scholarly journals Performance of Change Detection Algorithms Using Heterogeneous Images and Extended Multi-attribute Profiles (EMAPs)

2019 ◽  
Vol 11 (20) ◽  
pp. 2377 ◽  
Author(s):  
Chiman Kwan ◽  
Bulent Ayhan ◽  
Jude Larkin ◽  
Liyun Kwan ◽  
Sergio Bernabé ◽  
...  
Keyword(s):  
Change Detection ◽  
Detection Performance ◽  
Detection Algorithms ◽  
Change Detection Algorithms ◽  
Image Pairs ◽  
Change Detection Performance

We present detection performance of ten change detection algorithms with and without the use of Extended Multi-Attribute Profiles (EMAPs). Heterogeneous image pairs (also known as multimodal image pairs), which are acquired by different imagers, are used as the pre-event and post-event images in the investigations. The objective of this work is to examine if the use of EMAP, which generates synthetic bands, can improve the detection performances of these change detection algorithms. Extensive experiments using five heterogeneous image pairs and ten change detection algorithms were carried out. It was observed that in 34 out of 50 cases, change detection performance was improved with EMAP. A consistent detection performance boost in all five datasets was observed with EMAP for Homogeneous Pixel Transformation (HPT), Chronochrome (CC), and Covariance Equalization (CE) change detection algorithms.

scholarly journals S2Looking: A Satellite Side-Looking Dataset for Building Change Detection

2021 ◽  
Vol 13 (24) ◽  
pp. 5094
Author(s):  
Li Shen ◽  
Yao Lu ◽  
Hao Chen ◽  
Hao Wei ◽  
Donghai Xie ◽  
...  
Keyword(s):  
Deep Learning ◽  
Change Detection ◽  
Rural Areas ◽  
Large Scale ◽  
Training Data ◽  
Existing Building ◽  
Detection Algorithms ◽  
Change Detection Algorithms ◽  
Image Pairs ◽  
The Military

Building-change detection underpins many important applications, especially in the military and crisis-management domains. Recent methods used for change detection have shifted towards deep learning, which depends on the quality of its training data. The assembly of large-scale annotated satellite imagery datasets is therefore essential for global building-change surveillance. Existing datasets almost exclusively offer near-nadir viewing angles. This limits the range of changes that can be detected. By offering larger observation ranges, the scroll imaging mode of optical satellites presents an opportunity to overcome this restriction. This paper therefore introduces S2Looking, a building-change-detection dataset that contains large-scale side-looking satellite images captured at various off-nadir angles. The dataset consists of 5000 bitemporal image pairs of rural areas and more than 65,920 annotated instances of changes throughout the world. The dataset can be used to train deep-learning-based change-detection algorithms. It expands upon existing datasets by providing (1) larger viewing angles; (2) large illumination variances; and (3) the added complexity of rural images. To facilitate the use of the dataset, a benchmark task has been established, and preliminary tests suggest that deep-learning algorithms find the dataset significantly more challenging than the closest-competing near-nadir dataset, LEVIR-CD+. S2Looking may therefore promote important advances in existing building-change-detection algorithms.

Flightpath Tracking, Change Detection and Visual Scanning in an Integrated Hazard Display

2005 ◽  
Vol 49 (1) ◽  
pp. 68-72 ◽  
Author(s):  
Amy L. Alexander ◽  
Christopher D. Wickens
Keyword(s):  
Response Time ◽  
Change Detection ◽  
Detection Performance ◽  
Vertical Dimension ◽  
Visual Scanning ◽  
Flight Instructors ◽  
Detection Response ◽  
Step Down ◽  
Integrated Hazard ◽  
Change Detection Performance

Twenty-four certified flight instructors were required to fly a series of curved, step-down approaches while detecting changes to surrounding traffic aircraft and weather cell icons on two integrated hazard display (IHD) formats (2D coplanar and split-screen) under varying workload levels. Generally, it appears that the 2D coplanar IHD was better in supporting flightpath tracking and change detection performance when compared to a split-screen display. Pilots exhibited superior flightpath tracking (in the vertical dimension, and under low workload) when using the 2D coplanar IHD, although this effect was mitigated by increasing workload such that tracking deteriorated faster with the 2D coplanar than the split-screen display. The spawned 3D cost of diminished size with distance from ownship played a role in change detection response time—pilots were slower (particularly in detecting traffic aircraft changes) with the split-screen compared to the 2D coplanar IHD. These effects will be discussed within the context of visual scanning measures.

