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Water-body surveying and mapping is of great significance for water resources utilization, flood monitoring, and environmental protection. However, due to distribution diversities, shape and size variations, and complex scenarios of water-bodies, it is still challengeable to accurately and efficiently extract water-bodies from high-resolution remotely sensed images. In this paper, we propose a multi-scale context extractor network, termed as MSCENet, for delineating water-bodies from high-resolution optical remotely sensed images. The MSCENet mainly contains three key parts: a multi-scale feature encoder, a feature decoder, and a context feature extractor module. To address shape and size variations of water-bodies, the Res2Net is used in the feature encoder to extract rich multi-scale information of water-bodies. The context extractor module is composed of an assorted dilated convolution unit and a complex multi-kernel pooling unit, which further extracts multi-scale contextual information to generate high-level feature maps. The robustness and effectiveness of our MSCENet have been evaluated on two public datasets: LandCover.ai Data Set and DeepGlobe Data Set. Comparative experiments indicate the superiority and applicability of the MSCENet in water-body extraction.
Jian Kang; Haiyan Guan; Daifeng Peng; Ziyi Chen. Multi-scale context extractor network for water-body extraction from high-resolution optical remotely sensed images. International Journal of Applied Earth Observation and Geoinformation 2021, 103, 102499 .
AMA StyleJian Kang, Haiyan Guan, Daifeng Peng, Ziyi Chen. Multi-scale context extractor network for water-body extraction from high-resolution optical remotely sensed images. International Journal of Applied Earth Observation and Geoinformation. 2021; 103 ():102499.
Chicago/Turabian StyleJian Kang; Haiyan Guan; Daifeng Peng; Ziyi Chen. 2021. "Multi-scale context extractor network for water-body extraction from high-resolution optical remotely sensed images." International Journal of Applied Earth Observation and Geoinformation 103, no. : 102499.
Road detection plays an important role in a wide range of applications. However, due to size variations, spectral diversities, occlusions, and complex scenarios, it is still challenging to accurately extract roads from very-high resolution (VHR) optical remote sensing images. This paper proposes a capsule feature pyramid network for extracting road networks from VHR optical images, termed as RoadCapsFPN. By designing a capsule feature pyramid network, the RoadCapsFPN extracts and integrates multiscale capsule features to recover a high-resolution and semantically strong road feature representation. Next, we also design a contextual feature module, including dense atrous convolution (DAC) and residual multi-kernel pooling (RMP) units, to further exploit rich contextual properties of the roads at a high-resolution perspective. Benefitting from the multiscale feature abstraction and context augmentation, our RoadCapsFPN shows impressing results in processing variedly-sized and diversely-spectral roads in complex environments. Two testing datasets, Google and Massichusate Roads Datasets, are used for evaluating the proposed RoadCapsFPN via four testing indicators - precision, recall, intersection-over-union (IoU), and F₁-score. Comparative studies also confirm the superior performance of the RoadCapsFPN in accurately extracting road networks.
Haiyan Guan; Yongtao Yu; Dilong Li; Hanyun Wang. RoadCapsFPN: Capsule Feature Pyramid Network for Road Extraction From VHR Optical Remote Sensing Imagery. IEEE Transactions on Intelligent Transportation Systems 2021, PP, 1 -11.
AMA StyleHaiyan Guan, Yongtao Yu, Dilong Li, Hanyun Wang. RoadCapsFPN: Capsule Feature Pyramid Network for Road Extraction From VHR Optical Remote Sensing Imagery. IEEE Transactions on Intelligent Transportation Systems. 2021; PP (99):1-11.
Chicago/Turabian StyleHaiyan Guan; Yongtao Yu; Dilong Li; Hanyun Wang. 2021. "RoadCapsFPN: Capsule Feature Pyramid Network for Road Extraction From VHR Optical Remote Sensing Imagery." IEEE Transactions on Intelligent Transportation Systems PP, no. 99: 1-11.
Deep learning models have brought great breakthroughs in building extraction from high-resolution optical remote-sensing images. Among recent research, the self-attention module has called up a storm in many fields, including building extraction. However, most current deep learning models loading with the self-attention module still lose sight of the reconstruction bias’s effectiveness. Through tipping the balance between the abilities of encoding and decoding, i.e., making the decoding network be much more complex than the encoding network, the semantic segmentation ability will be reinforced. To remedy the research weakness in combing self-attention and reconstruction-bias modules for building extraction, this paper presents a U-Net architecture that combines self-attention and reconstruction-bias modules. In the encoding part, a self-attention module is added to learn the attention weights of the inputs. Through the self-attention module, the network will pay more attention to positions where there may be salient regions. In the decoding part, multiple large convolutional up-sampling operations are used for increasing the reconstruction ability. We test our model on two open available datasets: the WHU and Massachusetts Building datasets. We achieve IoU scores of 89.39% and 73.49% for the WHU and Massachusetts Building datasets, respectively. Compared with several recently famous semantic segmentation methods and representative building extraction methods, our method’s results are satisfactory.
Ziyi Chen; Dilong Li; Wentao Fan; Haiyan Guan; Cheng Wang; Jonathan Li. Self-Attention in Reconstruction Bias U-Net for Semantic Segmentation of Building Rooftops in Optical Remote Sensing Images. Remote Sensing 2021, 13, 2524 .
