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With the continuing improvement of remote-sensing (RS) sensors, it is crucial to monitor Earth surface changes at fine scale and in great detail. Thus, semantic change detection (SCD), which is capable of locating and identifying “from-to” change information simultaneously, is gaining growing attention in RS community. However, due to the limitation of large-scale SCD datasets, most existing SCD methods are focused on scene-level changes, where semantic change maps are generated with only coarse boundary or scarce category information. To address this issue, we propose a novel convolutional network for large-scale SCD (SCDNet). It is based on a Siamese UNet architecture, which consists of two encoders and two decoders with shared weights. First, multi-temporal images are given as input to the encoders to extract multi-scale deep representations. A multi-scale atrous convolution (MAC) unit is inserted at the end of the encoders to enlarge the receptive field as well as capturing multi-scale information. Then, difference feature maps are generated for each scale, which are combined with feature maps from the encoders to serve as inputs for the decoders. Attention mechanism and deep supervision strategy are further introduced to improve network performance. Finally, we utilize softmax layer to produce a semantic change map for each time image. Extensive experiments are carried out on two large-scale high-resolution SCD datasets, which demonstrates the effectiveness and superiority of the proposed method.
Daifeng Peng; Lorenzo Bruzzone; Yongjun Zhang; Haiyan Guan; Pengfei He. SCDNET: A novel convolutional network for semantic change detection in high resolution optical remote sensing imagery. International Journal of Applied Earth Observation and Geoinformation 2021, 103, 102465 .
AMA StyleDaifeng Peng, Lorenzo Bruzzone, Yongjun Zhang, Haiyan Guan, Pengfei He. SCDNET: A novel convolutional network for semantic change detection in high resolution optical remote sensing imagery. International Journal of Applied Earth Observation and Geoinformation. 2021; 103 ():102465.
Chicago/Turabian StyleDaifeng Peng; Lorenzo Bruzzone; Yongjun Zhang; Haiyan Guan; Pengfei He. 2021. "SCDNET: A novel convolutional network for semantic change detection in high resolution optical remote sensing imagery." International Journal of Applied Earth Observation and Geoinformation 103, no. : 102465.
Change detection plays a crucial role in observing earth surface transition and has been widely investigated using deep learning methods. However, the current deep learning methods for pixel-wise change detection still suffer from limited accuracy, mainly due to their insufficient feature extraction and context aggregation. To address this limitation, we propose a novel Cross Layer convolutional neural Network (CLNet) in this paper, where the UNet structure is used as the backbone and newly designed Cross Layer Blocks (CLBs) are embedded to incorporate the multi-scale features and multi-level context information. The designed CLB starts with one input and then split into two parallel but asymmetric branches, which are leveraged to extract the multi-scale features by using different strides; and the feature maps, which come from the opposite branches but have the same size, are concatenated to incorporate multi-level context information. The designed CLBs aggregate the multi-scale features and multi-level context information so that the proposed CLNet can reuse extracted feature information and capture accurate pixel-wise change in complex scenes. Quantitative and qualitative experiments were conducted on a public very-high-resolution satellite image dataset (VHR-Dataset), a newly released building change detection dataset (LEVIR-CD Dataset) and an aerial building change detection dataset (WHU Building Dataset). The CLNet reached an F1-score of 0.921 and an overall accuracy of 98.1% with the VHR-Dataset, an F1-score of 0.900 and an overall accuracy of 98.9% with the LEVIR-CD Dataset, and an F1-score of 0.963 and an overall accuracy of 99.7% with the WHU Building Dataset. The experimental results with all the selected datasets showed that the proposed CLNet outperformed several state-of-the-art (SOTA) methods and achieved competitive accuracy and efficiency trade-offs. The code of CLNet will be released soon at: https://skyearth.org/publication/project/CLNet.
Zhi Zheng; Yi Wan; Yongjun Zhang; Sizhe Xiang; Daifeng Peng; Bin Zhang. CLNet: Cross-layer convolutional neural network for change detection in optical remote sensing imagery. ISPRS Journal of Photogrammetry and Remote Sensing 2021, 175, 247 -267.
AMA StyleZhi Zheng, Yi Wan, Yongjun Zhang, Sizhe Xiang, Daifeng Peng, Bin Zhang. CLNet: Cross-layer convolutional neural network for change detection in optical remote sensing imagery. ISPRS Journal of Photogrammetry and Remote Sensing. 2021; 175 ():247-267.
Chicago/Turabian StyleZhi Zheng; Yi Wan; Yongjun Zhang; Sizhe Xiang; Daifeng Peng; Bin Zhang. 2021. "CLNet: Cross-layer convolutional neural network for change detection in optical remote sensing imagery." ISPRS Journal of Photogrammetry and Remote Sensing 175, no. : 247-267.
