Satellite radar observation of large surface collapses induced by the 2017 North Korea nuclear test.

The artificial earthquake of mb 6.1 related to the North Korea's sixth nuclear test occured at Mt. Mantap, North Korea on September 3, 2017. It was reported that a large and complex surface deformation was caused by the event. The surface deformation was composed of expansion of explosions, collapse, compaction and landslides. Since the precise vertical deformation measurement is very important to estimate the stability of the nuclear test facility, we retrieved a precise 3D surface deformation field and then decomposed the vertical deformation pattern from the 3D deformation. The measured maximum deformation was about - 491, - 343 and 166 cm with the measurement uncertainty of about 3.3, 4.1 and 2.7 cm in the east, north and up directions, respectively. The maximum horizontal deformation was approximately 515 cm. The horizontal deformation clearly showed a radial pattern because it was mainly caused by the explosions and landslides, while the vertical deformation displayed a rugged pattern because it was affected by the explosions, compaction and collapse. The collapse may seem to occur along the underground tunnels and at the test site's epicenter as well. Moreover, the severe collapse was observed westside from the epicenter of the sixth nuclear test, and it has a depth of about 68.6 cm on the area of 0.3765 km2. On the basis of our results including the shapes, locations and volume changes of the large collapse, evidently a new vital piece of information was obtained so that it could be used to interprete the sixth nuclear test more accurately.

Mapping Oil Spills from Dual-Polarized SAR Images Using an Artificial Neural Network: Application to Oil Spill in the Kerch Strait in November 2007.

Synthetic aperture radar (SAR) has been widely used to detect oil-spill areas through the backscattering intensity difference between oil and background pixels. However, since the signal is similar to that produced by other phenomena, positive identification can be challenging. In this study we developed an algorithm to effectively analyze large-scale oil spill areas in SAR images by focusing on optimizing the input layer to artificial neural network (ANN) through removal the factor of lowering the accuracy. An ANN algorithm was used to generate probability maps of oil spills. Highly accurate pixel-based data processing was conducted through false or un-detection element reduction by normalizing the image or applying a non-local (NL) means filter and median filter to the input neurons for ANN. In addition, the standard deviation of co-polarized phase difference (CPD) was used to reduce false detection from the look-alike with weak damping effect. The algorithm was validated using TerraSAR-X images of an oil spill caused by stranded oil tanker Volganefti-139 in the Kerch Strait in 2007. According to the validation results of the receiver operating characteristic (ROC) curve, the oil spill was detected with an accuracy of about 95.19% and un-detection or false detection by look-alike and speckle noise was greatly reduced.

Mapping three-dimensional surface deformation caused by the 2010 Haiti earthquake using advanced satellite radar interferometry.

Mapping three-dimensional (3D) surface deformation caused by an earthquake is very important for the environmental, cultural, economic and social sustainability of human beings. Synthetic aperture radar (SAR) systems made it possible to measure precise 3D deformations by combining SAR interferometry (InSAR) and multiple aperture interferometry (MAI). In this paper, we retrieve the 3D surface deformation field of the 2010 Haiti earthquake which occurred on January 12, 2010 by a magnitude 7.0 Mw by using the advanced interferometric technique that integrates InSAR and MAI data. The surface deformation has been observed by previous researchers using the InSAR and GPS method, but 3D deformation has not been measured yet due to low interferometric coherence. The combination of InSAR and MAI were applied to the ALOS PALSAR ascending and descending pairs, and were validated with the GPS in-situ measurements. The archived measurement accuracy was as little as 1.85, 5.49 and 3.08 cm in the east, north and up directions, respectively. This result indicates that the InSAR/MAI-derived 3D deformations are well matched with the GPS deformations. The 3D deformations are expected to allow us to improve estimation of the area affected by the 2010 Haiti earthquake.

Retrieving Precise Three-Dimensional Deformation on the 2014 M6.0 South Napa Earthquake by Joint Inversion of Multi-Sensor SAR.

We reconstructed the three-dimensional (3D) surface displacement field of the 24 August 2014 M6.0 South Napa earthquake using SAR data from the Italian Space Agency's COSMO-SkyMed and the European Space Agency's Sentinel-1A satellites. Along-track and cross-track displacements produced with conventional SAR interferometry (InSAR) and multiple-aperture SAR interferometry (MAI) techniques were integrated to retrieve the east, north, and up components of surface deformation. The resulting 3D displacement maps clearly delineated the right-lateral shear motion of the fault rupture with a maximum surface displacement of approximately 45 cm along the fault's strike, showing the east and north components of the trace particularly clearly. These maps also suggested a better-constrained model for the South Napa earthquake. We determined a strike of approximately 338° and dip of 85° by applying the Okada dislocation model considering a single patch with a homogeneous slip motion. Using the distributed slip model obtained by a linear solution, we estimated that a peak slip of approximately 1.7 m occurred around 4 km depth from the surface. 3D modelling using the retrieved 3D maps helps clarify the fault's nature and thus characterize its behaviour.

