Low Coherence Values Research Articles

A Comparison of Sentinel-1 Biased and Unbiased Coherence for Crop Monitoring and Classification

Abstract. Synthetic Aperture Radar (SAR) holds significant potential for applications in crop monitoring and classification. Interferometric SAR (InSAR) coherence proves effective in monitoring crop growth. Currently, the coherence based on the maximum likelihood estimator is biased towards low coherence values. Therefore, the main aim of this work is to access the performance of Sentinel-1 time-series biased coherence and unbiased coherence in crop monitoring and classification. This study was conducted during the 2018 growing season (April-October) in Komoka, an agricultural region in southwestern Ontario, Canada, primarily cultivating three crops: soybean, corn, and winter wheat. To verify the ability of coherence to monitor crops, a linear correlation coefficient between temporal coherence and dual polarimetric radar vegetation index (DpRVI) was fitted. The results revealed a stable correlation between temporal coherence and DpRVI time-series, with the highest correlation observed for soybean (0.7 < R < 0.8), followed by wheat and corn. Notably, unbiased coherence of the VV channel exhibited the highest correlation (R > 0.75). In addition, we applied unbiased coherence to crop classification. The results show that unbiased coherence exhibits very promising classification performance, with the overall accuracy (84.83%) and kappa coefficient (0.76) of VV improved by 8.35% and 0.12, respectively, over biased coherence, and the overall accuracy (73.25%) and kappa coefficient (0.57) of VH improved by 7.56% and 0.14, respectively, over biased coherence, and all crop classification accuracies were also effectively improved. This study demonstrates the feasibility of coherence monitoring of crops and provides new insights in enhancing the higher separability of crops.

How Does Wind Influence Near-Nadir and Low-Incidence Ka-Band Radar Backscatter and Coherence from Small Inland Water Bodies?

While many studies have been conducted regarding wind-driven Ka-band scattering on the ocean and sea surfaces, few have identified the impacts of Ka-band scattering on small inland water bodies, and fewer have identified the influence of wind on coherence over water. These previous studies have been limited in spatial scale, covering only large water bodies >25 km2. The recently launched Surface Water and Ocean Topography (SWOT) mission is the first Ka-band InSAR satellite designed for mapping water surface elevations and open water areas for rivers as narrow as 100 m and lakes as small as 0.0625 km2. Because measurements of these types are novel, there remains some uncertainty about expected backscatter amplitudes given wind-driven water surface roughness variability. A previous study using the airborne complement to SWOT, AirSWOT, found that low backscatter and low coherence values were indicative of higher errors in the water surface elevation products, recommending minimum thresholds for backscatter and coherence for filtering the data to increase the accuracy of averaged data for lakes and rivers. We determined that the global average wind speed over lakes is 4 m/s, and after comparing AirSWOT backscatter and coherence data with ERA-5 wind speeds, we found that the minimum required speed to retrieve high backscatter and coherence is 3 m/s. We examined 11,072 lakes across Canada and Alaska, with sizes ranging from 350 m2 to 156 km2, significantly smaller than what could be measured with previous Ka-band instruments in orbit. We found that small lakes (0.0625–0.25 km2) have significantly lower backscatter (3–5 dB) and 0.20–0.25 lower coherence than larger lakes (>1 km2). These results suggest that approximately 75% of SWOT observable lake areas around the globe will have consistently high-accuracy water surface elevations, though seasonal wind variability should remain an important consideration. Despite very small lakes presenting lower average backscatter and coherence, this study asserts that SWOT will be able to accurately resolve the water surface elevations and water surface extents for significantly smaller water bodies than have been previously recorded from satellite altimeters. This study additionally lays the foundation for future high-resolution inland water wind speed studies using SWOT data, when the data become available, as the relationships between wind speed and Ka-band backscatter reflect those of traditional scatterometers designed for oceanic studies.

Topic Modelling Using VSM-LDA For Document Summarization

Summarization is a process to simplify the contents of a document by eliminating elements that are considered unimportant but do not reduce the core meaning the document wants to convey. However, as is known, a document will contain more than one topic. So it is necessary to identify the topic so that the summarization process is more effective. Latent Dirichlet Allocation (LDA) is a commonly used method of identifying topics. However, when running a program on a different dataset, LDA experiences "order effects", that is, the resulting topic will be different if the train data sequence is changed. In the same document input, LDA will provide inconsistent topics resulting in low coherence values. Therefore, this paper proposes a topic modelling method using a combination of LDA and VSM (Vector Space Model) for automatic summarization. The proposed method can overcome order effects and identify document topics that are calculated based on the TF-IDF weight on VSM generated by LDA. The results of the proposed topic modeling method on the 1300 Twitter data resulted in the highest coherence value reaching 0.72. The summary results obtained Rouge 1 is 0.78, Rouge 2 is 0.67 dan Rouge L is 0.80.