AMA StyleZiyi Chen, Dilong Li, Wentao Fan, Haiyan Guan, Cheng Wang, Jonathan Li. Self-Attention in Reconstruction Bias U-Net for Semantic Segmentation of Building Rooftops in Optical Remote Sensing Images. Remote Sensing. 2021; 13 (13):2524.
Chicago/Turabian StyleZiyi Chen; Dilong Li; Wentao Fan; Haiyan Guan; Cheng Wang; Jonathan Li. 2021. "Self-Attention in Reconstruction Bias U-Net for Semantic Segmentation of Building Rooftops in Optical Remote Sensing Images." Remote Sensing 13, no. 13: 2524.
A multispectral light detection and ranging (LiDAR) system, which simultaneously collects spatial geometric data and multi-wavelength intensity information, opens the door to three-dimensional (3-D) point cloud classification and object recognition. Because of the irregular distribution property of point clouds and the massive data volume, point cloud classification directly from multispectral LiDAR data is still challengeable and questionable. In this paper, a point-wise multispectral LiDAR point cloud classification architecture termed as SE-PointNet++ is proposed via integrating a Squeeze-and-Excitation (SE) block with an improved PointNet++ semantic segmentation network. PointNet++ extracts local features from unevenly sampled points and represents local geometrical relationships among the points through multi-scale grouping. The SE block is embedded into PointNet++ to strengthen important channels to increase feature saliency for better point cloud classification. Our SE-PointNet++ architecture has been evaluated on the Titan multispectral LiDAR test datasets and achieved an overall accuracy, a mean Intersection over Union (mIoU), an F1-score, and a Kappa coefficient of 91.16%, 60.15%, 73.14%, and 0.86, respectively. Comparative studies with five established deep learning models confirmed that our proposed SE-PointNet++ achieves promising performance in multispectral LiDAR point cloud classification tasks.
Zhuangwei Jing; Haiyan Guan; Peiran Zhao; Dilong Li; Yongtao Yu; Yufu Zang; Hanyun Wang; Jonathan Li. Multispectral LiDAR Point Cloud Classification Using SE-PointNet++. Remote Sensing 2021, 13, 2516 .
AMA StyleZhuangwei Jing, Haiyan Guan, Peiran Zhao, Dilong Li, Yongtao Yu, Yufu Zang, Hanyun Wang, Jonathan Li. Multispectral LiDAR Point Cloud Classification Using SE-PointNet++. Remote Sensing. 2021; 13 (13):2516.
Chicago/Turabian StyleZhuangwei Jing; Haiyan Guan; Peiran Zhao; Dilong Li; Yongtao Yu; Yufu Zang; Hanyun Wang; Jonathan Li. 2021. "Multispectral LiDAR Point Cloud Classification Using SE-PointNet++." Remote Sensing 13, no. 13: 2516.
The information-accurate road network database is greatly significant and provides essential input to many transportation-related activities. Recently, remote sensing images have been an important data source for assisting rapid road network updating tasks. However, due to the diverse challenging scenarios of roads in remote sensing images, such as occlusions, shadows, material diversities, and topology variations, it is still difficult to realize highly accurate extraction of roads. This paper proposes a novel context-augmentation and self-attention capsule feature pyramid network (CS-CapsFPN) to extract roads from remote sensing images. By designing a capsule feature pyramid network architecture, the proposed CS-CapsFPN can extract and fuze different-level and different-scale high-order capsule features to provide a high-resolution and semantically strong feature representation for predicting the road region maps. By integrating the context-augmentation and self-attention modules, the proposed CS-CapsFPN can exploit multi-scale contextual properties at a high-resolution perspective and emphasize channel-wise informative features to further enhance the feature representation robustness. Quantitative evaluations on two test datasets show that the proposed CS-CapsFPN achieves a competitive performance with a precision, recall, intersection-over-union, and Fscore of 0.9470, 0.9407, 0.8957, and 0.9438, respectively. Comparative studies also confirm the feasibility and superiority of the proposed CS-CapsFPN in road extraction tasks.
Yongtao Yu; Jun Wang; Haiyan Guan; Shenghua Jin; Yongjun Zhang; Changhui Yu; E. Tang; Shaozhang Xiao; Jonathan Li. CS-CapsFPN: A Context-Augmentation and Self-Attention Capsule Feature Pyramid Network for Road Network Extraction from Remote Sensing Imagery. Canadian Journal of Remote Sensing 2021, 1 -19.
AMA StyleYongtao Yu, Jun Wang, Haiyan Guan, Shenghua Jin, Yongjun Zhang, Changhui Yu, E. Tang, Shaozhang Xiao, Jonathan Li. CS-CapsFPN: A Context-Augmentation and Self-Attention Capsule Feature Pyramid Network for Road Network Extraction from Remote Sensing Imagery. Canadian Journal of Remote Sensing. 2021; ():1-19.
Chicago/Turabian StyleYongtao Yu; Jun Wang; Haiyan Guan; Shenghua Jin; Yongjun Zhang; Changhui Yu; E. Tang; Shaozhang Xiao; Jonathan Li. 2021. "CS-CapsFPN: A Context-Augmentation and Self-Attention Capsule Feature Pyramid Network for Road Network Extraction from Remote Sensing Imagery." Canadian Journal of Remote Sensing , no. : 1-19.
