scholarly journals Building Change Detection Using a Shape Context Similarity Model for LiDAR Data

2020 ◽  
Vol 9 (11) ◽  
pp. 678
Author(s):  
Xuzhe Lyu ◽  
Ming Hao ◽  
Wenzhong Shi
Keyword(s):  
Change Detection ◽  
Detection Methods ◽  
Lidar Data ◽  
Shape Context ◽  
Multi Scale ◽  
Detection Approach ◽  
Context Similarity ◽  
Surface Models ◽  
Similarity Model ◽  
The Difference

In this paper, a novel building change detection approach is proposed using statistical region merging (SRM) and a shape context similarity model for Light Detection and Ranging (LiDAR) data. First, digital surface models (DSMs) are generated from LiDAR acquired at two different epochs, and the difference data D-DSM is created by difference processing. Second, to reduce the noise and registration error of the pixel-based method, the SRM algorithm is applied to segment the D-DSM, and multi-scale segmentation results are obtained under different scale values. Then, the shape context similarity model is used to calculate the shape similarity between the segmented objects and the buildings. Finally, the refined building change map is produced by the k-means clustering method based on shape context similarity and area-to-length ratio. The experimental results indicated that the proposed method could effectively improve the accuracy of building change detection compared with some popular change detection methods.

scholarly journals SAR Image Change Detection Based on Multi-scale Feature Extraction

2021 ◽  
Vol 10 (4) ◽  
pp. 077-081
Author(s):  
Xiaoqian Yuan ◽  
Chao Chen ◽  
Shan Tian ◽  
Jiandan Zhong
Keyword(s):  
Feature Extraction ◽  
Change Detection ◽  
Speckle Noise ◽  
Kernel Matrix ◽  
Sar Image ◽  
Scale Feature ◽  
Difference Image ◽  
Multi Scale ◽  
Image Change Detection ◽  
The Difference

In order to improve the contrast of the difference image and reduce the interference of the speckle noise in the synthetic aperture radar (SAR) image, this paper proposes a SAR image change detection algorithm based on multi-scale feature extraction. In this paper, a kernel matrix with weights is used to extract features of two original images, and then the logarithmic ratio method is used to obtain the difference images of two images, and the change area of the images are extracted. Then, the different sizes of kernel matrix are used to extract the abstract features of different scales of the difference image. This operation can make the difference image have a higher contrast. Finally, the cumulative weighted average is obtained to obtain the final difference image, which can further suppress the speckle noise in the image.

scholarly journals Change Detection Based on Multi-Grained Cascade Forest and Multi-Scale Fusion for SAR Images

2019 ◽  
Vol 11 (2) ◽  
pp. 142 ◽  
Author(s):  
Wenping Ma ◽  
Hui Yang ◽  
Yue Wu ◽  
Yunta Xiong ◽  
Tao Hu ◽  
...  
Keyword(s):  
Change Detection ◽  
Representation Learning ◽  
Local Information ◽  
Learning Ability ◽  
Detection Methods ◽  
Detection Accuracy ◽  
Image Block ◽  
Sar Images ◽  
Difference Image ◽  
Multi Scale

In this paper, a novel change detection approach based on multi-grained cascade forest(gcForest) and multi-scale fusion for synthetic aperture radar (SAR) images is proposed. It detectsthe changed and unchanged areas of the images by using the well-trained gcForest. Most existingchange detection methods need to select the appropriate size of the image block. However, thesingle size image block only provides a part of the local information, and gcForest cannot achieve agood effect on the image representation learning ability. Therefore, the proposed approach choosesdifferent sizes of image blocks as the input of gcForest, which can learn more image characteristicsand reduce the influence of the local information of the image on the classification result as well.In addition, in order to improve the detection accuracy of those pixels whose gray value changesabruptly, the proposed approach combines gradient information of the difference image with theprobability map obtained from the well-trained gcForest. Therefore, the image edge information canbe enhanced and the accuracy of edge detection can be improved by extracting the image gradientinformation. Experiments on four data sets indicate that the proposed approach outperforms otherstate-of-the-art algorithms.

