Automatic building extraction from high-resolution aerial images and LiDAR data using gated residual refinement network

Huang, Jianfeng; Zhang, Xinchang; Xin, Qinchuan; Sun, Ying; Zhang, Pengcheng

doi:10.1016/j.isprsjprs.2019.02.019

Cited by 184 publications

(122 citation statements)

References 41 publications

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“…Recently, some studies [63][64][65][66] found that the effective fusion of color imagery with elevation (such as DSM) might be helpful to resolving these problems. The elevation data containing the height information of the ground surface make it easy to discriminate the building roofs from impervious surfaces.…”

Section: Limitations Of Deep Learning Models In This Studymentioning

confidence: 99%

Semantic Segmentation of Urban Buildings from VHR Remote Sensing Imagery Using a Deep Convolutional Neural Network

Zhang

et al. 2019

Remote Sensing

172

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Urban building segmentation is a prevalent research domain for very high resolution (VHR) remote sensing; however, various appearances and complicated background of VHR remote sensing imagery make accurate semantic segmentation of urban buildings a challenge in relevant applications. Following the basic architecture of U-Net, an end-to-end deep convolutional neural network (denoted as DeepResUnet) was proposed, which can effectively perform urban building segmentation at pixel scale from VHR imagery and generate accurate segmentation results. The method contains two sub-networks: One is a cascade down-sampling network for extracting feature maps of buildings from the VHR image, and the other is an up-sampling network for reconstructing those extracted feature maps back to the same size of the input VHR image. The deep residual learning approach was adopted to facilitate training in order to alleviate the degradation problem that often occurred in the model training process. The proposed DeepResUnet was tested with aerial images with a spatial resolution of 0.075 m and was compared in performance under the exact same conditions with six other state-of-the-art networks-FCN-8s, SegNet, DeconvNet, U-Net, ResUNet and DeepUNet. Results of extensive experiments indicated that the proposed DeepResUnet outperformed the other six existing networks in semantic segmentation of urban buildings in terms of visual and quantitative evaluation, especially in labeling irregular-shape and small-size buildings with higher accuracy and entirety. Compared with the U-Net, the F1 score, Kappa coefficient and overall accuracy of DeepResUnet were improved by 3.52%, 4.67% and 1.72%, respectively. Moreover, the proposed DeepResUnet required much fewer parameters than the U-Net, highlighting its significant improvement among U-Net applications. Nevertheless, the inference time of DeepResUnet is slightly longer than that of the U-Net, which is subject to further improvement.

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Section: Limitations Of Deep Learning Models In This Studymentioning

confidence: 99%

Semantic Segmentation of Urban Buildings from VHR Remote Sensing Imagery Using a Deep Convolutional Neural Network

Zhang

et al. 2019

Remote Sensing

172

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“…In their review, Ma et al [26] showed that nearly 200 publications using deep convolutional neural networks (CNNs) have been published in the field of remote sensing by early 2019 of which most focused on land use land cover (LULC) classification [28], urban feature extraction [29][30][31], and crop detection [32,33]. Deep learning approaches often require a large amount of training data, and there are benchmark datasets publicly available for training and testing of deep learning approaches in the abovementioned remote sensing fields.…”

Section: Introductionmentioning

confidence: 99%

Deep Learning Approaches for the Mapping of Tree Species Diversity in a Tropical Wetland Using Airborne LiDAR and High-Spatial-Resolution Remote Sensing Images