An Image Change Detection Algorithm Based on Multi-Feature Self-Attention Fusion Mechanism UNet Network

2021 ◽  
Author(s):  
Gulnaz Alimjan ◽  
Yiliyaer Jiaermuhamaiti ◽  
Huxidan Jumahong ◽  
Shuangling Zhu ◽  
Pazilat Nurmamat
Keyword(s):  
Change Detection ◽  
Detection Algorithm ◽  
Convolutional Network ◽  
Detection Algorithms ◽  
Multiple Dimensions ◽  
Change Detection Algorithms ◽  
Image Change Detection ◽  
Control Loss ◽  
The Relationship ◽  
Entire Network

Various UNet architecture-based image change detection algorithms promote the development of image change detection, but there are still some defects. First, under the encoder–decoder framework, the low-level features are extracted many times in multiple dimensions, which generates redundant information; second, the relationship between each feature layer is not modeled so sufficiently that it cannot produce the optimal feature differentiation representation. This paper proposes a remote image change detection algorithm based on the multi-feature self-attention fusion mechanism UNet network, abbreviated as MFSAF UNet (multi-feature self-attention fusion UNet). We attempt to add multi-feature self-attention mechanism between the encoder and decoder of UNet to obtain richer context dependence and overcome the two above-mentioned restrictions. Since the capacity of convolution-based UNet network is directly proportional to network depth, and a deeper convolutional network means more training parameters, so the convolution of each layer of UNet is replaced as a separated convolution, which makes the entire network to be lighter and the model’s execution efficiency is slightly better than the traditional convolution operation. In addition to these, another innovation point of this paper is using preference to control loss function and meet the demands for different accuracies and recall rates. The simulation test results verify the validity and robustness of this approach.

scholarly journals AIRBORNE LIGHT DETECTION AND RANGING (LIDAR) DERIVED DEFORMATION FROM THE MW 6.0 24 AUGUST, 2014 SOUTH NAPA EARTHQUAKE ESTIMATED BY TWO AND THREE DIMENSIONAL POINT CLOUD CHANGE DETECTION TECHNIQUES

2016 ◽  
Vol XLI-B2 ◽  
pp. 35-42 ◽  
Author(s):  
A. W. Lyda ◽  
X. Zhang ◽  
C. L. Glennie ◽  
K. Hudnut ◽  
B. A. Brooks
Keyword(s):  
Change Detection ◽  
Point Cloud ◽  
Near Field ◽  
Three Dimensional ◽  
Light Detection And Ranging ◽  
Data Sets ◽  
Light Detection ◽  
Icp Algorithm ◽  
Detection Algorithms ◽  
Change Detection Algorithms

Remote sensing via LiDAR (Light Detection And Ranging) has proven extremely useful in both Earth science and hazard related studies. Surveys taken before and after an earthquake for example, can provide decimeter-level, 3D near-field estimates of land deformation that offer better spatial coverage of the near field rupture zone than other geodetic methods (e.g., InSAR, GNSS, or alignment array). In this study, we compare and contrast estimates of deformation obtained from different pre and post-event airborne laser scanning (ALS) data sets of the 2014 South Napa Earthquake using two change detection algorithms, Iterative Control Point (ICP) and Particle Image Velocimetry (PIV). The ICP algorithm is a closest point based registration algorithm that can iteratively acquire three dimensional deformations from airborne LiDAR data sets. By employing a newly proposed partition scheme, “moving window,” to handle the large spatial scale point cloud over the earthquake rupture area, the ICP process applies a rigid registration of data sets within an overlapped window to enhance the change detection results of the local, spatially varying surface deformation near-fault. The other algorithm, PIV, is a well-established, two dimensional image co-registration and correlation technique developed in fluid mechanics research and later applied to geotechnical studies. Adapted here for an earthquake with little vertical movement, the 3D point cloud is interpolated into a 2D DTM image and horizontal deformation is determined by assessing the cross-correlation of interrogation areas within the images to find the most likely deformation between two areas. Both the PIV process and the ICP algorithm are further benefited by a presented, novel use of urban geodetic markers. Analogous to the persistent scatterer technique employed with differential radar observations, this new LiDAR application exploits a classified point cloud dataset to assist the change detection algorithms. Ground deformation results and statistics from these techniques are presented and discussed here with supplementary analyses of the differences between techniques and the effects of temporal spacing between LiDAR datasets. Results show that both change detection methods provide consistent near field deformation comparable to field observed offsets. The deformation can vary in quality but estimated standard deviations are always below thirty one centimeters. This variation in quality differentiates the methods and proves that factors such as geodetic markers and temporal spacing play major roles in the outcomes of ALS change detection surveys.

A CFAR optimization for low frequency UWB SAR change detection algorithms

2017 ◽  
Author(s):  
Ana C. F. Fabrin ◽  
Ricardo D. Molin ◽  
Dimas I. Alves ◽  
Renato Machado ◽  
Fabio M. Bayer ◽  
...  
Keyword(s):  
Change Detection ◽  
Low Frequency ◽  
Detection Algorithms ◽  
Change Detection Algorithms
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