scholarly journals A Multiple-Feature Reuse Network to Extract Buildings from Remote Sensing Imagery

2018 ◽  
Vol 10 (9) ◽  
pp. 1350 ◽  
Author(s):  
Lin Li ◽  
Jian Liang ◽  
Min Weng ◽  
Haihong Zhu
Keyword(s):  
Remote Sensing ◽  
High Resolution ◽  
Image Features ◽  
Building Extraction ◽  
Remote Sensing Imagery ◽  
High Resolution Imagery ◽  
Multiple Feature ◽  
Hierarchical Features ◽  
Direct Use ◽  
Different Levels

Automatic building extraction from remote sensing imagery is important in many applications. The success of convolutional neural networks (CNNs) has also led to advances in using CNNs to extract man-made objects from high-resolution imagery. However, the large appearance and size variations of buildings make it difficult to extract both crowded small buildings and large buildings. High-resolution imagery must be segmented into patches for CNN models due to GPU memory limitations, and buildings are typically only partially contained in a single patch with little context information. To overcome the problems involved when using different levels of image features with common CNN models, this paper proposes a novel CNN architecture called a multiple-feature reuse network (MFRN) in which each layer is connected to all the subsequent layers of the same size, enabling the direct use of the hierarchical features in each layer. In addition, the model includes a smart decoder that enables precise localization with less GPU load. We tested our model on a large real-world remote sensing dataset and obtained an overall accuracy of 94.5% and an 85% F1 score, which outperformed the compared CNN models, including a 56-layer fully convolutional DenseNet with 93.8% overall accuracy and an F1 score of 83.5%. The experimental results indicate that the MFRN approach to connecting convolutional layers improves the performance of common CNN models for extracting buildings of different sizes and can achieve high accuracy with a consumer-level GPU.

scholarly journals Multi-Scene Building Height Estimation Method Based on Shadow in High Resolution Imagery

2021 ◽  
Vol 13 (15) ◽  
pp. 2862
Author(s):  
Yakun Xie ◽  
Dejun Feng ◽  
Sifan Xiong ◽  
Jun Zhu ◽  
Yangge Liu
Keyword(s):  
Remote Sensing ◽  
High Resolution ◽  
Estimation Method ◽  
Absolute Error ◽  
Large Error ◽  
Estimation Methods ◽  
Height Estimation ◽  
Building Height ◽  
Remote Sensing Imagery ◽  
High Resolution Imagery

Accurately building height estimation from remote sensing imagery is an important and challenging task. However, the existing shadow-based building height estimation methods have large errors due to the complex environment in remote sensing imagery. In this paper, we propose a multi-scene building height estimation method based on shadow in high resolution imagery. First, the shadow of building is classified and described by analyzing the features of building shadow in remote sensing imagery. Second, a variety of shadow-based building height estimation models is established in different scenes. In addition, a method of shadow regularization extraction is proposed, which can solve the problem of mutual adhesion shadows in dense building areas effectively. Finally, we propose a method for shadow length calculation combines with the fish net and the pauta criterion, which means that the large error caused by the complex shape of building shadow can be avoided. Multi-scene areas are selected for experimental analysis to prove the validity of our method. The experiment results show that the accuracy rate is as high as 96% within 2 m of absolute error of our method. In addition, we compared our proposed approach with the existing methods, and the results show that the absolute error of our method are reduced by 1.24 m-3.76 m, which can achieve high-precision estimation of building height.

Building Extraction From Remote Sensing Imagery With a High-Resolution Capsule Network

2021 ◽  
pp. 1-5
Author(s):  
Yongtao Yu ◽  
Chao Liu ◽  
Junyong Gao ◽  
Shenghua Jin ◽  
Xiaoling Jiang ◽  
...  
Keyword(s):  
Remote Sensing ◽  
High Resolution ◽  
Building Extraction ◽  
Remote Sensing Imagery

scholarly journals A Coarse-to-Fine Contour Optimization Network for Extracting Building Instances from High-Resolution Remote Sensing Imagery