Sun

Huang

et al. 2019

Forests

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The monitoring of tree species diversity is important for forest or wetland ecosystem service maintenance or resource management. Remote sensing is an efficient alternative to traditional field work to map tree species diversity over large areas. Previous studies have used light detection and ranging (LiDAR) and imaging spectroscopy (hyperspectral or multispectral remote sensing) for species richness prediction. The recent development of very high spatial resolution (VHR) RGB images has enabled detailed characterization of canopies and forest structures. In this study, we developed a three-step workflow for mapping tree species diversity, the aim of which was to increase knowledge of tree species diversity assessment using deep learning in a tropical wetland (Haizhu Wetland) in South China based on VHR-RGB images and LiDAR points. Firstly, individual trees were detected based on a canopy height model (CHM, derived from LiDAR points) by the local-maxima-based method in the FUSION software (Version 3.70, Seattle, USA). Then, tree species at the individual tree level were identified via a patch-based image input method, which cropped the RGB images into small patches (the individually detected trees) based on the tree apexes detected. Three different deep learning methods (i.e., AlexNet, VGG16, and ResNet50) were modified to classify the tree species, as they can make good use of the spatial context information. Finally, four diversity indices, namely, the Margalef richness index, the Shannon–Wiener diversity index, the Simpson diversity index, and the Pielou evenness index, were calculated from the fixed subset with a size of 30 × 30 m for assessment. In the classification phase, VGG16 had the best performance, with an overall accuracy of 73.25% for 18 tree species. Based on the classification results, mapping of tree species diversity showed reasonable agreement with field survey data (R2Margalef = 0.4562, root-mean-square error RMSEMargalef = 0.5629; R2Shannon–Wiener = 0.7948, RMSEShannon–Wiener = 0.7202; R2Simpson = 0.7907, RMSESimpson = 0.1038; and R2Pielou = 0.5875, RMSEPielou = 0.3053). While challenges remain for individual tree detection and species classification, the deep-learning-based solution shows potential for mapping tree species diversity.

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“…The main factors of data source selection for BFE are related to separation between buildings from non-buildings (spatial resolution considerations), the confusing effect of vegetationcover on building detection (spectral resolution considerations), and the shade of building/non-building objects and lighting conditions (types of sensors considerations). The majority of related works that uses the multi-sensor data consist of very high spatial resolution multispectral images and light detection and ranging (LiDAR) data (Huang et al, 2019;Li et al, 2013;Rottensteiner et al, 2003;Volpi and Tuia, 2018;Yang et al, 2018). LiDAR data (also known as point clouds) and digital surface models (DSMs) generated by aerial platform equipped with airborne laser scanning, such as unmanned aerial vehicle or aircraft are applicable for the automatic BFE, because these data provide the geometrical features of buildings shapes (Cai et al, 2019;Jung and Sohn, 2019;Rottensteiner et al, 2007;Sohn and Dowman, 2007).…”

Section: Data Sourcementioning

confidence: 99%

“…No requirement of post-processing Res-U-Net (Xu et al, 2018) -Feature extraction based on several residual blocks -A concatenation with the corresponding block from the encoding part is designed MC-FCN (Wu et al, 2018) -A bottom-up / top-down multi-constraint fully convolutional network -Basic structure based on fusion of U-Net and three extra multi-scale constraintsGRRNet(Huang et al, 2019) …”

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confidence: 99%

Robust Building Footprint Extraction From Big Multi-Sensor Data Using Deep Competition Network

Khoshboresh-Masouleh¹,

Saradjian

2019

Int. Arch. Photogramm. Remote Sens. Spatial Inf. Sci.

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Commission VI, WG VI/4 ABSTRACT:Building footprint extraction (BFE) from multi-sensor data such as optical images and light detection and ranging (LiDAR) point clouds is widely used in various fields of remote sensing applications. However, it is still challenging research topic due to relatively inefficient building extraction techniques from variety of complex scenes in multi-sensor data. In this study, we develop and evaluate a deep competition network (DCN) that fuses very high spatial resolution optical remote sensing images with LiDAR data for robust BFE. DCN is a deep superpixelwise convolutional encoder-decoder architecture using the encoder vector quantization with classified structure. DCN consists of five encoding-decoding blocks with convolutional weights for robust binary representation (superpixel) learning. DCN is trained and tested in a big multi-sensor dataset obtained from the state of Indiana in the United States with multiple building scenes. Comparison results of the accuracy assessment showed that DCN has competitive BFE performance in comparison with other deep semantic binary segmentation architectures. Therefore, we conclude that the proposed model is a suitable solution to the robust BFE from big multi-sensor data.

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Automatic building extraction from high-resolution aerial images and LiDAR data using gated residual refinement network

Cited by 184 publications

References 41 publications

Semantic Segmentation of Urban Buildings from VHR Remote Sensing Imagery Using a Deep Convolutional Neural Network

Semantic Segmentation of Urban Buildings from VHR Remote Sensing Imagery Using a Deep Convolutional Neural Network

Deep Learning Approaches for the Mapping of Tree Species Diversity in a Tropical Wetland Using Airborne LiDAR and High-Spatial-Resolution Remote Sensing Images

Robust Building Footprint Extraction From Big Multi-Sensor Data Using Deep Competition Network

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