Deep Learning for Mapping Tropical Forests with TanDEM-X Bistatic InSAR Data

The TanDEM-X synthetic aperture radar (SAR) system allows for the recording of bistatic interferometric SAR (InSAR) acquisitions, which provide additional information to the common amplitude images acquired by monostatic SAR systems. More concretely, the volume decorrelation factor, which can be derived from the bistatic interferometric coherence, is a reliable indicator of the presence of vegetation and it was used as main input feature for the generation of the global TanDEM-X forest/non-forest map, by means of a clustering algorithm. In this work, we investigate the capabilities of deep Convolutional Neural Networks (CNNs) for mapping tropical forests at large-scale using TanDEM-X InSAR data. For this purpose, we rely on a U-Net architecture, which takes as input a set of feature maps selected on the basis of previous preparatory works. Moreover, we design an ad hoc training strategy, aimed at developing a robust model for global mapping purposes, which has to properly manage the large variety of different acquisition geometries characterizing the TanDEM-X global data set. In addition to detecting forest/non-forest areas, the CNN has also been trained to detect water surfaces, which are typically characterized by low values of coherence. By applying the proposed method on single TanDEM-X images, we achieved a significant performance improvement with respect to the baseline clustering approach, with an average F-score increase of 0.13. We then applied such a model for mapping the entire Amazon rainforest, as well as the other tropical forests in Central Africa and South-East Asia, in order to test its robustness and generalization capabilities, and we observed that forests are typically well detected as contour closed regions and that water classification is reliable, too. Finally, the generated maps show a great potential for mapping temporal changes occurring over forested areas and can be used for generating large-scale maps of deforestation.

Uncertainties and Perspectives on Forest Height Estimates by Sentinel-1 Interferometry

Forest height is a key parameter in forestry. SAR interferometry (InSAR) techniques have been extensively adopted to retrieve digital elevation models (DEM) to give a representation of the continuous variation of the Earth’s topography, including forests. Unfortunately, InSAR has been proven to fail over vegetation due to low coherence values; therefore, all phase unwrapping algorithms tend to avoid these areas, making InSAR-derived DEM over vegetation unreliable. In this work, a sensitivity analysis was performed with the aim of properly initializing the relevant operational parameters (baseline and multilooking factor) to maximize the theoretical accuracy of the height difference between the forest and reference point. Some scenarios were proposed to test the resulting “optimal values”, as estimated at the previous step. A simple model was additionally proposed and calibrated, aimed at predicting the optimal baseline value (and therefore image pair selection) for height uncertainty minimization. All our analyses were conducted using free available data from the Copernicus Sentinel-1 mission to support the operational transfer into the forest sector. Finally, the potential uncertainty affecting resulting height measures was quantified, showing that a value lower than 5 m can be expected once all user-dependent parameters (i.e., baseline, multilooking factor, temporal baseline) are properly tuned.

Design of High-Dimensional Grassmannian Frames via Block Minorization Maximization

In this letter, we present an iterative algorithm for constructing high-dimensional incoherent Grassmannian Frames (GFs) which are sets of unit-norm vectors that are minimax optimal: the maximum absolute value of their inner products is a minimum. We formulate the GF design as a nonconvex optimization problem and solve it via an efficient iterative algorithm. The bulk of each iteration of the proposed algorithm is a Linear Program (LP) for which there are a host of efficient solvers. The proposed algorithm is based on the theoretically sound Minorization Maximization technique which, unlike some of the state-of-the-art design approaches, monotonically minimizes the design criterion and can construct GFs with low coherence values.