An efficient mosaic algorithm considering seasonal variation: application to KOMPSAT-2 satellite images.

As the aerospace industry grows, images obtained from Earth observation satellites have been successfully used in various fields. Specifically, the demand for a high-resolution (HR) optical images is gradually increasing, and hence the generation of a high-quality mosaic image is being magnified as an interesting issue. In this paper, we have proposed an efficient mosaic algorithm for HR optical images that are significantly different due to seasonal change. The algorithm includes main steps such as: (1) seamline extraction from gradient magnitude and seam images; (2) histogram matching; and (3) image feathering. Eleven Kompsat-2 images characterized by seasonal variations are used for the performance validation of the proposed method. The results of the performance test show that the proposed method effectively mosaics Kompsat-2 adjacent images including severe seasonal changes. Moreover, the results reveal that the proposed method is applicable to HR optic images such as GeoEye, IKONOS, QuickBird, RapidEye, SPOT, WorldView, etc.

Multi-sensor fusion of Landsat 8 thermal infrared (TIR) and panchromatic (PAN) images.

Data fusion is defined as the combination of data from multiple sensors such that the resulting information is better than would be possible when the sensors are used individually. The multi-sensor fusion of panchromatic (PAN) and thermal infrared (TIR) images is a good example of this data fusion. While a PAN image has higher spatial resolution, a TIR one has lower spatial resolution. In this study, we have proposed an efficient method to fuse Landsat 8 PAN and TIR images using an optimal scaling factor in order to control the trade-off between the spatial details and the thermal information. We have compared the fused images created from different scaling factors and then tested the performance of the proposed method at urban and rural test areas. The test results show that the proposed method merges the spatial resolution of PAN image and the temperature information of TIR image efficiently. The proposed method may be applied to detect lava flows of volcanic activity, radioactive exposure of nuclear power plants, and surface temperature change with respect to land-use change.

Theoretical accuracy of along-track displacement measurements from multiple-aperture interferometry (MAI).

The measurement of precise along-track displacements has been made with the multiple-aperture interferometry (MAI). The empirical accuracies of the MAI measurements are about 6.3 and 3.57 cm for ERS and ALOS data, respectively. However, the estimated empirical accuracies cannot be generalized to any interferometric pair because they largely depend on the processing parameters and coherence of the used SAR data. A theoretical formula is given to calculate an expected MAI measurement accuracy according to the system and processing parameters and interferometric coherence. In this paper, we have investigated the expected MAI measurement accuracy on the basis of the theoretical formula for the existing X-, C- and L-band satellite SAR systems. The similarity between the expected and empirical MAI measurement accuracies has been tested as well. The expected accuracies of about 2-3 cm and 3-4 cm (γ = 0.8) are calculated for the X- and L-band SAR systems, respectively. For the C-band systems, the expected accuracy of Radarsat-2 ultra-fine is about 3-4 cm and that of Sentinel-1 IW is about 27 cm (γ = 0.8). The results indicate that the expected MAI measurement accuracy of a given interferometric pair can be easily calculated by using the theoretical formula.

Detection and restoration of defective lines in the SPOT 4 SWIR band.

This paper presents the categorization and restoration of defective lines developed in pushbroom images. About 100 of the 3000 SPOT 4 SWIR detectors malfunction, which degrades image quality. Conventional methods have difficulties in effectively detecting and restoring defective lines, because they ignore the heterogeneity of the ground surface and the presence of sporadically unstable detectors with gain and offset that vary during a scan. While all defective lines have previously been considered as a single type, here they are categorized into three types according to the variation pattern in the scanning direction: constant defective lines, irregular defective lines, and irrecoverable defective lines. The detection procedure utilizes summed data and standard deviation data that consist of abnormal peaks originating from defective lines and a slowly varying baseline reflecting the surface characteristics within the image. The defective lines are detected by finding abnormal peaks, and classified and restored by using either a moment-matching method or interpolation, depending upon their types. Three SPOT 4 images were used to test and evaluate the performance of the proposed method. From the test results, the constant defective line was the most common type, comprising about 60%, while the irregular defective lines caused serious image degradation because of the difficulty of detecting and classifying them. Commission and omission errors were less than 10% and detection accuracy was higher than 90%. The analysis of signal-to-noise ratio (SNR) showed that the low SNR created by the defective lines was effectively removed. Our method gave a significant improvement of the detection and restoration capability.