Use of Landsat 5 for Change Detection at 1998 Indian and Pakistani Nuclear Test Sites

SUMMARYDeclared North Korean nuclear tests in 2006, 2009, 2013, and 2016 were observed seismically at regional and teleseismic distances. Waveform similarity allows the events to be located relatively with far greater accuracy than the absolute locations can be determined from seismic data alone. There is now significant redundancy in the data given the large number of regional and teleseismic stations that have recorded multiple events, and relative location estimates can be confirmed independently by performing calculations on many mutually exclusive sets of measurements. Using a 1-dimensional global velocity model, the distances between the events estimated using teleseismic P phases are found to be approximately 25% shorter than the distances between events estimated using regional Pn phases. The 2009The , 2013, and 2016 events all take place within 1 km of each other and the discrepancy between the regional and teleseismic relative location estimates is no more than about 150 m. The discrepancy is much more significant when estimating the location of the more distant 2006 event relative to the later explosions with regional and teleseismic estimates varying by many hundreds of meters. The relative location of the 2006 event is challenging given the smaller number of observing stations, the lower signal-to-noise ratio, and significant waveform dissimilarity at some regional 2 S. J. Gibbons et al.stations. The 2006 event is however highly significant in constraining the absolute locations in the terrain at the Punggye-ri test-site in relation to observed surface infrastructure.For each seismic arrival used to estimate the relative locations, we define a slowness scaling factor which multiplies the gradient of seismic traveltime versus distance, evaluated at the source, relative to the applied 1-d velocity model. A procedure for estimating correction terms which reduce the double-difference time residual vector norms is presented together with a discussion of the associated uncertainty. The modified velocity gradients reduce the residuals, the relative location uncertainties, and the sensitivity to the combination of stations used. The traveltime gradients appear to be overestimated for the regional phases, and teleseismic relative location estimates are likely to be more accurate despite an apparent lower precision. Calibrations for regional phases are essential given that smaller magnitude events are likely not to be recorded teleseismically. We discuss

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“…be determined with the greatest confidence using remote sensing observation of surface deformation (e.g. Zelinski et al 2014).…”

Section: The North Korean Nuclear Tests 15mentioning

confidence: 99%

Accurate relative location estimates for the North Korean nuclear tests using empirical slowness corrections

Gibbons¹,

Pabian

Näsholm³

et al. 2016

Geophys. J. Int.

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“…. Remote sensing image change detection research plays a crucial role in land use and land cover change analysis, forest and vegetation change monitoring, ecosystem monitoring, urban expansion research, resource management, and damage assessment [2]- [9]. With the development of satellite imaging technology, many high-resolution remote sensing images can be obtained more easily.…”

mentioning

confidence: 99%

DASNet: Dual Attentive Fully Convolutional Siamese Networks for Change Detection in High-Resolution Satellite Images

Chen

Yuan

Peng

et al. 2021

IEEE J. Sel. Top. Appl. Earth Observations Remote Sensing

481

209

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Change detection is a basic task of remote sensing image processing. The research objective is to identify the change information of interest and filter out the irrelevant change information as interference factors. Recently, the rise in deep learning has provided new tools for change detection, which have yielded impressive results. However, the available methods focus mainly on the difference information between multitemporal remote sensing images and lack robustness to pseudochange information. To overcome the lack of resistance in current methods to pseudochanges, in this article, we propose a new method, namely, dual attentive fully convolutional Siamese networks, for change detection in high-resolution images. Through the dual attention mechanism, long-range dependencies are captured to obtain more discriminant feature representations to enhance the recognition performance of the model. Moreover, the imbalanced sample is a serious problem in change detection, i.e., unchanged samples are much more abundant than changed samples, which is one of the main reasons for pseudochanges. We propose the weighted double-margin contrastive loss to address this problem by punishing attention to unchanged feature pairs and increasing attention to changed feature pairs. The experimental results of our method on the change detection dataset and the building change detection dataset demonstrate that compared with other baseline methods, the proposed method realizes maximum improvements of 2.9% and 4.2%, respectively, in the F1 score. Our PyTorch implementation is available at https://github.com/lehaifeng/DASNet.

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“…can give all solutions α of Eq. (7). Further, we can normalize α to make the corresponding eigenvectors in H be normalized…”

Section: A Kpcamentioning

confidence: 99%

Unsupervised Change Detection in Multi-temporal VHR Images Based on Deep Kernel PCA Convolutional Mapping Network

Chen

Do³

et al. 2019

Preprint

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With the development of Earth observation technology, very-high-resolution (VHR) image has become an important data source of change detection. Nowadays, deep learning methods have achieved conspicuous performance in the change detection of VHR images. Nonetheless, most of the existing change detection models based on deep learning require annotated training samples. In this paper, a novel unsupervised model called kernel principal component analysis (KPCA) convolution is proposed for extracting representative features from multi-temporal VHR images. Based on the KPCA convolution, an unsupervised deep siamese KPCA convolutional mapping network (KPCA-MNet) is designed for binary and multi-class change detection. In the KPCA-MNet, the high-level spatial-spectral feature maps are extracted by a deep siamese network consisting of weight-shared PCA convolution layers. Then, the change information in the feature difference map is mapped into a 2-D polar domain. Finally, the change detection results are generated by threshold segmentation and clustering algorithms. All procedures of KPCA-MNet does not require labeled data. The theoretical analysis and experimental results demonstrate the validity, robustness, and potential of the proposed method in two binary change detection data sets and one multi-class change detection data set. Index Terms-Change detection, kernel principal component analysis (KPCA), deep siamese KPCA convolutional mapping network, very-high-resolution (VHR) images, unsupervised multiclass change detection I. INTRODUCTIONT HE Earth surface is constantly evolving. Real-time and accurate access to the surface changes is of great importance for the better understanding of human activities, ecosystem and their interactions [1]. Multi-temporal remote sensing images can reveal the dynamic changes in the surface. Therefore, change detection based on multi-temporal images has become more and more significant. Change detection (CD) is the process of identifying differences in the state of an object

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Use of Landsat 5 for Change Detection at 1998 Indian and Pakistani Nuclear Test Sites

Cited by 21 publications

References 10 publications

Accurate relative location estimates for the North Korean nuclear tests using empirical slowness corrections

Accurate relative location estimates for the North Korean nuclear tests using empirical slowness corrections

DASNet: Dual Attentive Fully Convolutional Siamese Networks for Change Detection in High-Resolution Satellite Images

Unsupervised Change Detection in Multi-temporal VHR Images Based on Deep Kernel PCA Convolutional Mapping Network

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