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Improved Anchor-Free Instance Segmentation for Building Extraction from High-Resolution Remote Sensing Images
2020
Remote Sensing
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for accurate extraction of buildings in high-resolution remote sensing images. ...
Building extraction from high-resolution remote sensing images plays a vital part in urban planning, safety supervision, geographic databases updates, and some other applications. ...
As can be seen from above, CNN-based instance segmentation methods for building extraction from high-resolution remote sensing images are generally via two-stage, just like Mask R-CNN, which focus primarily ...
doi:10.3390/rs12182910
fatcat:jaw23x7cb5e4jfyqphjdnblbam
Improved Mask R-CNN for Rural Building Roof Type Recognition from UAV High-Resolution Images: A Case Study in Hunan Province, China
2022
Remote Sensing
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In response to the above problems, this paper proposes a method for identifying roof types of complex rural buildings based on visible high-resolution remote sensing images from UAVs. ...
Secondly, an improved Mask R-CNN model is proposed to learn more complex features of different types of images of building roofs by using the ResNet152 feature extraction network with migration learning ...
Data Availability Statement: The data presented in this study are openly available in FigShare at https://doi.org/10.6084/m9.figshare.17696072.v1 (accessed on 20 November 2021). ...
doi:10.3390/rs14020265
fatcat:w7vpowmzwjhete7jfugodzeeaq
Remote sensing image building detection method based on Mask R-CNN
2021
Complex & Intelligent Systems
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Finally, the improved Mask R-CNN algorithm is used to detect buildings in the images. ...
To achieve the technical requirements of flood disaster relief projects, this paper proposes a building extraction method for use with remote sensing images that combines traditional digital image processing ...
Acknowledgements The research work described in this paper was fully supported by the National Key R&D program of China (2017YFC1502505) and the Joint Research Fund in Astronomy (U2031136) under cooperative ...
doi:10.1007/s40747-021-00322-z
fatcat:aefobvdbgbac7kvbnwjiz4acl4
Deep convolutional neural network application on rooftop detection for aerial image
[article]
2019
arXiv
pre-print
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In this research, we proposed an automatic rooftop detection method based on the convolutional neural network (CNN) to extract buildings in the city of Christchurch and tuned hyperparameters to detect ...
small detached houses from the aerial image. ...
Various algorithms have been proposed through the literature that detect and extract buildings from the remote sensing images. ...
arXiv:1910.13509v1
fatcat:bsaxfahhkneu7ag3py5nhq2mfu
Intelligent Object Recognition of Urban Water Bodies Based on Deep Learning for Multi-Source and Multi-Temporal High Spatial Resolution Remote Sensing Imagery
2020
Sensors
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High spatial resolution remote sensing image (HSRRSI) data provide rich texture, geometric structure, and spatial distribution information for surface water bodies. ...
a new method for water body recognition based on remote sensing data. ...
In this study, we used panchromatic-multispectral fusion methods to generate remote sensing images with both high spatial resolution and high spectral resolution as training data in an improved Mask R-CNN ...
doi:10.3390/s20020397
pmid:31936791
pmcid:PMC7014233
fatcat:37n5fijasjfzzbzd5djspbdr34
S-CNN-BASED SHIP DETECTION FROM HIGH-RESOLUTION REMOTE SENSING IMAGES
2016
The International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences
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However, it makes the task difficult with high-resolution remote sensing images with complex background and various types of ships with different poses, shapes and scales. ...
from the ship model combined with an improved saliency detection method. ...
The first module aims to extract ship proposals from a high-resolution remote sensing image with two effective methods, the ship model based detection and saliency based one. ...
doi:10.5194/isprs-archives-xli-b7-423-2016
fatcat:f5bg5ocevffjnkdl4i5c75xwgy
S-CNN-BASED SHIP DETECTION FROM HIGH-RESOLUTION REMOTE SENSING IMAGES
2016
The International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences
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However, it makes the task difficult with high-resolution remote sensing images with complex background and various types of ships with different poses, shapes and scales. ...
from the ship model combined with an improved saliency detection method. ...
The first module aims to extract ship proposals from a high-resolution remote sensing image with two effective methods, the ship model based detection and saliency based one. ...
doi:10.5194/isprsarchives-xli-b7-423-2016
fatcat:ffdlmyvhnnh4rouknlurbwbz5y
TARGETS MASK U-NET FOR WIND TURBINES DETECTION IN REMOTE SENSING IMAGES
2018
The International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences
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To detect wind turbines precisely and quickly in very high resolution remote sensing images (VHRRSI) we propose target mask U-Net. ...
The comparison of detection accuracy and time consuming with the weakly supervised targets detection method based on CNN illustrates the superiority of our method. ...