scholarly journals A Novel Regional Fusion Network for 3D Object Detection based on RGB Images and Point Clouds

2021 ◽  
Author(s):  
Hung-Hao Chen ◽  
Chia-Hung Wang ◽  
Hsueh-Wei Chen ◽  
Pei-Yung Hsiao ◽  
Li-Chen Fu ◽  
...  
Keyword(s):  
Object Detection ◽  
Receptive Fields ◽  
Point Clouds ◽  
Detection Methods ◽  
Lidar Data ◽  
3D Object ◽  
Multi Scale ◽  
Interest Level ◽  
Rgb Images ◽  
3D Object Detection

The current fusion-based methods transform LiDAR data into bird’s eye view (BEV) representations or 3D voxel, leading to information loss and heavy computation cost of 3D convolution. In contrast, we directly consume raw point clouds and perform fusion between two modalities. We employ the concept of region proposal network to generate proposals from two streams, respectively. In order to make two sensors compensate the weakness of each other, we utilize the calibration parameters to project proposals from one stream onto the other. With the proposed multi-scale feature aggregation module, we are able to combine the extracted regionof-interest-level (RoI-level) features of RGB stream from different receptive fields, resulting in fertilizing feature richness. Experiments on KITTI dataset show that our proposed network outperforms other fusion-based methods with meaningful improvements as compared to 3D object detection methods under challenging setting.

A novel change detection approach for VHR remote sensing images by integrating multi-scale features

2019 ◽  
Vol 40 (13) ◽  
pp. 4910-4933 ◽  
Author(s):  
Ming Hao ◽  
Wenzhong Shi ◽  
Yuanxin Ye ◽  
Hua Zhang ◽  
Kazhong Deng
Keyword(s):  
Remote Sensing ◽  
Change Detection ◽  
Remote Sensing Images ◽  
Multi Scale ◽  
Detection Approach

scholarly journals Multi-Scale Proposal Generation for Ship Detection in SAR Images

2019 ◽  
Vol 11 (5) ◽  
pp. 526 ◽  
Author(s):  
Nengyuan Liu ◽  
Zongjie Cao ◽  
Zongyong Cui ◽  
Yiming Pi ◽  
Sihang Dang
Keyword(s):  
Area Under The Curve ◽  
Detection Methods ◽  
Edge Density ◽  
Sar Images ◽  
Ship Detection ◽  
Multi Scale ◽  
Hierarchical Grouping ◽  
The Difference ◽  
Two Stages ◽  
Strong Scattering

The classic ship detection methods in synthetic aperture radar (SAR) images suffer from an extreme variance of ship scale. Generating a set of ship proposals before detection operation can effectively alleviate the multi-scale problem. In order to construct a scale-independent proposal generator for SAR images, we suggest four characteristics of ships in SAR images and the corresponding four procedures in this paper. Based on these characteristics and procedures, we put forward a framework to explore multi-scale ship proposals. The designed framework mainly contains two stages: hierarchical grouping and proposal scoring. Firstly, we extract edges, superpixels and strong scattering components from SAR images. The ship proposals are obtained at hierarchical grouping stage by combining the strong scattering components with superpixel grouping. Considering the difference of edge density and the completeness and tightness of contour, we obtain the scores to measure the confidence that a proposal contains a ship. Finally, the ranking proposals are obtained. Extensive experiments demonstrate the effectiveness of the four procedures. Our method achieves 0.70 the average best overlap (ABO) score, 0.59 the area under the curve (AUC) score and 0.85 best recall on a challenging dataset. In addition, the recall of our method on three scale subsets are all above 0.80. Experimental results demonstrate that our algorithm outperforms the approaches previously used for SAR images.