Investigation of Sentinel-1 capabilities to detect hydrothermal alteration based on multitemporal interferometric coherence: the case of Nisyros volcano (Greece)

Abstract This paper aims to investigate the capability of Sentinel-1 Synthetic Aperture Radar (SAR) data to detect hydrothermal alteration zones within the caldera of the Nisyros volcano in Greece through a multitemporal interferometric coherence analysis. For that purpose, seven ascending pass Sentinel-1B SAR Single Look Complex images were collected spanning October 2017 to January 2018. The local meteorological conditions, precipitation in particular, were taken into consideration as the humidity levels could affect the radar scattering properties. Six coherence images were calculated using as master image the 16th October 2017 acquisition and as slave images the other dates. The coherence products were then used for a multitemporal interferometric coherence analysis through the calculation of the average coherence map. The average coherence map showed that Sentinel-1 data seems to well delimitate the hydrothermal alteration area according to the geologic map of Nisyros. Comparing the hydrothermal alteration with the loose alluvial deposits within the caldera, the hydrothermal altered products showed high coherence values while the loose alluvial products low coherence values. The preliminary results show the potential of Sentinel-1 multitemporal interferometric coherence to differentiate hydrothermal alteration from volatile loose formations such as alluvial deposits.

EMG Rectification Is Detrimental for Identifying Abnormalities in Corticomuscular and Intermuscular Coherence in Spinocerebellar Ataxia Type 2.

Corticomuscular and intermuscular coherence (CMC, IMC) reflect connectivity between neuronal activity in the motor cortex measured by electroencephalography (EEG) and muscular activity measured by electromyography (EMG), or between activity in different muscles, respectively. There is an ongoing debate on the appropriateness of EMG rectification prior to coherence estimation. This work examines the effects of EMG rectification in CMC and IMC estimation in 20 spinocerebellar ataxia type 2 (SCA2) patients, 16 prodromal SCA2 gene mutation carriers, and 26 healthy controls during a repetitive upper or lower limb motor task. Coherence estimations were performed using the non-rectified raw EMG signal vs. the rectified EMG signal. EMG rectification decreases the level of significance of lower beta-frequency band CMC and IMC values in SCA2 patients and prodromal SCA2 mutation carriers vs. healthy controls, and also results in overall lower coherence values. EMG rectification is detrimental for beta-frequency band CMC and IMC estimation. One likely reason for this effect is distortion of coherence estimation in high-frequency signals, where the level of amplitude cancelation is high.

OSARIS, the “Open Source SAR Investigation System” for Automatized Parallel InSAR Processing of Sentinel-1 Time Series Data With Special Emphasis on Cryosphere Applications

With the advent of the two Sentinel-1 (S1) satellites, Synthetic Aperture Radar (SAR) data with high temporal and spatial resolution is freely available. This provides a promising framework to facilitate detailed investigations of surface instabilities and movements on large scales with high temporal resolution, but also poses substantial processing challenges because of storage and computation requirements. Here we present OSARIS, the ‘Open Source SAR Investigation System’, as a framework to process large stacks of S1 data on High-Performance Computing (HPC) clusters. Based on GMTSAR, shell scripts, and the workload manager Slurm, OSARIS provides an open and modular framework combining parallelization of high-performance C programs, flexibility of processing schemes, convenient configuration, and generation of geocoded stacks of analysis-ready base data, including amplitude, phase, coherence, and unwrapped interferograms. Time series analyses can be conducted by applying automated modules to the data stacks. The capabilities of OSARIS are demonstrated in a case study from the northwestern Tien Shan, Central Asia. After merging of slices, a total of 65 scene pairs were processed from 150 total input scenes. OSARIS processing yields a comprehensive set of interferometric data for each pair, including amplitude, coherence, unwrapped interferometric phase, and line-of-sight displacement (LOSD). The coherence time series exhibits pronounced seasonal variability, with relatively high coherence values prevailing during the summer months in the nival zone. As an example of a time series analysis module, we present OSARIS’ ‘Unstable Coherence Metric’ (UCM) which identifies pixels affected by significant drops from high to low coherence values. Surface changes along moraine ridges, steep slopes, and several gullies during July and August were observed. Measurements of motion provided by LOSD measurements require careful evaluation because interferometric phase unwrapping is prone to errors. Here, OSARIS provides a series of modules to detect and mask unwrapping errors, correct for atmospheric disturbances, and remove large-scale trends. Wall clock processing time for the case study (area ~9,000 km²) was ~9h:47m on a machine with 320 cores and 1536 GB RAM. In total, ~11d:08h:28m were saved through parallelization. OSARIS thus allows to implement S1-based region-wide investigations of surface movement events over multiple years.

