sensing Image ◽  
Morphological Operations ◽  
Building Extraction ◽  
Multi Scale ◽  
Training Samples ◽  
Image Building ◽  
Very High

Building extraction plays a significant role in many high-resolution remote sensing image applications. Many current building extraction methods need training samples while it is common knowledge that different samples often lead to different generalization ability. Morphological building index (MBI), representing morphological features of building regions in an index form, can effectively extract building regions especially in Chinese urban regions without any training samples and has drawn much attention. However, some problems like the heavy computation cost of multi-scale and multi-direction morphological operations still exist. In this paper, a multi-scale filtering building index (MFBI) is proposed in the hope of overcoming these drawbacks and dealing with the increasing noise in very high-resolution remote sensing image. The profile of multi-scale average filtering is averaged and normalized to generate this index. Moreover, to fully utilize the relatively little spectral information in very high-resolution remote sensing image, two scenarios to generate the multi-channel multi-scale filtering index (MMFBI) are proposed. While no high-resolution remote sensing image building extraction dataset is open to the public now and the current very high-resolution remote sensing image building extraction datasets usually contain samples from the Northern American or European regions, we offer a very high-resolution remote sensing image building extraction datasets in which the samples contain multiple building styles from multiple Chinese regions. The proposed MFBI and MMFBI outperform MBI and the currently used object based segmentation method on the dataset, with a high recall and F-score. Meanwhile, the computation time of MFBI and MBI is compared on three large-scale very high-resolution satellite image and the sensitivity analysis demonstrates the robustness of the proposed method.

scholarly journals High-quality observation of surface imperviousness for urban runoff modelling using UAV imagery

2015 ◽  
Vol 19 (10) ◽  
pp. 4215-4228 ◽  
Author(s):  
P. Tokarczyk ◽  
J. P. Leitao ◽  
J. Rieckermann ◽  
K. Schindler ◽  
F. Blumensaat
Keyword(s):  
Land Use ◽  
High Resolution ◽  
Surface Runoff ◽  
Urban Areas ◽  
Image Data ◽  
Aerial Images ◽  
Urban Drainage ◽  
High Quality ◽  
Modern Classification

Abstract. Modelling rainfall–runoff in urban areas is increasingly applied to support flood risk assessment, particularly against the background of a changing climate and an increasing urbanization. These models typically rely on high-quality data for rainfall and surface characteristics of the catchment area as model input. While recent research in urban drainage has been focusing on providing spatially detailed rainfall data, the technological advances in remote sensing that ease the acquisition of detailed land-use information are less prominently discussed within the community. The relevance of such methods increases as in many parts of the globe, accurate land-use information is generally lacking, because detailed image data are often unavailable. Modern unmanned aerial vehicles (UAVs) allow one to acquire high-resolution images on a local level at comparably lower cost, performing on-demand repetitive measurements and obtaining a degree of detail tailored for the purpose of the study. In this study, we investigate for the first time the possibility of deriving high-resolution imperviousness maps for urban areas from UAV imagery and of using this information as input for urban drainage models. To do so, an automatic processing pipeline with a modern classification method is proposed and evaluated in a state-of-the-art urban drainage modelling exercise. In a real-life case study (Lucerne, Switzerland), we compare imperviousness maps generated using a fixed-wing consumer micro-UAV and standard large-format aerial images acquired by the Swiss national mapping agency (swisstopo). After assessing their overall accuracy, we perform an end-to-end comparison, in which they are used as an input for an urban drainage model. Then, we evaluate the influence which different image data sources and their processing methods have on hydrological and hydraulic model performance. We analyse the surface runoff of the 307 individual subcatchments regarding relevant attributes, such as peak runoff and runoff volume. Finally, we evaluate the model's channel flow prediction performance through a cross-comparison with reference flow measured at the catchment outlet. We show that imperviousness maps generated from UAV images processed with modern classification methods achieve an accuracy comparable to standard, off-the-shelf aerial imagery. In the examined case study, we find that the different imperviousness maps only have a limited influence on predicted surface runoff and pipe flows, when traditional workflows are used. We expect that they will have a substantial influence when more detailed modelling approaches are employed to characterize land use and to predict surface runoff. We conclude that UAV imagery represents a valuable alternative data source for urban drainage model applications due to the possibility of flexibly acquiring up-to-date aerial images at a quality compared with off-the-shelf image products and a competitive price at the same time. We believe that in the future, urban drainage models representing a higher degree of spatial detail will fully benefit from the strengths of UAV imagery.

scholarly journals AUTOMATIC OBJECT EXTRACTION FROM HIGH RESOLUTION AERIAL IMAGERY WITH SIMPLE LINEAR ITERATIVE CLUSTERING AND CONVOLUTIONAL NEURAL NETWORKS

2019 ◽  
Vol XLII-2/W16 ◽  
pp. 61-66 ◽  
Author(s):  
A. C. Carrilho ◽  
M. Galo
Keyword(s):  
High Resolution ◽  
Object Detection ◽  
Semantic Segmentation ◽  
Aerial Images ◽  
Machine Learning Techniques ◽  
Object Extraction ◽  
Multi Scale ◽  
Learning Techniques ◽  
Simple Linear Iterative Clustering ◽  
Fast Region

<p><strong>Abstract.</strong> Recent advances in machine learning techniques for image classification have led to the development of robust approaches to both object detection and extraction. Traditional CNN architectures, such as LeNet, AlexNet and CaffeNet, usually use as input images of fixed sizes taken from objects and attempt to assign labels to those images. Another possible approach is the Fast Region-based CNN (or Fast R-CNN), which works by using two models: (i) a Region Proposal Network (RPN) which generates a set of potential Regions of Interest (RoI) in the image; and (ii) a traditional CNN which assigns labels to the proposed RoI. As an alternative, this study proposes an approach to automatic object extraction from aerial images similar to the Fast R-CNN architecture, the main difference being the use of the Simple Linear Iterative Clustering (SLIC) algorithm instead of an RPN to generate the RoI. The dataset used is composed of high-resolution aerial images and the following classes were considered: house, sport court, hangar, building, swimming pool, tree, and street/road. The proposed method can generate RoI with different sizes by running a multi-scale SLIC approach. The overall accuracy obtained for object detection was 89% and the major advantage is that the proposed method is capable of semantic segmentation by assigning a label to each selected RoI. Some of the problems encountered are related to object proximity, in which different instances appeared merged in the results.</p>

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