scholarly journals Plagiarism Detection in Armenian Texts Using Intrinsic Stylometric Analysis

2021 ◽  
Vol 33 (1) ◽  
pp. 209-224
Author(s):  
Yeva Maksimovna Yeshilbashian ◽  
Ariana Armenovna Asatryan ◽  
Tsolak Gukasovitch Ghukasyan
Keyword(s):  
Change Detection ◽  
Hierarchical Clustering ◽  
Detection Methods ◽  
Plagiarism Detection ◽  
Academic Literature ◽  
Synthetic Test ◽  
Standard Set ◽  
Test Sets ◽  
The Difference ◽  
Better Than

In this work we study the application of intrinsic stylometric methods to the task of plagiarism detection in Armenian texts. We use two task setups from PAN’s series of conferences on text forensics and stylometry: style change detection and style breach detection. Style change detection aims to determine whether the text is written by more than one author, while style breach detection detects the boundaries of stylistically distinct text fragments. For these tasks, we generate synthetic test sets for three genres of text: academic, literature, and news, and then use them to evaluate the effectiveness of hierarchical clustering and other relevant models from PAN conferences. We employ a standard set of character-level, lexical and readability features, and additionally perform morphological and dependency parsing of text fragments to extract syntactic features encoding author style information. The evaluation results show that the clustering-based approach fails to correctly detect style change detection in longer texts and is only marginally better for shorter texts. For style breach detection, hierarchical clustering-based approach performs better than a random baseline classifier, but the difference is not sufficient to warrant its practical use. In a complementary experiment, we show that reducing the number of features and multicollinearity in them via PCA helps to increase the precision of style breach detection methods for certain text categories.

scholarly journals OBJECT-ORIENTED CHANGE DETECTION FOR REMOTE SENSING IMAGES BASED ON MULTI-SCALE FUSION

2016 ◽  
Vol XLI-B7 ◽  
pp. 483-491 ◽  
Author(s):  
Wenqing Feng ◽  
Haigang Sui ◽  
Jihui Tu
Keyword(s):  
Remote Sensing ◽  
Change Detection ◽  
Object Oriented ◽  
Detection Methods ◽  
Multi Scale ◽  
Level Change ◽  
Fusion Methods ◽  
Change Vector ◽  
Fuzzy Fusion

In the process of object-oriented change detection, the determination of the optimal segmentation scale is directly related to the subsequent change information extraction and analysis. Aiming at this problem, this paper presents a novel object-level change detection method based on multi-scale segmentation and fusion. First of all, the fine to coarse segmentation is used to obtain initial objects of different sizes; then, according to the features of the objects, Change Vector Analysis is used to obtain the change detection results of various scales. Furthermore, in order to improve the accuracy of change detection, this paper introduces fuzzy fusion and two kinds of decision level fusion methods to get the results of multi-scale fusion. Based on these methods, experiments are done with SPOT5 multi-spectral remote sensing imagery. Compared with pixel-level change detection methods, the overall accuracy of our method has been improved by nearly 10%, and the experimental results prove the feasibility and effectiveness of the fusion strategies.

scholarly journals A Convolutional Neural Network with Parallel Multi-Scale Spatial Pooling to Detect Temporal Changes in SAR Images

2020 ◽  
Vol 12 (10) ◽  
pp. 1619 ◽  
Author(s):  
Jia-Wei Chen ◽  
Rongfang Wang ◽  
Fan Ding ◽  
Bo Liu ◽  
Licheng Jiao ◽  
...  
Keyword(s):  
Change Detection ◽  
Sar Image ◽  
Sar Images ◽  
Convolutional Network ◽  
Difference Image ◽  
Multi Scale ◽  
Spatial Pooling ◽  
Testing Dataset ◽  
Image Change Detection ◽  
The Difference

