scholarly journals Improved Fully Convolutional Network with Conditional Random Fields for Building Extraction

2018 ◽  
Vol 10 (7) ◽  
pp. 1135 ◽  
Author(s):  
Sanjeevan Shrestha ◽  
Leonardo Vanneschi
Keyword(s):  
Performance Measures ◽  
Random Fields ◽  
Conditional Random Fields ◽  
Remotely Sensed ◽  
High Accuracy ◽  
Large Network ◽  
Building Extraction ◽  
Deep Convolutional Neural Networks ◽  
Convolutional Network ◽  
Fully Convolutional Network

Building extraction from remotely sensed imagery plays an important role in urban planning, disaster management, navigation, updating geographic databases, and several other geospatial applications. Several published contributions dedicated to the applications of deep convolutional neural networks (DCNN) for building extraction using aerial/satellite imagery exists. However, in all these contributions, high accuracy is always obtained at the price of extremely complex and large network architectures. In this paper, we present an enhanced fully convolutional network (FCN) framework that is designed for building extraction of remotely sensed images by applying conditional random fields (CRFs). The main objective is to propose a methodology selecting a framework that balances high accuracy with low network complexity. A modern activation function, namely, the exponential linear unit (ELU), is applied to improve the performance of the fully convolutional network (FCN), thereby resulting in more accurate building prediction. To further reduce the noise (falsely classified buildings) and to sharpen the boundaries of the buildings, a post-processing conditional random fields (CRFs) is added at the end of the adopted convolutional neural network (CNN) framework. The experiments were conducted on Massachusetts building aerial imagery. The results show that our proposed framework outperformed the fully convolutional network (FCN), which is the existing baseline framework for semantic segmentation, in terms of performance measures such as the F1-score and IoU measure. Additionally, the proposed method outperformed a pre-existing classifier for building extraction using the same dataset in terms of the performance measures and network complexity.

High-accuracy image segmentation for lactating sows using a fully convolutional network

2018 ◽  
Vol 176 ◽  
pp. 36-47 ◽  
Author(s):  
Aqing Yang ◽  
Huasheng Huang ◽  
Chan Zheng ◽  
Xunmu Zhu ◽  
Xiaofan Yang ◽  
...  
Keyword(s):  
Image Segmentation ◽  
High Accuracy ◽  
Convolutional Network ◽  
Fully Convolutional Network ◽  
Lactating Sows

scholarly journals Road Extraction from Remote Sensing Images Using the Inner Convolution Integrated Encoder-Decoder Network and Directional Conditional Random Fields

2021 ◽  
Vol 13 (3) ◽  
pp. 465
Author(s):  
Shuyang Wang ◽  
Xiaodong Mu ◽  
Dongfang Yang ◽  
Hao He ◽  
Peng Zhao
Keyword(s):  
Remote Sensing ◽  
Random Fields ◽  
Conditional Random Fields ◽  
Road Extraction ◽  
Remote Sensing Images ◽  
Feature Maps ◽  
Convolutional Network ◽  
The Road ◽  
Disaster Assessment

Road extraction from remote sensing images is of great significance to urban planning, navigation, disaster assessment, and other applications. Although deep neural networks have shown a strong ability in road extraction, it remains a challenging task due to complex circumstances and factors such as occlusion. To improve the accuracy and connectivity of road extraction, we propose an inner convolution integrated encoder-decoder network with the post-processing of directional conditional random fields. Firstly, we design an inner convolutional network which can propagate information slice-by-slice within feature maps, thus enhancing the learning of road topology and linear features. Additionally, we present the directional conditional random fields to improve the quality of the extracted road by adding the direction of roads to the energy function of the conditional random fields. The experimental results on the Massachusetts road dataset show that the proposed approach achieves high-quality segmentation results, with the F1-score of 84.6%, which outperforms other comparable “state-of-the-art” approaches. The visualization results prove that the proposed approach is able to effectively extract roads from remote sensing images and can solve the road connectivity problem produced by occlusions to some extent.

scholarly journals Building Extraction from High Spatial Resolution Remote Sensing Images via Multiscale-Aware and Segmentation-Prior Conditional Random Fields

2020 ◽  
Vol 12 (23) ◽  
pp. 3983
Author(s):  
Qiqi Zhu ◽  
Zhen Li ◽  
Yanan Zhang ◽  
Qingfeng Guan
Keyword(s):  
Remote Sensing ◽  
Spatial Resolution ◽  
Random Fields ◽  
Conditional Random Fields ◽  
High Spatial Resolution ◽  
Conditional Random Field ◽  
Binary Classification ◽  
Building Extraction ◽  
Remote Sensing Images ◽  
Multi Scale

Building extraction is a binary classification task that separates the building area from the background in remote sensing images. The conditional random field (CRF) is directly modelled by the maximum posterior probability, which can make full use of the spatial neighbourhood information of both labelled and observed images. CRF is widely used in building footprint extraction. However, edge oversmoothing still exists when CRF is directly used to extract buildings from high spatial resolution (HSR) remote sensing images. Based on a computer vision multi-scale semantic segmentation network (D-LinkNet), a novel building extraction framework is proposed, named multiscale-aware and segmentation-prior conditional random fields (MSCRF). To solve the problem of losing building details in the downsampling process, D-LinkNet connecting the encoder and decoder is correspondingly used to generate the unary potential. By integrating multi-scale building features in the central module, D-LinkNet can integrate multiscale contextual information without loss of resolution. For the pairwise potential, the segmentation prior is fused to alleviate the influence of spectral diversity between the building and the background area. Moreover, the local class label cost term is introduced. The clear boundaries of the buildings are obtained by using the larger-scale context information. The experimental results demonstrate that the proposed MSCRF framework is superior to the state-of-the-art methods and performs well for building extraction of complex scenes.

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