High-resolution remote sensing image building extraction combined with Faster-RCNN and Level-Set

2019 ◽  
Vol 34 (4) ◽  
pp. 439-447
Author(s):  
左俊皓 ZUO Jun-hao ◽  
赵 聪 ZHAO Cong ◽  
朱晓龙 ZHU Xiao-long ◽  
任洪娥 REN Hong-e
Keyword(s):  
Remote Sensing ◽  
High Resolution ◽  
Level Set ◽  
Remote Sensing Image ◽  
Building Extraction ◽  
Image Building

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-RESOLUTION REMOTE SENSING IMAGE BUILDING EXTRACTION BASED ON MARKOV MODEL

2018 ◽  
Vol XLII-3 ◽  
pp. 2411-2418 ◽  
Author(s):  
W. Zhao ◽  
L. Yan ◽  
Y. Chang ◽  
L. Gong
Keyword(s):  
Remote Sensing ◽  
High Resolution ◽  
Markov Model ◽  
Remote Sensing Image ◽  
Image Features ◽  
Building Extraction ◽  
Texture Information ◽  
Building Information ◽  
Building Area ◽  
Image Building

With the increase of resolution, remote sensing images have the characteristics of increased information load, increased noise, more complex feature geometry and texture information, which makes the extraction of building information more difficult. To solve this problem, this paper designs a high resolution remote sensing image building extraction method based on Markov model. This method introduces Contourlet domain map clustering and Markov model, captures and enhances the contour and texture information of high-resolution remote sensing image features in multiple directions, and further designs the spectral feature index that can characterize “pseudo-buildings” in the building area. Through the multi-scale segmentation and extraction of image features, the fine extraction from the building area to the building is realized. Experiments show that this method can restrain the noise of high-resolution remote sensing images, reduce the interference of non-target ground texture information, and remove the shadow, vegetation and other pseudo-building information, compared with the traditional pixel-level image information extraction, better performance in building extraction precision, accuracy and completeness.

scholarly journals Remote Sensing Image Building Extraction Method Based on Deep Learning

2020 ◽  
Vol 1631 ◽  
pp. 012010
Author(s):  
Minshui Wang ◽  
Mingchang Wang ◽  
Guodong Yang ◽  
Ziwei Liu
Keyword(s):  
Remote Sensing ◽  
Deep Learning ◽  
Extraction Method ◽  
Remote Sensing Image ◽  
Building Extraction ◽  
Image Building

Object Detection with an Improved Level Set Method

2013 ◽  
Vol 303-306 ◽  
pp. 1060-1066
Author(s):  
Hui Cao ◽  
Bo Cheng
Keyword(s):  
Remote Sensing ◽  
High Resolution ◽  
Object Detection ◽  
Level Set ◽  
Region Of Interest ◽  
Remote Sensing Image ◽  
Aerial Images ◽  
Important Research ◽  
Speed Up ◽  
Level Set Formulation

Object detection is quite an important research in remote sensing image analysis. In this paper, we propose an edge and region based model for high resolution remote sensing image segmentation with level set formulation. Our method firstly made an image enhancement based on ROI (Region of Interest). By introducing the edge speed-up function, we can save time through decreasing the iterations and get a flexible segmentation considering the complexity of high resolution remote sensing image. Our method has been preliminarily applied to QuickBird and aerial images.

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