Polarimetric Interferometric SAR Change Detection Discrimination

A coherent change detection (CCD) image, computed from a geometrically matched, temporally separated pair of complex-valued synthetic aperture radar (SAR) image sets, conveys the pixel-level equivalence between the two observations. Low-coherence values in a CCD image are typically due to either some physical change in the corresponding pixels or a low signal-to-noise observation. A CCD image does not directly convey the nature of the change that occurred to cause low coherence. In this paper, we introduce a mathematical framework for discriminating between different types of change within a CCD image. We utilize the extra degrees of freedom and information from polarimetric interferometric SAR (PolInSAR) data and PolInSAR processing techniques to define a 29-dimensional feature vector that contains information capable of discriminating between different types of change in a scene. We also propose two change-type discrimination functions that can be trained with feature vector training data and demonstrate change-type discrimination on an example image set for three different types of change. Furthermore, we also describe and characterize the performance of the two proposed change-type discrimination functions by way of receiver operating characteristic curves, confusion matrices, and pass matrices.

Using InSAR Coherence for Investigating the Interplay of Fluvial and Aeolian Features in Arid Lands: Implications for Groundwater Potential in Egypt

Despite the fact that the Sahara is considered the most arid region on Earth, it has witnessed prolonged fluvial and aeolian depositional history, and might harbor substantial fresh groundwater resources. Its ancient fluvial surfaces are, however, often concealed by aeolian deposits, inhibiting the discovery and mapping of potential groundwater recharge areas. However, recent advances in synthetic aperture radar (SAR) imaging offer a novel approach for detecting partially hidden and dynamic landscape features. Interferometry SAR coherence change detection (CCD) is a fairly recent technique that allows the mapping of very slight surface changes between multidate SAR images. Thus, this work explores the use of the CCD method to investigate the fluvial and aeolian morphodynamics along two paleochannels in Egypt. The results show that during wetter climates, runoff caused the erosion of solid rocks and the rounding of sand-sized grains, which were subsequently deposited in depressions further downstream. As an alternating dry climate prevailed, the sand deposits were reshaped into migrating linear dunes. These highly dynamic features are depicted on the CCD image with very low coherence values close to 0 (high change), while the deposits within the associated ephemeral wadis show low to moderate coherence values ranging from 0.2 to 0.4 (high to moderate change), and the country rocks show a relative absence of change with high coherence values close to 1. These linear dunes crossed their parent’s stream courses and dammed the runoff to form lakes during rainy seasons. Part of the dammed surface water would have infiltrated the ground to recharge the permeable wadi deposits. The alternation of fluvial and aeolian depositional environments produced unique hydromorphometrically trapped lakes that are very rare in arid regions, but of great interest because of their significance to groundwater recharge.

Functional inter-cortical connectivity among motor-related cortices during motor imagery: A magnetoencephalographic study

Neural connectivity was measured during motor imagery (MI) and motor execution (ME) using magnetoencephalography in nine healthy subjects, MI, and at rest. Lower coherence values during ME and MI between sensorimotor areas than at rest, and lower values during MI between the left supplementary motor area and inferior frontal gyrus than ME suggested the sensorimotor network of MI functioned with similar connectivity to ME and that the inhibitory activity functioned continuously during MI, respectively.

Muscle fatigue increases beta-band coherence between the firing times of simultaneously active motor units in the first dorsal interosseous muscle.

Synchronization between the firing times of simultaneously active motor units (MUs) is generally assumed to increase during fatiguing contractions. To date, however, estimates of MU synchronization have relied on indirect measures, derived from surface electromyographic (EMG) interference signals. This study used intramuscular coherence to investigate the correlation between MU discharges in the first dorsal interosseous muscle during and immediately following a submaximal fatiguing contraction, and after rest. Coherence between composite MU spike trains, derived from decomposed surface EMG, were examined in the delta (1-4 Hz), alpha (8-12 Hz), beta (15-30 Hz), and gamma (30-60 Hz) frequency band ranges. A significant increase in MU coherence was observed in the delta, alpha, and beta frequency bands postfatigue. In addition, wavelet coherence revealed a tendency for delta-, alpha-, and beta-band coherence to increase during the fatiguing contraction, with subjects exhibiting low initial coherence values displaying the greatest relative increase. This was accompanied by an increase in MU short-term synchronization and a decline in mean firing rate of the majority of MUs detected during the sustained contraction. A model of the motoneuron pool and surface EMG was used to investigate factors influencing the coherence estimate. Simulation results indicated that changes in motoneuron inhibition and firing rates alone could not directly account for increased beta-band coherence postfatigue. The observed increase is, therefore, more likely to arise from an increase in the strength of correlated inputs to MUs as the muscle fatigues.

