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

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 INTELLIGENT DETECTION OF STRUCTURE FROM REMOTE SENSING IMAGES BASED ON DEEP LEARNING METHOD

2018 ◽  
Vol XLII-3 ◽  
pp. 1959-1962
Author(s):  
L. Xin
Keyword(s):  
Remote Sensing ◽  
Deep Learning ◽  
Image Interpretation ◽  
Network Models ◽  
Remote Sensing Image ◽  
Building Extraction ◽  
Learning Method ◽  
Remote Sensing Images ◽  
Neural Network Models ◽  
Intelligent Detection

Utilizing high-resolution remote sensing images for earth observation has become the common method of land use monitoring. It requires great human participation when dealing with traditional image interpretation, which is inefficient and difficult to guarantee the accuracy. At present, the artificial intelligent method such as deep learning has a large number of advantages in the aspect of image recognition. By means of a large amount of remote sensing image samples and deep neural network models, we can rapidly decipher the objects of interest such as buildings, etc. Whether in terms of efficiency or accuracy, deep learning method is more preponderant. This paper explains the research of deep learning method by a great mount of remote sensing image samples and verifies the feasibility of building extraction via experiments.

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

Deep Learning Based High-Resolution Remote Sensing Image classification

2017 ◽  
Vol 7 (10) ◽  
pp. 22
Author(s):  
Sumit Kaur
Keyword(s):  
Machine Learning ◽  
Remote Sensing ◽  
Deep Learning ◽  
Image Classification ◽  
Language Processing ◽  
Object Perception ◽  
Remote Sensing Image ◽  
Research Area ◽  
Remote Sensing Image Classification ◽  
Unsupervised Algorithms

Abstract- Deep learning is an emerging research area in machine learning and pattern recognition field which has been presented with the goal of drawing Machine Learning nearer to one of its unique objectives, Artificial Intelligence. It tries to mimic the human brain, which is capable of processing and learning from the complex input data and solving different kinds of complicated tasks well. Deep learning (DL) basically based on a set of supervised and unsupervised algorithms that attempt to model higher level abstractions in data and make it self-learning for hierarchical representation for classification. In the recent years, it has attracted much attention due to its state-of-the-art performance in diverse areas like object perception, speech recognition, computer vision, collaborative filtering and natural language processing. This paper will present a survey on different deep learning techniques for remote sensing image classification. 

Sign in / Sign up

Export Citation Format

Share Document