Periodically conducting land cover mapping plays a vital role in monitoring the status and changes of the land use. The up-to-date and accurate land use database serves importantly for a wide range of applications. This letter constructs an efficient self-attention capsule network (ESA-CapsNet) for land cover classification of multispectral light detection and ranging (LiDAR) data. First, formulated with a novel capsule encoder-decoder architecture, the ESA-CapsNet performs promisingly in extracting high-level, informative, and strong feature semantics for pixel-wise land cover classification by using the five types of rasterized feature images. Furthermore, designed with a novel capsule-based attention module, the channel and spatial feature encodings are comprehensively exploited to boost the feature saliency and robustness. The ESA-CapsNet is evaluated on two multispectral LiDAR data sets and achieves an advantageous performance with the overall accuracy, average accuracy, and kappa coefficient of over 98.42%, 95.15%, and 0.9776, respectively. Comparative experiments with the existing methods also demonstrate the effectiveness and applicability of the ESA-CapsNet in land cover classification tasks.
Yongtao Yu; Chao Liu; Haiyan Guan; Lanfang Wang; Shangbing Gao; Haiyan Zhang; Yahong Zhang; Jonathan Li. Land Cover Classification of Multispectral LiDAR Data With an Efficient Self-Attention Capsule Network. IEEE Geoscience and Remote Sensing Letters 2021, PP, 1 -5.
AMA StyleYongtao Yu, Chao Liu, Haiyan Guan, Lanfang Wang, Shangbing Gao, Haiyan Zhang, Yahong Zhang, Jonathan Li. Land Cover Classification of Multispectral LiDAR Data With an Efficient Self-Attention Capsule Network. IEEE Geoscience and Remote Sensing Letters. 2021; PP (99):1-5.
Chicago/Turabian StyleYongtao Yu; Chao Liu; Haiyan Guan; Lanfang Wang; Shangbing Gao; Haiyan Zhang; Yahong Zhang; Jonathan Li. 2021. "Land Cover Classification of Multispectral LiDAR Data With an Efficient Self-Attention Capsule Network." IEEE Geoscience and Remote Sensing Letters PP, no. 99: 1-5.
Object detection from remote sensing images serves as an important prerequisite to many applications. However, caused by scale and orientation variations, appearance and distribution diversities, occlusion and shadow contaminations, and complex environmental scenarios of the objects in remote sensing images, it brings great challenges to realize highly accurate recognition of geospatial objects. This paper proposes a novel one-stage anchor-free capsule network (OA-CapsNet) for detecting geospatial objects from remote sensing images. By employing a capsule feature pyramid network architecture as the backbone, a pyramid of high-quality, semantically strong feature representations are generated at multiple scales for object detection. Integrated with two types of capsule feature attention modules, the feature quality is further enhanced by emphasizing channel-wise informative features and class-specific spatial features. By designing a centreness-assisted one-stage anchor-free object detection strategy, the proposed OA-CapsNet performs effectively in recognizing arbitrarily-orientated and diverse-scale geospatial objects. Quantitative evaluations on two large remote sensing datasets show that a competitive overall accuracy with a precision, a recall, and an Fscore of 0.9625, 0.9228, and 0.9423, respectively, is achieved. Comparative studies also confirm the feasibility and superiority of the proposed OA-CapsNet in geospatial object detection tasks.
Yongtao Yu; Junyong Gao; Chao Liu; Haiyan Guan; Dilong Li; Changhui Yu; Shenghua Jin; Fenfen Li; Jonathan Li. OA-CapsNet: A One-Stage Anchor-Free Capsule Network for Geospatial Object Detection from Remote Sensing Imagery. Canadian Journal of Remote Sensing 2021, 1 -14.
AMA StyleYongtao Yu, Junyong Gao, Chao Liu, Haiyan Guan, Dilong Li, Changhui Yu, Shenghua Jin, Fenfen Li, Jonathan Li. OA-CapsNet: A One-Stage Anchor-Free Capsule Network for Geospatial Object Detection from Remote Sensing Imagery. Canadian Journal of Remote Sensing. 2021; ():1-14.
Chicago/Turabian StyleYongtao Yu; Junyong Gao; Chao Liu; Haiyan Guan; Dilong Li; Changhui Yu; Shenghua Jin; Fenfen Li; Jonathan Li. 2021. "OA-CapsNet: A One-Stage Anchor-Free Capsule Network for Geospatial Object Detection from Remote Sensing Imagery." Canadian Journal of Remote Sensing , no. : 1-14.
Periodically monitoring the pavement conditions is of great importance to many intelligent transportation activities. Timely and correctly identifying the distresses or anomalies on pavement surfaces can help to smooth traffic flows and avoid potential threats to pavement securities. In this paper, we develop a novel context-augmented capsule feature pyramid network (CCapFPN) to detect cracks from pavement images. The CCapFPN adopts vectorial capsules to represent high-level, intrinsic, and salient features of cracks. By designing a feature pyramid architecture, the CCapFPN can fuse different levels and different scales of capsule features to provide a high-resolution, semantically strong feature representation for accurate crack detection. To take advantage of the context properties, a context-augmented module is embedded into each stage of the CCapFPN to rapidly enlarge the receptive field. The CCapFPN performs effectively and efficiently in processing pavement images of diverse conditions and detecting cracks of different topologies. Quantitative evaluations show that an overall performance with a precision, a recall, and an F-score of 0.9200, 0.9149, and 0.9174, respectively, were achieved on the test datasets. Comparative studies with some existing deep learning and edge based crack detection methods also confirm the superior performance of the CCapFPN in crack detection tasks.