Ok et al. (2013) modeled the spatial relationship between buildings and their shadows to automatically detect buildings with arbitrary shapes from monocular very high resolution (VHR) remote sensing images ...
doi:10.5194/isprs-archives-xlii-3-475-2018
fatcat:pjytwc63dffyjmpqsrlaiyizce
HQ-ISNet: High-Quality Instance Segmentation for Remote Sensing Imagery
2020
Remote Sensing
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In this article, a novel instance segmentation approach of HR remote sensing imagery based on Cascade Mask R-CNN is proposed, which is called a high-quality instance segmentation network (HQ-ISNet). ...
However, there are rare methods currently suitable for instance segmentation in the HR remote sensing images. ...
Therefore, this paper focuses on a high-quality instance segmentation method for remote sensing images, especially for high-resolution artificial targets. ...
doi:10.3390/rs12060989
fatcat:fataurpmhfbh3n43l63cdedo6a
A Coarse-to-Fine Contour Optimization Network for Extracting Building Instances from High-Resolution Remote Sensing Imagery
2021
Remote Sensing
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Challenges still exist in extracting building instances from high-resolution remote sensing imagery mainly because of complex structures, variety of scales, and interconnected buildings. ...
Experimental results on three challenging building extraction datasets demonstrated that the proposed method outperformed the state-of-the-art methods' accuracy and quality of building contours. ...
from high-resolution remote sensing imagery and alleviate the above two limitations. ...
doi:10.3390/rs13193814
fatcat:ekjj2rdhe5dwzjdmcsmbfj7myy
BUILDING OUTLINE DELINEATION: FROM VERY HIGH RESOLUTION REMOTE SENSING IMAGERY TO POLYGONS WITH AN IMPROVED END-TO-END LEARNING FRAMEWORK
2020
The International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences
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Deep learning methods based on Fully convolution networks (FCNs) have shown an impressive progress in building outline delineation from very high resolution (VHR) remote sensing (RS) imagery. ...
Common issues still exist in extracting precise building shapes and outlines, often resulting in irregular edges and over smoothed corners. ...
The first author is funded by the China Scholarship Council (CSC) from the Ministry of Education of P.R. China. ...
doi:10.5194/isprs-archives-xliii-b2-2020-731-2020
fatcat:2mhcmuev5zcjrhnk64kdtdmo2i
Building Footprint Extraction from High Resolution Aerial Images Using Generative Adversarial Network (GAN) Architecture
2020
IEEE Access
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Building extraction with high accuracy using semantic segmentation from high-resolution remotely sensed imagery has a wide range of applications like urban planning, updating of geospatial database, and ...
of some barriers like cars, vegetation cover and shadow of trees in the highresolution remote sensing imagery. ...
Instead of a batch-based CNN model, [28] applied an object-based segmentation CNN approach with a similar architecture for building extraction from orthorectified images with a 12-cm spatial resolution ...
doi:10.1109/access.2020.3038225
fatcat:fuhwrnm5dfg7nck2d52xsb2yry
Building Extraction from Remote Sensing Images with Sparse Token Transformers
2021
Remote Sensing
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Deep learning methods have achieved considerable progress in remote sensing image building extraction. Most building extraction methods are based on Convolutional Neural Networks (CNN). ...
We design an efficient dual-pathway transformer structure that learns the long-term dependency of tokens in both their spatial and channel dimensions and achieves state-of-the-art accuracy on benchmark ...
Conclusions In this work, we propose an efficient transformer-based building extraction method for remote sensing images. ...
doi:10.3390/rs13214441
fatcat:sbe72zybwrdw3anlg7kyeyzlve
Mapping Tea Plantations from VHR Images Using OBIA and Convolutional Neural Networks
2020
Remote Sensing
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We propose an object-based convolutional neural network (CNN) to extract tea plantations from very high resolution remote sensing images. ...
We compared the proposed classification with existing methods: Object-based classification using random forest, Mask R-CNN, and object-based CNN without fine-tuning. ...
., Ltd. for providing GF-2 images and Siying Wu for technical support.
Conflicts of Interest: The authors declare no conflict of interest. ...
doi:10.3390/rs12182935
fatcat:qpcjmyf5qrerrcxiishb2exwle
Techniques for the Automatic Detection and Hiding of Sensitive Targets in Emergency Mapping Based on Remote Sensing Data
2021
ISPRS International Journal of Geo-Information
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Traditional emergency remote sensing mapping methods use decryption algorithms based on manual retrieval and image editing tools when processing sensitive targets. ...
Experiments revealed that our method is more efficient than traditional manual processing; the precision is 94.87%, the recall is 84.75% higher than that of the original mask R-CNN model, and the F1-score ...
Acknowledgments: We would like to acknowledges the National Key R&D Program of China and the National Natural Science Foundation of China for the financial support. ...
doi:10.3390/ijgi10020068
fatcat:p4gfvvq7m5h4npi5l3amqvubwu
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