In synthetic aperture radar (SAR) image change detection, it is quite challenging to exploit the changing information from the noisy difference image subject to the speckle. In this paper, we propose a multi-scale spatial pooling (MSSP) network to exploit the changed information from the noisy difference image. Being different from the traditional convolutional network with only mono-scale pooling kernels, in the proposed method, multi-scale pooling kernels are equipped in a convolutional network to exploit the spatial context information on changed regions from the difference image. Furthermore, to verify the generalization of the proposed method, we apply our proposed method to the cross-dataset bitemporal SAR image change detection, where the MSSP network (MSSP-Net) is trained on a dataset and then applied to an unknown testing dataset. We compare the proposed method with other state-of-arts and the comparisons are performed on four challenging datasets of bitemporal SAR images. Experimental results demonstrate that our proposed method obtain comparable results with S-PCA-Net on YR-A and YR-B dataset and outperforms other state-of-art methods, especially on the Sendai-A and Sendai-B datasets with more complex scenes. More important, MSSP-Net is more efficient than S-PCA-Net and convolutional neural networks (CNN) with less executing time in both training and testing phases.

scholarly journals OBJECT-ORIENTED CHANGE DETECTION FOR REMOTE SENSING IMAGES BASED ON MULTI-SCALE FUSION

2016 ◽  
Vol XLI-B7 ◽  
pp. 483-491
Author(s):  
Wenqing Feng ◽  
Haigang Sui ◽  
Jihui Tu
Keyword(s):  
Remote Sensing ◽  
Change Detection ◽  
Object Oriented ◽  
Detection Methods ◽  
Multi Scale ◽  
Level Change ◽  
Fusion Methods ◽  
Change Vector ◽  
Fuzzy Fusion

In the process of object-oriented change detection, the determination of the optimal segmentation scale is directly related to the subsequent change information extraction and analysis. Aiming at this problem, this paper presents a novel object-level change detection method based on multi-scale segmentation and fusion. First of all, the fine to coarse segmentation is used to obtain initial objects of different sizes; then, according to the features of the objects, Change Vector Analysis is used to obtain the change detection results of various scales. Furthermore, in order to improve the accuracy of change detection, this paper introduces fuzzy fusion and two kinds of decision level fusion methods to get the results of multi-scale fusion. Based on these methods, experiments are done with SPOT5 multi-spectral remote sensing imagery. Compared with pixel-level change detection methods, the overall accuracy of our method has been improved by nearly 10%, and the experimental results prove the feasibility and effectiveness of the fusion strategies.

scholarly journals How Can Despeckling and Structural Features Benefit to Change Detection on Bitemporal SAR Images?

2019 ◽  
Vol 11 (4) ◽  
pp. 421 ◽  
Author(s):  
Rongfang Wang ◽  
Jia-Wei Chen ◽  
Licheng Jiao ◽  
Mi Wang
Keyword(s):  
Change Detection ◽  
Image Interpretation ◽  
Structural Features ◽  
Detection Methods ◽  
Sar Images ◽  
Sparse Model ◽  
Registration Errors ◽  
The Difference ◽  
Difference Images ◽  
Suppress Noise

Change detection on bitemporal synthetic aperture radar (SAR) images is a key branch of SAR image interpretation. However, it is challenging due to speckle and unavoidable registration errors within bitemporal SAR images. A key issue is whether and how despeckling and structural features can improve accuracy. In this paper, we investigate how despeckling and structural features can benefit change detection for SAR images. Several change detection methods were performed on both input images and the corresponding despeckled images, where despeckling was achieved by different methods. The comparisons demonstrate that despeckling methods that preserve the structures can suppress noise in difference images and can improve the accuracy of change detection. We also developed a sparse model to exploit structural features from the difference images while reducing the influence of misalignment between bitemporal SAR images. The comparisons were performed on five datasets of bitemporal SAR images, and the experimental results show that our proposed sparse model outperforms other traditional methods, demonstrating the advantages of change detection.

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