Yongtao Yu; Haiyan Guan; Dilong Li; Yongjun Zhang; Shenghua Jin; Changhui Yu. CCapFPN: A Context-Augmented Capsule Feature Pyramid Network for Pavement Crack Detection. IEEE Transactions on Intelligent Transportation Systems 2021, PP, 1 -12.
AMA StyleYongtao Yu, Haiyan Guan, Dilong Li, Yongjun Zhang, Shenghua Jin, Changhui Yu. CCapFPN: A Context-Augmented Capsule Feature Pyramid Network for Pavement Crack Detection. IEEE Transactions on Intelligent Transportation Systems. 2021; PP (99):1-12.
Chicago/Turabian StyleYongtao Yu; Haiyan Guan; Dilong Li; Yongjun Zhang; Shenghua Jin; Changhui Yu. 2021. "CCapFPN: A Context-Augmented Capsule Feature Pyramid Network for Pavement Crack Detection." IEEE Transactions on Intelligent Transportation Systems PP, no. 99: 1-12.
Timely and accurately measuring surface water bodies and monitoring their conditions and changes are greatly important to a wide range of environmental and social activities. Recently, with the development of optical remote sensing sensors in resolutions and qualities, as well as the convenience in data acquisition, remote sensing images have become an important data source for assisting water body measurements. However, due to the considerable variations of water bodies in shapes, areas, and sizes, the diversities of colour appearances, and the complicated surface and surrounding scenarios, it is still challenging to automatically and accurately extract water bodies from remote sensing images. In this paper, we develop a novel self-attention capsule feature pyramid network (SA-CapsFPN) to extract water bodies from remote sensing images. By designing a deep capsule feature pyramid architecture, the SA-CapsFPN can extract and fuse multi-level and multiscale high-order capsule features to provide a high-resolution, semantically strong feature encoding for improving pixel-wise water body extraction accuracy. With the integration of the context-augmentation and self-attention modules, the SA-CapsFPN can exploit multiscale contextual properties and emphasize channel-wise informative features, thereby enhancing the feature representation capability. The SA-CapsFPN performs superiorly in extracting water bodies of varying shapes, areas, and sizes, as well as diverse surface and environmental scenarios. Quantitative evaluations on two big remote sensing image datasets show that an overall performance with a P, an R, and an F score of 0.9771, 0.9684, and 0.9727, respectively, are achieved. Comparative studies with five deep learning based methods also demonstrate the applicability and superiority of the SA-CapsFPN in water body extraction tasks.
Yongtao Yu; Yuting Yao; Haiyan Guan; Dilong Li; Zuojun Liu; Lanfang Wang; Changhui Yu; Shaozhang Xiao; Wenhao Wang; Lv Chang. A self-attention capsule feature pyramid network for water body extraction from remote sensing imagery. International Journal of Remote Sensing 2020, 42, 1801 -1822.
AMA StyleYongtao Yu, Yuting Yao, Haiyan Guan, Dilong Li, Zuojun Liu, Lanfang Wang, Changhui Yu, Shaozhang Xiao, Wenhao Wang, Lv Chang. A self-attention capsule feature pyramid network for water body extraction from remote sensing imagery. International Journal of Remote Sensing. 2020; 42 (5):1801-1822.
Chicago/Turabian StyleYongtao Yu; Yuting Yao; Haiyan Guan; Dilong Li; Zuojun Liu; Lanfang Wang; Changhui Yu; Shaozhang Xiao; Wenhao Wang; Lv Chang. 2020. "A self-attention capsule feature pyramid network for water body extraction from remote sensing imagery." International Journal of Remote Sensing 42, no. 5: 1801-1822.
Building extraction has attracted much attentions for decades as a prerequisite for many applications and is still a challenging topic in the field of photogrammetry and remote sensing. Due to the lack of spectral information, massive data processing, and approach universality, building extraction from point clouds is still a thorny and challenging problem. In this paper, a novel deep-learning-based framework is proposed for building extraction from point cloud data. Specifically, first, a sample generation method is proposed to split the raw preprocessed multi-spectral light detection and ranging (LiDAR) data into numerous samples, which are directly fed into convolutional neural networks and completely cover the original inputs. Then, a graph geometric moments (GGM) convolution is proposed to encode the local geometric structure of point sets. In addition, a hierarchical architecture equipped with GGM convolution, called GGM convolutional neural networks, is proposed to train and recognize building points. Finally, the test scenes with varying sizes can be fed into the framework and obtain a point-wise extraction result. We evaluate the proposed framework and methods on the airborne multi-spectral LiDAR point clouds collected by an Optech Titan system. Compared with previous state-of-the-art networks, which are designed for point cloud segmentation, our method achieves the best performance with a correctness of 95.1%, a completeness of 93.7%, an F-measure of 94.4%, and an intersection over union (IoU) of 89.5% on two test areas. The experimental results confirm the effectiveness and efficiency of the proposed framework and methods.