Feature matching is to detect and match corresponding feature points in stereo pairs, which is one of the key techniques in accurate camera orientations. However, several factors limit the feature matching accuracy, e.g., image textures, viewing angles of stereo cameras, and resolutions of stereo pairs. To improve the feature matching accuracy against these limiting factors, this paper imposes spatial smoothness constraints over the whole feature point sets with the underlying assumption that feature points should have similar matching results with their surrounding high-confidence points and proposes a robust feature matching method with the spatial smoothness constraints (RMSS). The core algorithm constructs a graph structure from the feature point sets and then formulates the feature matching problem as the optimization of a global energy function with first-order, spatial smoothness constraints based on the graph. For computational purposes, the global optimization of the energy function is then broken into sub-optimizations of each feature point, and an approximate solution of the energy function is iteratively derived as the matching results of the whole feature point sets. Experiments on close-range datasets with some above limiting factors show that the proposed method was capable of greatly improving the matching robustness and matching accuracy of some feature descriptors (e.g., scale-invariant feature transform (SIFT) and Speeded Up Robust Features (SURF)). After the optimization of the proposed method, the inlier number of SIFT and SURF was increased by average 131.9% and 113.5%, the inlier percentages between the inlier number and the total matches number of SIFT and SURF were increased by average 259.0% and 307.2%, and the absolute matching accuracy of SIFT and SURF was improved by average 80.6% and 70.2%.
Xu Huang; Xue Wan; Daifeng Peng. Robust Feature Matching with Spatial Smoothness Constraints. Remote Sensing 2020, 12, 3158 .
AMA StyleXu Huang, Xue Wan, Daifeng Peng. Robust Feature Matching with Spatial Smoothness Constraints. Remote Sensing. 2020; 12 (19):3158.
Chicago/Turabian StyleXu Huang; Xue Wan; Daifeng Peng. 2020. "Robust Feature Matching with Spatial Smoothness Constraints." Remote Sensing 12, no. 19: 3158.
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.
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.
Vehicle detection from remote sensing images plays a significant role in transportation related applications. However, the scale variations, orientation variations, illumination variations, and partial occlusions of vehicles, as well as the image qualities, bring great challenges for accurate vehicle detection. In this paper, we present an affine-function transformation-based object matching framework for vehicle detection from unmanned aerial vehicle (UAV) images. First, meaningful and non-redundant patches are generated through a superpixel segmentation strategy. Then, the affine-function transformation-based object matching framework is applied to a vehicle template and each of the patches for vehicle existence estimation. Finally, vehicles are detected and located after matching cost thresholding, vehicle location estimation, and multiple response elimination. Quantitative evaluations on two UAV image datasets show that the proposed method achieves an average completeness, correctness, quality, and F1-measure of 0.909, 0.969, 0.883, and 0.938, respectively. Comparative studies also demonstrate that the proposed method achieves compatible performance with the Faster R-CNN and outperforms the other eight existing methods in accurately detecting vehicles of various conditions.
Shuang Cao; Yongtao Yu; Haiyan Guan; Daifeng Peng; Wanqian Yan. Affine-Function Transformation-Based Object Matching for Vehicle Detection from Unmanned Aerial Vehicle Imagery. Remote Sensing 2019, 11, 1708 .
AMA StyleShuang Cao, Yongtao Yu, Haiyan Guan, Daifeng Peng, Wanqian Yan. Affine-Function Transformation-Based Object Matching for Vehicle Detection from Unmanned Aerial Vehicle Imagery. Remote Sensing. 2019; 11 (14):1708.
Chicago/Turabian StyleShuang Cao; Yongtao Yu; Haiyan Guan; Daifeng Peng; Wanqian Yan. 2019. "Affine-Function Transformation-Based Object Matching for Vehicle Detection from Unmanned Aerial Vehicle Imagery." Remote Sensing 11, no. 14: 1708.
Change detection (CD) is essential to the accurate understanding of land surface changes using available Earth observation data. Due to the great advantages in deep feature representation and nonlinear problem modeling, deep learning is becoming increasingly popular to solve CD tasks in remote-sensing community. However, most existing deep learning-based CD methods are implemented by either generating difference images using deep features or learning change relations between pixel patches, which leads to error accumulation problems since many intermediate processing steps are needed to obtain final change maps. To address the above-mentioned issues, a novel end-to-end CD method is proposed based on an effective encoder-decoder architecture for semantic segmentation named UNet++, where change maps could be learned from scratch using available annotated datasets. Firstly, co-registered image pairs are concatenated as an input for the improved UNet++ network, where both global and fine-grained information can be utilized to generate feature maps with high spatial accuracy. Then, the fusion strategy of multiple side outputs is adopted to combine change maps from different semantic levels, thereby generating a final change map with high accuracy. The effectiveness and reliability of our proposed CD method are verified on very-high-resolution (VHR) satellite image datasets. Extensive experimental results have shown that our proposed approach outperforms the other state-of-the-art CD methods.