2021 ◽  
Vol 13 (19) ◽  
pp. 3814
Author(s):  
Fang Fang ◽  
Kaishun Wu ◽  
Yuanyuan Liu ◽  
Shengwen Li ◽  
Bo Wan ◽  
...  
Keyword(s):  
Remote Sensing ◽  
High Resolution ◽  
State Of The Art ◽  
Complex Structures ◽  
Building Extraction ◽  
Remote Sensing Imagery ◽  
Feature Pyramid ◽  
Coarse To Fine ◽  
Original Feature

Building instances extraction is an essential task for surveying and mapping. Challenges still exist in extracting building instances from high-resolution remote sensing imagery mainly because of complex structures, variety of scales, and interconnected buildings. This study proposes a coarse-to-fine contour optimization network to improve the performance of building instance extraction. Specifically, the network contains two special sub-networks: attention-based feature pyramid sub-network (AFPN) and coarse-to-fine contour sub-network. The former sub-network introduces channel attention into each layer of the original feature pyramid network (FPN) to improve the identification of small buildings, and the latter is designed to accurately extract building contours via two cascaded contour optimization learning. Furthermore, the whole network is jointly optimized by multiple losses, that is, a contour loss, a classification loss, a box regression loss and a general mask loss. 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.

scholarly journals DE-Net: Deep Encoding Network for Building Extraction from High-Resolution Remote Sensing Imagery

2019 ◽  
Vol 11 (20) ◽  
pp. 2380 ◽  
Author(s):  
Liu ◽  
Luo ◽  
Huang ◽  
Hu ◽  
Sun ◽  
...  
Keyword(s):  
Remote Sensing ◽  
Image Segmentation ◽  
High Resolution ◽  
Spatial Information ◽  
Training Data ◽  
Fine Tuning ◽  
Building Extraction ◽  
Feature Maps ◽  
Deep Convolutional Neural Networks ◽  
Remote Sensing Imagery

Deep convolutional neural networks have promoted significant progress in building extraction from high-resolution remote sensing imagery. Although most of such work focuses on modifying existing image segmentation networks in computer vision, we propose a new network in this paper, Deep Encoding Network (DE-Net), that is designed for the very problem based on many lately introduced techniques in image segmentation. Four modules are used to construct DE-Net: the inceptionstyle downsampling modules combining a striding convolution layer and a max-pooling layer, the encoding modules comprising six linear residual blocks with a scaled exponential linear unit (SELU) activation function, the compressing modules reducing the feature channels, and a densely upsampling module that enables the network to encode spatial information inside feature maps. Thus, DE-Net achieves stateoftheart performance on the WHU Building Dataset in recall, F1-Score, and intersection over union (IoU) metrics without pretraining. It also outperformed several segmentation networks in our self-built Suzhou Satellite Building Dataset. The experimental results validate the effectiveness of DE-Net on building extraction from aerial imagery and satellite imagery. It also suggests that given enough training data, designing and training a network from scratch may excel fine-tuning models pre-trained on datasets unrelated to building extraction.

AN IMPROVED APPROACH OF INFORMATION EXTRACTION FOR EARTHQUAKE-DAMAGED BUILDINGS USING HIGH-RESOLUTION IMAGERY

2011 ◽  
Vol 05 (04) ◽  
pp. 389-399 ◽  
Author(s):  
XIAODONG LI ◽  
WUNIAN YANG ◽  
TIANQI AO ◽  
HONGXIA LI ◽  
WENQING CHEN
Keyword(s):  
Remote Sensing ◽  
High Resolution ◽  
Information Extraction ◽  
Image Features ◽  
Building Recognition ◽  
Sensing Technology ◽  
High Resolution Imagery ◽  
Building Information ◽  
Ground Object ◽  
High Resolution Satellite Imagery

The development of remote sensing technology, especially the availability of high-resolution satellite imagery, has been applied to building recognition, hazard investigation and rapid pre-evaluation in post-earthquake management. Existing pixel-oriented approaches which are commonly used for satellite high-resolution imagery have limitations in information extraction, ground object classification, and processing speed. This paper presents an object-oriented method to extract earthquake-damaged building information using high-resolution remote sensing imagery of the 5.12 Wenchuan Earthquake. This method segmented the whole image into non-intersecting pieces of image objects, and then classified these pieces to extract damaged/undamaged buildings using image features such as spectral characters, textures, shapes, and their contexts. The results show a higher-precision classification than conventional methods.

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.

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