Dilong Li; Xin Shen; Yongtao Yu; Haiyan Guan; Jonathan Li; Guo Zhang; Deren Li. Building Extraction from Airborne Multi-Spectral LiDAR Point Clouds Based on Graph Geometric Moments Convolutional Neural Networks. Remote Sensing 2020, 12, 3186 .
AMA StyleDilong Li, Xin Shen, Yongtao Yu, Haiyan Guan, Jonathan Li, Guo Zhang, Deren Li. Building Extraction from Airborne Multi-Spectral LiDAR Point Clouds Based on Graph Geometric Moments Convolutional Neural Networks. Remote Sensing. 2020; 12 (19):3186.
Chicago/Turabian StyleDilong Li; Xin Shen; Yongtao Yu; Haiyan Guan; Jonathan Li; Guo Zhang; Deren Li. 2020. "Building Extraction from Airborne Multi-Spectral LiDAR Point Clouds Based on Graph Geometric Moments Convolutional Neural Networks." Remote Sensing 12, no. 19: 3186.
The up-to-date and information-accurate road database plays a significant role in many applications. Recently, with the improvement in image resolutions and quality, remote sensing images have provided an important data source for road extraction tasks. However, due to the topology variations, spectral diversities, and complex scenarios, it is still challenging to realize fully automated and highly accurate road extractions from remote sensing images. This paper proposes a novel dual-attention capsule U-Net (DA-CapsUNet) for road region extraction by combining the advantageous properties of capsule representations and the powerful features of attention mechanisms. By constructing a capsule U-Net architecture, the DA-CapsUNet can extract and fuse multiscale capsule features to recover a high-resolution and semantically strong feature representation. By designing the multiscale context-augmentation and two types of feature attention modules, the DA-CapsUNet can exploit multiscale contextual properties at a high-resolution perspective and generate an informative and class-specific feature encoding. Quantitative evaluations on a large dataset showed that the DA-CapsUNet provides a competitive road extraction performance with a precision of 0.9523, a recall of 0.9486, and an F-score of 0.9504, respectively. Comparative studies with eight recently developed deep learning methods also confirmed the applicability and superiority or compatibility of the DA-CapsUNet in road extraction tasks.
Yongfeng Ren; Yongtao Yu; Haiyan Guan. DA-CapsUNet: A Dual-Attention Capsule U-Net for Road Extraction from Remote Sensing Imagery. Remote Sensing 2020, 12, 2866 .
AMA StyleYongfeng Ren, Yongtao Yu, Haiyan Guan. DA-CapsUNet: A Dual-Attention Capsule U-Net for Road Extraction from Remote Sensing Imagery. Remote Sensing. 2020; 12 (18):2866.
Chicago/Turabian StyleYongfeng Ren; Yongtao Yu; Haiyan Guan. 2020. "DA-CapsUNet: A Dual-Attention Capsule U-Net for Road Extraction from Remote Sensing Imagery." Remote Sensing 12, no. 18: 2866.
Change detection (CD) is one of the main applications of remote sensing. With the increasing popularity of deep learning, most recent developments of CD methods have introduced the use of deep learning techniques to increase the accuracy and automation level over traditional methods. However, when using supervised CD methods, a large amount of labeled data is needed to train deep convolutional networks with millions of parameters. These labeled data are difficult to acquire for CD tasks. To address this limitation, a novel semisupervised convolutional network for CD (SemiCDNet) is proposed based on a generative adversarial network (GAN). First, both the labeled data and unlabeled data are input into the segmentation network to produce initial predictions and entropy maps. Then, to exploit the potential of unlabeled data, two discriminators are adopted to enforce the feature distribution consistency of segmentation maps and entropy maps between the labeled and unlabeled data. During the competitive training, the generator is continuously regularized by utilizing the unlabeled information, thus improving its generalization capability. The effectiveness and reliability of our proposed method are verified on two high-resolution remote sensing data sets. Extensive experimental results demonstrate the superiority of the proposed method against other state-of-the-art approaches.
Daifeng Peng; Lorenzo Bruzzone; Yongjun Zhang; Haiyan Guan; Haiyong Ding; Xu Huang. SemiCDNet: A Semisupervised Convolutional Neural Network for Change Detection in High Resolution Remote-Sensing Images. IEEE Transactions on Geoscience and Remote Sensing 2020, 59, 5891 -5906.
AMA StyleDaifeng Peng, Lorenzo Bruzzone, Yongjun Zhang, Haiyan Guan, Haiyong Ding, Xu Huang. SemiCDNet: A Semisupervised Convolutional Neural Network for Change Detection in High Resolution Remote-Sensing Images. IEEE Transactions on Geoscience and Remote Sensing. 2020; 59 (7):5891-5906.
Chicago/Turabian StyleDaifeng Peng; Lorenzo Bruzzone; Yongjun Zhang; Haiyan Guan; Haiyong Ding; Xu Huang. 2020. "SemiCDNet: A Semisupervised Convolutional Neural Network for Change Detection in High Resolution Remote-Sensing Images." IEEE Transactions on Geoscience and Remote Sensing 59, no. 7: 5891-5906.