Daifeng Peng; Yongjun Zhang; Haiyan Guan. End-to-End Change Detection for High Resolution Satellite Images Using Improved UNet++. Remote Sensing 2019, 11, 1382 .
AMA StyleDaifeng Peng, Yongjun Zhang, Haiyan Guan. End-to-End Change Detection for High Resolution Satellite Images Using Improved UNet++. Remote Sensing. 2019; 11 (11):1382.
Chicago/Turabian StyleDaifeng Peng; Yongjun Zhang; Haiyan Guan. 2019. "End-to-End Change Detection for High Resolution Satellite Images Using Improved UNet++." Remote Sensing 11, no. 11: 1382.
In this letter, we propose a mixture likelihood model for accurate oblique image point matching. The basic prior assumption is that the noises are anisotropic with zero mean and different covariances in x- and y-directions for inliers, while the outliers have uniform distribution, which is more suitable for tilted scenes or viewpoint changes. Furthermore, the oblique image point matching problem is formulated as an improved maximum a posteriori (IMAP) estimation of a Bayesian model. In this model, based on the vector field interpolation framework, we combined the mixture likelihood model and our previous adaptive image mismatch removal method, where a two-order term of the regularization coefficient is introduced into the regularized risk function, and a parameter self-adaptive Gaussian kernel function is imposed to construct the regularization term. Subsequently, the expectation-maximization algorithm is utilized to solve the IMAP estimation, in which all the latent variances are able to obtain excellent estimation. Experimental results on real data sets verified that our method was superior to some similar methods in terms of precision and also had better self-adaptability characteristic than some hypothesis-and-verify methods. More experiments on viewpoint changes demonstrated our method's effectiveness without loss of precision-recall tradeoffs, besides significant efficiency improvement.
Xunwei Xie; Yongjun Zhang; Xiang Wang; Daifeng Peng. A Mixture Likelihood Model of the Anisotropic Gaussian and Uniform Distributions for Accurate Oblique Image Point Matching. IEEE Geoscience and Remote Sensing Letters 2019, 16, 1437 -1441.
AMA StyleXunwei Xie, Yongjun Zhang, Xiang Wang, Daifeng Peng. A Mixture Likelihood Model of the Anisotropic Gaussian and Uniform Distributions for Accurate Oblique Image Point Matching. IEEE Geoscience and Remote Sensing Letters. 2019; 16 (9):1437-1441.
Chicago/Turabian StyleXunwei Xie; Yongjun Zhang; Xiang Wang; Daifeng Peng. 2019. "A Mixture Likelihood Model of the Anisotropic Gaussian and Uniform Distributions for Accurate Oblique Image Point Matching." IEEE Geoscience and Remote Sensing Letters 16, no. 9: 1437-1441.
Multispectral LiDAR, characterization of completeness, and consistency of spectrum and spatial geometric data provide a new data source for land cover classification. However, how to choose the optimal features for a given set of land covers is an open problem for effective land cover classification. To address this problem, we propose a comparative scheme, which investigates a popular deep learning (deep Boltzmann machine, DBM) model for high-level feature representation and widely used machine learning methods for low-level feature extraction and selection [principal component analysis (PCA) and random forest (RF)] in land cover classification. The comparative study was conducted on the multispectral LiDAR point clouds, acquired by a Teledyne Optech's Titan airborne system. The deep learning-based high-level feature representation experimental results showed that, on an ordinary personal computer or workstation, this method required larger training samples and more computational complexity than the machine learning-based low-level feature extraction and selection methods. However, our comparative experiments demonstrated that the classification accuracies of the DBM-based method were higher than those of the RF-based and PCA-based methods using multispectral LiDAR data.
Suoyan Pan; Haiyan Guan; Yongtao Yu; Jonathan Li; Daifeng Peng. A Comparative Land-Cover Classification Feature Study of Learning Algorithms: DBM, PCA, and RF Using Multispectral LiDAR Data. IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing 2019, 12, 1314 -1326.
AMA StyleSuoyan Pan, Haiyan Guan, Yongtao Yu, Jonathan Li, Daifeng Peng. A Comparative Land-Cover Classification Feature Study of Learning Algorithms: DBM, PCA, and RF Using Multispectral LiDAR Data. IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing. 2019; 12 (4):1314-1326.
Chicago/Turabian StyleSuoyan Pan; Haiyan Guan; Yongtao Yu; Jonathan Li; Daifeng Peng. 2019. "A Comparative Land-Cover Classification Feature Study of Learning Algorithms: DBM, PCA, and RF Using Multispectral LiDAR Data." IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing 12, no. 4: 1314-1326.