InSAR 긴밀도 영상을 이용한 남극 장보고기지 인근 정착해빙의 연간 변화 분석

Landfast sea ice (LFI) in Terra Nova Bay, East Antarctica where the Jangbogo Antarctic Research Station is located, has significant influences on marine ecosystem and the sailing of an icebreaker. Therefore, it is essential to analyze the spatio-temporal variation of the LFI in Terra Nova Bay. In this study, we chose interferometric pairs with the temporal baseline from 1 to 9 days out of a total of 62 COSMO–SkyMed synthetic aperture radar (SAR) images over Terra Nova Bay obtained from December 2010 to January 2012, and then constructed the coherence image of each pair. The LFI showed coherence values higher than 0.3 even in the interferometric SAR (InSAR) pairs of up to 9-days of temporal baseline. This was because the LFI was fixed at coastline and thus showed low temporal phase decorrelation. Based on the characteristics of the coherence on LFI, We defined the areas of LFI that show spatially homogeneous coherence values higher than 0.3. Pack ice (PI) and open water showed coherence values lower than 0.3 due to large temporal phase decorreation caused by current and wind. Distinguishing PI from open water in the coherence images was difficult due to their similarly low coherence values. PI was identified in SAR amplitude images by investigating cracks on the ice. The extents of the LFI and PI were estimated from the coherence and SAR amplitude images and their temporal variations were analyzed. The extent of the LFI increased from March to July (maximum extent of 170.7 km2) and decreased from October. The extent of the PI increased from February to May and decreased from May to July when the LFI increases dramatically. The extent of the LFI and air temperature showed an inverse correlation with a time lag of about 2 months, i.e., the extent of the LFI decreases after 2 months of the increase in the air temperature. Meanwhile the correlation between wind speed and the extent of the LFI was very low. This represents that the extent of LFI in Terra Nova Bay are influenced more by the air temperature than wind speed.

Man-Made Change Detection Using High-Resolution Cosmo-SkyMed SAR Interferometry

The technique of coherent change detection (CCD) in repeat-pass synthetic aperture radar (SAR) imagery has the potential to detect very subtle scene changes. With the advent of high-resolution space-borne SAR images, CCD has received a lot of interest. In CCD, the interferometric coherence is evaluated and analyzed to detect changes. The sample coherence estimator is biased, especially for low-coherence values, which complicates CCD map analysis and interpretation. The bias decreases when the number of samples used to evaluate the coherence increases. However, a large number of samples causes the loss of the small size changes. In this work, an adaptive method for coherence estimation is described and evaluated. The local fringe frequency is used as an additional change indicator to adapt the number of samples used to estimate the coherence. The results obtained using Cosmo-SkyMed (CSK) SAR images indicate that the proposed adaptive method improves detection performance while preserving the small size changes.

Evaluation of TanDEM-X elevation data for geomorphological mapping and interpretation in high mountain environments — A case study from SE Tibet, China

Digital elevation models (DEMs) are a prerequisite for many different applications in the field of geomorphology. In this context, the two near-global medium resolution DEMs originating from the SRTM and ASTER missions are widely used. For detailed geomorphological studies, particularly in high mountain environments, these datasets are, however, known to have substantial disadvantages beyond their posting, i.e., data gaps and miscellaneous artifacts. The upcoming TanDEM-X DEM is a promising candidate to improve this situation by application of state-of-the-art radar technology, exhibiting a posting of 12m and less proneness to errors. In this study, we present a DEM processed from a single TanDEM-X CoSSC scene, covering a study area in the extreme relief of the eastern Nyainqêntanglha Range, southeastern Tibet. The potential of the resulting experimental TanDEM-X DEM for geomorphological applications was evaluated by geomorphometric analyses and an assessment of landform cognoscibility and artifacts in comparison to the ASTER GDEM and the recently released SRTM 1″ DEM. Detailed geomorphological mapping was conducted for four selected core study areas in a manual approach, based exclusively on the TanDEM-X DEM and its basic derivates. The results show that the self-processed TanDEM-X DEM yields a detailed and widely consistent landscape representation. It thus fosters geomorphological analysis by visual and quantitative means, allowing delineation of landforms down to footprints of ~30m. Even in this premature state, the TanDEM-X elevation data are widely superior to the ASTER and SRTM datasets, primarily owing to its significantly higher resolution and its lower susceptibility to artifacts that hamper landform interpretation. Conversely, challenges toward interferometric DEM generation were identified, including (i) triangulation facets and missing topographic information resulting from radar layover on steep slopes facing toward the radar sensor, (ii) low coherence values on leeward slopes, (iii) decorrelation effects over water bodies, and (iv) challenges for phase unwrapping in settings of strong topographic contrasts. There is, however, a high probability that these drawbacks can be overcome by applying multiple interferograms exhibiting different perpendicular baselines as planned for the generation of the final TanDEM-X DEM product.