Geometric feature acts as an important role in point cloud shape classification tasks. Previous methods have proved that the geometric information of point clouds effectively improves the classification accuracy. Mo-Net firstly introduced geometric moments into point cloud shape classification, which, to fit the form of second order geometric moments, extends the number of input channels from three to nine. Unfortunately, similar to PointNet, Mo-Net cannot capture the local structures. To address this issue, we propose a graph geometric moments convolution neural network (GGM-Net), which learns local geometric features from geometric moments representation of a local point set. The core module of the GGM-Net is to learn features from geometric moments (termed as GGM convolution). Specifically, the GGM convolution learns point features and local features from the first and second order geometric moments of points and its local neighbors, respectively, and then combines these features by using an addition operation. In this way, a geometrical local representation about points is obtained, which leads to much surface geometry awareness and robustness. Equipped with the GGM convolution, GGM-Net, a simple end-to-end architecture, is developed to achieve a competitive accuracy on the benchmark dataset ModelNet40 and perform more efficiently in terms of memory and computational complexity.
Dilong Li; Xin Shen; Yongtao Yu; Haiyan Guan; Hanyun Wang; Deren Li. GGM-Net: Graph Geometric Moments Convolution Neural Network for Point Cloud Shape Classification. IEEE Access 2020, 8, 124989 -124998.
AMA StyleDilong Li, Xin Shen, Yongtao Yu, Haiyan Guan, Hanyun Wang, Deren Li. GGM-Net: Graph Geometric Moments Convolution Neural Network for Point Cloud Shape Classification. IEEE Access. 2020; 8 ():124989-124998.
Chicago/Turabian StyleDilong Li; Xin Shen; Yongtao Yu; Haiyan Guan; Hanyun Wang; Deren Li. 2020. "GGM-Net: Graph Geometric Moments Convolution Neural Network for Point Cloud Shape Classification." IEEE Access 8, no. : 124989-124998.
Multispectral LiDAR (Light Detection And Ranging) is characterized of the completeness and consistency of its spectrum and spatial geometric data, which provides a new data source for land-cover classification. In recent years, the convolutional neural network (CNN), compared with traditional machine learning methods, has made a series of breakthroughs in image classification, object detection, and image semantic segmentation due to its stronger feature learning and feature expression abilities. However, traditional CNN models suffer from some issues, such as a large number of layers, leading to higher computational cost. To address this problem, we propose a CNN-based multi-spectral LiDAR land-cover classification framework and analyze its optimal parameters to improve classification accuracy. This framework starts with the preprocessing of multi-spectral 3D LiDAR data into 2D images. Next, a CNN model is constructed with seven fundamental functional layers, and its hyper-parameters are comprehensively discussed and optimized. The constructed CNN model with the optimized hyper-parameters was tested on the Titan multi-spectral LiDAR data, which include three wavelengths of 532 nm, 1064 nm, and 1550 nm. Extensive experiments demonstrated that the constructed CNN with the optimized hyper-parameters is feasible for multi-spectral LiDAR land-cover classification tasks. Compared with the classical CNN models (i.e., AlexNet, VGG16 and ResNet50) and our previous studies, our constructed CNN model with the optimized hyper-parameters is superior in computational performance and classification accuracies.
Suoyan Pan; Haiyan Guan; Yating Chen; Yongtao Yu; Wesley Nunes Gonçalves; José Marcato Junior; Jonathan Li. Land-cover classification of multispectral LiDAR data using CNN with optimized hyper-parameters. ISPRS Journal of Photogrammetry and Remote Sensing 2020, 166, 241 -254.
AMA StyleSuoyan Pan, Haiyan Guan, Yating Chen, Yongtao Yu, Wesley Nunes Gonçalves, José Marcato Junior, Jonathan Li. Land-cover classification of multispectral LiDAR data using CNN with optimized hyper-parameters. ISPRS Journal of Photogrammetry and Remote Sensing. 2020; 166 ():241-254.
Chicago/Turabian StyleSuoyan Pan; Haiyan Guan; Yating Chen; Yongtao Yu; Wesley Nunes Gonçalves; José Marcato Junior; Jonathan Li. 2020. "Land-cover classification of multispectral LiDAR data using CNN with optimized hyper-parameters." ISPRS Journal of Photogrammetry and Remote Sensing 166, no. : 241-254.
Building footprint extraction plays an important role in a wide range of applications. However, due to size and shape diversities, occlusions, and complex scenarios, it is still challenging to accurately extract building footprints from aerial images. This letter proposes a capsule feature pyramid network (CapFPN) for building footprint extraction from aerial images. Taking advantage of the properties of capsules and fusing different levels of capsule features, the CapFPN can extract high-resolution, intrinsic, and semantically strong features, which perform effectively in improving the pixel-wise building footprint extraction accuracy. With the use of signed distance maps as ground truths, the CapFPN can extract solid building regions free of tiny holes. Quantitative evaluations on an aerial image data set show that a precision, recall, intersection-over-union (IoU), and F-score of 0.928, 0.914, 0.853, and 0.921, respectively, are obtained. Comparative studies with six existing methods confirm the superior performance of the CapFPN in accurately extracting building footprints.
Yongtao Yu; Yongfeng Ren; Haiyan Guan; Dilong Li; Changhui Yu; Shenghua Jin; Lanfang Wang. Capsule Feature Pyramid Network for Building Footprint Extraction From High-Resolution Aerial Imagery. IEEE Geoscience and Remote Sensing Letters 2020, 18, 895 -899.
