Building Extraction Building Extraction and Verification from Spaceborne and Aerial Imagery using Image Understanding Fusion Techniques

1995 ◽  
pp. 221-230 ◽  
Author(s):  
Taejung Kim ◽  
Jan-Peter Muller
Keyword(s):  
Image Understanding ◽  
Aerial Imagery ◽  
Building Extraction

scholarly journals Integration of Deep Learning Algorithms and Bilateral Filters with the Purpose of Building Extraction from Mono Optical Aerial Imagery

2019 ◽  
Vol 7 (2) ◽  
pp. 241-263
Author(s):  
Mahdi Khoshboresh Masouleh ◽  
Reza Shah-Hosseini ◽  
Abdol Reza Safari ◽  
◽  
◽  
...  
Keyword(s):  
Deep Learning ◽  
Learning Algorithms ◽  
Aerial Imagery ◽  
Building Extraction ◽  
Bilateral Filters

Large-scale building extraction in very high-resolution aerial imagery using Mask R-CNN

2019 ◽  
Author(s):  
Dorothee Stiller ◽  
Thomas Stark ◽  
Michael Wurm ◽  
Stefan Dech ◽  
Hannes Taubenbock
Keyword(s):  
High Resolution ◽  
Large Scale ◽  
Aerial Imagery ◽  
Building Extraction ◽  
Very High

scholarly journals Building Extraction from Very High Resolution Aerial Imagery Using Joint Attention Deep Neural Network

2019 ◽  
Vol 11 (24) ◽  
pp. 2970 ◽  
Author(s):  
Ziran Ye ◽  
Yongyong Fu ◽  
Muye Gan ◽  
Jinsong Deng ◽  
Alexis Comber ◽  
...  
Keyword(s):  
Neural Network ◽  
High Resolution ◽  
Spatial Information ◽  
Remote Sensing Data ◽  
Aerial Imagery ◽  
Building Extraction ◽  
Feature Maps ◽  
Convolutional Network ◽  
Wide Range ◽  
Very High

Automated methods to extract buildings from very high resolution (VHR) remote sensing data have many applications in a wide range of fields. Many convolutional neural network (CNN) based methods have been proposed and have achieved significant advances in the building extraction task. In order to refine predictions, a lot of recent approaches fuse features from earlier layers of CNNs to introduce abundant spatial information, which is known as skip connection. However, this strategy of reusing earlier features directly without processing could reduce the performance of the network. To address this problem, we propose a novel fully convolutional network (FCN) that adopts attention based re-weighting to extract buildings from aerial imagery. Specifically, we consider the semantic gap between features from different stages and leverage the attention mechanism to bridge the gap prior to the fusion of features. The inferred attention weights along spatial and channel-wise dimensions make the low level feature maps adaptive to high level feature maps in a target-oriented manner. Experimental results on three publicly available aerial imagery datasets show that the proposed model (RFA-UNet) achieves comparable and improved performance compared to other state-of-the-art models for building extraction.

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