High-density surface electromyography improves the identification of oscillatory synaptic inputs to motoneurons

Many studies have addressed corticomuscular coherence (CMC), but broad applications are limited by low coherence values and the variability across subjects and recordings. Here, we investigated how the use of high-density surface electromyography (HDsEMG) can improve the detection of CMC. Sixteen healthy subjects performed isometric contractions at six low-force levels using a pinch-grip, while HDsEMG of the adductor pollicis transversus and flexor and abductor pollicis brevis and whole-head magnetoencephalography were recorded. Different configurations were constructed from the HDsEMG grid, such as a bipolar and Laplacian montage, as well as a montage based on principal component analysis (PCA). CMC was estimated for each configuration, and the strength of coherence was compared across configurations. As expected, performance of the precision-grip task resulted in significant CMC in the β-frequency band (16-26 Hz). Compared with a bipolar EMG montage, all multichannel configurations obtained from the HDsEMG grid revealed a significant increase in CMC. The configuration, based on PCA, showed the largest (37%) increase. HDsEMG did not reduce the between-subject variability; rather, many configurations showed an increased coefficient of variation. Increased CMC presumably reflects the ability of HDsEMG to counteract inherent EMG signal factors-such as amplitude cancellation-which impact the detection of oscillatory inputs. In contrast, the between-subject variability of CMC most likely has a cortical origin.

Interannual variability of Indian summer monsoon rainfall onset date at local scale

AbstractVariability of Indian summer monsoon local‐scale onset dates is investigated at 1° spatial resolution by applying an agronomic definition (i.e. the first significant rains without a potentially crop‐threatening dry spell thereafter) to gridded observed daily rainfall data (1901–2004). Median onset dates are compared well with previous estimates. The inter‐quartile range varies from less than 2 weeks over the monsoon zone and Western Ghats, to about a month over the northwestern desert. A relationship between local‐scale rainfall onset date and regional‐scale indices of onset is demonstrated over the eastern part of the monsoon zone (i.e. approximately from Orissa and eastern Maharashtra to Western Bengal) and along the coast of the Arabian Sea; these regions are also found to exhibit significant correlations with Niño 3.4 sea surface temperature index in May. However, these relationships are found to be rather weak and geographically confined, which is attributed to low values of spatial coherence of local‐scale onset date variability. A weak relationship is also found between local‐scale onset date and the June–September seasonal rainfall total, while the latter is shown to be weakly more spatially coherent than local‐scale onset date over the monsoon zone, and thus slightly more potentially predictable.

IMPROVING CCD PERFORMANCE BY THE USE OF LOCAL FRINGE FREQUENCIES

Coherent Change Detection (CCD) using multi-temporal Synthetic Aperture Radar (SAR) is one of the most important applications of remote sensing technology. With the advent of high- resolution SAR images, CCD has received a lot of attention. In CCD, the interferometric coherence between two SAR images is evaluated and analyzed to detect surface changes. Unfortunately, the sample coherence estimator is biased, especially for low-coherence values. The consequence of this bias is the apparition of highly coherent pixels inside the changed area. Within this context, the detection performance will considerably degrade, particularly when using high resolution SAR data. In this paper, we propose a new CCD method based on cleaning of coherence inside changed areas, which is characterized by high Local Fringe Frequencies (LFF) values, followed by a space-averaged coherence method. According to the proposed method, the results obtained with Cosmo-SkyMed (CSK) SAR data show an enhancement of change detection performance of about 6% while preserving subtle changes.

Spatial synchronization of the θ and α band cortical electrical oscillations in the formation of a set to an angry face expression in 5- to 11-year-old children

The θ and α band EEG coherence during forming and testing sets to an emotionally negative facial expression (angry) was studied in five- to six-year-old (n = 18) and 10- to 11-year-old (n = 25) children. Younger children displayed lower coherence values, especially in the right hemisphere, than older ones. Differences in θ- and α band coherence in the cases of rigid and flexible sets to an angry face expression were also revealed. With relatively rigid forms of cognitive activity, the EEG coherence values were lower. A correlation between the electrophysiological and behavioral data was established. It supports the hypothesis that two functional brain integration systems, the corticohippocampal and the frontothalamic ones, play a role in the processes of facial expression recognition and provide cognition flexibility.