AMA StyleYongtao Yu, Yongfeng Ren, Haiyan Guan, Dilong Li, Changhui Yu, Shenghua Jin, Lanfang Wang. Capsule Feature Pyramid Network for Building Footprint Extraction From High-Resolution Aerial Imagery. IEEE Geoscience and Remote Sensing Letters. 2020; 18 (5):895-899.
Chicago/Turabian StyleYongtao Yu; Yongfeng Ren; Haiyan Guan; Dilong Li; Changhui Yu; Shenghua Jin; Lanfang Wang. 2020. "Capsule Feature Pyramid Network for Building Footprint Extraction From High-Resolution Aerial Imagery." IEEE Geoscience and Remote Sensing Letters 18, no. 5: 895-899.
Unmanned aerial vehicles using light detection and ranging (UAV LiDAR) with high spatial resolution have shown great potential in forest applications because they can capture vertical structures of forests. Individual tree segmentation is the foundation of many forest research works and applications. The tradition fixed bandwidth mean shift has been applied to individual tree segmentation and proved to be robust in tree segmentation. However, the fixed bandwidth-based segmentation methods are not suitable for various crown sizes, resulting in omission or commission errors. Therefore, to increase tree-segmentation accuracy, we propose a self-adaptive bandwidth estimation method to estimate the optimal kernel bandwidth automatically without any prior knowledge of crown size. First, from the global maximum point, we divide the three-dimensional (3D) space into a set of angular sectors, for each of which a canopy surface is simulated and the potential tree crown boundaries are identified to estimate average crown width as the kernel bandwidth. Afterwards, we use a mean shift with the automatically estimated kernel bandwidth to extract individual tree points. The method is iteratively implemented within a given area until all trees are segmented. The proposed method was tested on the 7 plots acquired by a Velodyne 16E LiDAR system, including 3 simple plots and 4 complex plots, and 95% and 80% of trees were correctly segmented, respectively. Comparative experiments show that our method contributes to the improvement of both segmentation accuracy and computational efficiency.
Wanqian Yan; Haiyan Guan; Lin Cao; Wilson Yu; Cheng Li; Jianyong Lu. A Self-Adaptive Mean Shift Tree-Segmentation Method Using UAV LiDAR Data. Remote Sensing 2020, 12, 515 .
AMA StyleWanqian Yan, Haiyan Guan, Lin Cao, Wilson Yu, Cheng Li, Jianyong Lu. A Self-Adaptive Mean Shift Tree-Segmentation Method Using UAV LiDAR Data. Remote Sensing. 2020; 12 (3):515.
Chicago/Turabian StyleWanqian Yan; Haiyan Guan; Lin Cao; Wilson Yu; Cheng Li; Jianyong Lu. 2020. "A Self-Adaptive Mean Shift Tree-Segmentation Method Using UAV LiDAR Data." Remote Sensing 12, no. 3: 515.
Object detection from remote sensing imagery plays a significant role in a wide range of applications, including urban planning, intelligent transportation systems, ecology and environment analysis, etc. However, scale variations, orientation variations, illumination changes, and partial occlusions, as well as image qualities, bring great challenges for accurate geospatial object detection. In this paper, we propose an efficient orientation guided anchoring based geospatial object detection network based on convolutional neural networks. To handle objects of varying sizes, the feature extraction subnetwork extracts a pyramid of semantically strong features at different scales. Based on orientation guided anchoring, the anchor generation subnetwork generates a small set of high-quality, oriented anchors as object proposals. After orientation region of interest pooling, objects of interest are detected from the object proposals through the object detection subnetwork. The proposed method has been tested on a large geospatial object detection dataset. Quantitative evaluations show that an overall completeness, correctness, quality, and F1-measure of 0.9232, 0.9648, 0.8931, and 0.9435, respectively, are obtained. In addition, the proposed method achieves a processing speed of 8 images per second on a GPU on the cloud computing platform. Comparative studies with the existing object detection methods also demonstrate the advantageous detection accuracy and computational efficiency of our proposed method.
Yongtao Yu; Haiyan Guan; Dilong Li; Tiannan Gu; E. Tang; Aixia Li. Orientation guided anchoring for geospatial object detection from remote sensing imagery. ISPRS Journal of Photogrammetry and Remote Sensing 2019, 160, 67 -82.
AMA StyleYongtao Yu, Haiyan Guan, Dilong Li, Tiannan Gu, E. Tang, Aixia Li. Orientation guided anchoring for geospatial object detection from remote sensing imagery. ISPRS Journal of Photogrammetry and Remote Sensing. 2019; 160 ():67-82.
Chicago/Turabian StyleYongtao Yu; Haiyan Guan; Dilong Li; Tiannan Gu; E. Tang; Aixia Li. 2019. "Orientation guided anchoring for geospatial object detection from remote sensing imagery." ISPRS Journal of Photogrammetry and Remote Sensing 160, no. : 67-82.
Probabilistic registration algorithms [e.g., coherent point drift, (CPD)] provide effective solutions for point cloud alignment. However, using the original CPD algorithm for automatic registration of terrestrial laser scanner (TLS) point clouds is highly challenging because of density variations caused by scanning acquisition geometry. In this letter, we propose a new global registration method, introducing the use of the CPD framework for TLS point clouds. We first consider the measurement geometry and the intrinsic characteristics of the scene to simplify points. In addition to the Euclidean distance, we incorporate geometric information as well as structural constraints in the probabilistic model to optimize the so-called matching probability matrix. Among the structural constraints, we use a spectral graph to measure the structural similarity between matches at each iteration. The method is tested on three data sets collected by different TLS scanners. Experimental results demonstrate that the proposed method is robust to density variations and can decrease iterations effectively. The average registration errors of the three data sets are 0.05, 0.12, and 0.08 m, respectively. It is also shown that our registration framework is superior to the state-of-the-art methods in terms of both registration errors and efficiency. The experiments demonstrate the effectiveness and efficiency of the proposed probabilistic global registration.
Yufu Zang; Roderik Lindenbergh; Bisheng Yang; Haiyan Guan. Density-Adaptive and Geometry-Aware Registration of TLS Point Clouds Based on Coherent Point Drift. IEEE Geoscience and Remote Sensing Letters 2019, 17, 1628 -1632.
AMA StyleYufu Zang, Roderik Lindenbergh, Bisheng Yang, Haiyan Guan. Density-Adaptive and Geometry-Aware Registration of TLS Point Clouds Based on Coherent Point Drift. IEEE Geoscience and Remote Sensing Letters. 2019; 17 (9):1628-1632.
Chicago/Turabian StyleYufu Zang; Roderik Lindenbergh; Bisheng Yang; Haiyan Guan. 2019. "Density-Adaptive and Geometry-Aware Registration of TLS Point Clouds Based on Coherent Point Drift." IEEE Geoscience and Remote Sensing Letters 17, no. 9: 1628-1632.
Traffic-sign recognition plays an important role in road transportation systems. This letter presents a novel two-stage method for detecting and recognizing traffic signs from mobile Light Detection and Ranging (LiDAR) point clouds and digital images. First, traffic signs are detected from mobile LiDAR point cloud data according to their geometrical and spectral properties, which have been fully studied in our previous work. Afterward, the traffic-sign patches are obtained by projecting the detected points onto the registered digital images. To improve the performance of traffic-sign recognition, we apply a convolutional capsule network to the traffic-sign patches to classify them into different types. We have evaluated the proposed framework on data sets acquired by a RIEGL VMX-450 system. Quantitative evaluations show that a recognition rate of 0.957 is achieved. Comparative studies with the convolutional neural network (CNN) and our previous supervised Gaussian-Bernoulli deep Boltzmann machine (GB-DBM) classifier also confirm that the proposed method performs effectively and robustly in recognizing traffic signs of various types and conditions.
Haiyan Guan; Yongtao Yu; Daifeng Peng; Yufu Zang; Jianyong Lu; Aixia Li; Jonathan Li. A Convolutional Capsule Network for Traffic-Sign Recognition Using Mobile LiDAR Data With Digital Images. IEEE Geoscience and Remote Sensing Letters 2019, 17, 1067 -1071.
AMA StyleHaiyan Guan, Yongtao Yu, Daifeng Peng, Yufu Zang, Jianyong Lu, Aixia Li, Jonathan Li. A Convolutional Capsule Network for Traffic-Sign Recognition Using Mobile LiDAR Data With Digital Images. IEEE Geoscience and Remote Sensing Letters. 2019; 17 (6):1067-1071.
Chicago/Turabian StyleHaiyan Guan; Yongtao Yu; Daifeng Peng; Yufu Zang; Jianyong Lu; Aixia Li; Jonathan Li. 2019. "A Convolutional Capsule Network for Traffic-Sign Recognition Using Mobile LiDAR Data With Digital Images." IEEE Geoscience and Remote Sensing Letters 17, no. 6: 1067-1071.
Land cover mapping is an effective way to quantify land resources and monitor their changes. It plays an important role in a wide range of applications. This letter proposes a hybrid capsule network for land cover classification using multispectral light detection and ranging (LiDAR) data. First, the multispectral LiDAR data were rasterized into a set of feature images to exploit the geometrical and spectral properties of different types of land covers. Then, a hybrid capsule network composed of an encoder network and a decoder network is trained to extract both high-level local and global entity-oriented capsule features for accurate land cover classification. Quantitative classification evaluations on two data sets show that the overall accuracy, average accuracy, and kappa coefficient of over 97.89%, 94.54%, and 0.9713, respectively, are obtained. Comparative studies with five existing methods confirm that the proposed method performs robustly and accurately in land cover classification using the multispectral LiDAR data.
Yongtao Yu; Haiyan Guan; Dilong Li; Tiannan Gu; Lanfang Wang; Lingfei Ma; Jonathan Li. A Hybrid Capsule Network for Land Cover Classification Using Multispectral LiDAR Data. IEEE Geoscience and Remote Sensing Letters 2019, 17, 1263 -1267.
AMA StyleYongtao Yu, Haiyan Guan, Dilong Li, Tiannan Gu, Lanfang Wang, Lingfei Ma, Jonathan Li. A Hybrid Capsule Network for Land Cover Classification Using Multispectral LiDAR Data. IEEE Geoscience and Remote Sensing Letters. 2019; 17 (7):1263-1267.
Chicago/Turabian StyleYongtao Yu; Haiyan Guan; Dilong Li; Tiannan Gu; Lanfang Wang; Lingfei Ma; Jonathan Li. 2019. "A Hybrid Capsule Network for Land Cover Classification Using Multispectral LiDAR Data." IEEE Geoscience and Remote Sensing Letters 17, no. 7: 1263-1267.