In multitemporal SAR and Polarimetric SAR (Pol-SAR) coherence is a capital parameter to exploit common information between temporal acquisitions. Yet, its use is limited to high coherences. This article proposes the analysis of low coherence scenarios by introducing a reinterpretation of coherence. It is demonstrated that coherence results from the product of two terms accounting for coherent and radiometric changes, respectively. For low coherences, the first term presents low values, preventing its exploitation for information retrieval. The information provided by the second term can be used in these circumstances to exploit common information. This second term is proposed, as an alternative to coherence, for information retrieval for low coherences. Besides, it is shown that polarimetry allows the temporal optimization of its values. To prove the benefits of this approach, multitemporal SAR and PolSAR data classification is considered as a tool, showing that improvements of the classification overall accuracy may range between 20% and 50%, compared to classification based on coherence.

Differential Interferometric Synthetic Aperture Radar (DInSAR), also known as Persistent Scatters Interferometry (PSI), has proved its unprecedented advantages of monitoring ground deformation on large scale with centimeter to millimeter precision in the last two decades. However, the reliability and accuracy are often contaminated with atmospheric artefacts caused by spatial and temporal variations of the atmosphere. Recent studies revealed atmospheric artefacts can be compensated with empirical models, GPS zenith path delay and numerical weather prediction models. In this paper, an improved methodology is proposed based on atmospheric reanalysis data to estimate atmospheric artefacts. With our approach, the realistic line of sight (LOS) path along satellite location and monitored points is considered, rather than the zenith path delay. The effectiveness of our method is validated over Tenerife island, Spain by using Sentinel 1 datasets.

Mitigating the atmospheric phase delay is one of the largest challenges faced by the differential synthetic aperture radar (SAR) interferometry community. Recently, many publications have studied correcting the stratified tropospheric phase delay by assuming a linear model between them and the topography. However, most of these studies have not considered the effect of turbulent atmospheric artifacts when adjusting the linear model to data. In this paper, we present an improved technique that minimizes the influence of the turbulent atmosphere in the model adjustment. In the proposed algorithm, the model is adjusted to the phase differences of pixels instead of using the unwrapped phase of each pixel. In addition, the different phase differences are weighted as a function of its atmospheric phase screen covariance estimated from an empirical variogram to reduce, in the model adjustment, the impact of pixel pairs with a significant turbulent atmosphere. The good performance of the proposed method has been validated with both the simulated and real Sentinel-1A SAR data in the mountainous area of Tenerife island, Spain.

Differential Interferometry SAR (DInSAR) is an advanced technique to retrieve ground deformation in the geoscience community. Atmospheric Phase Screen (APS) is one of the largest challenges limiting the application of DInSAR especially in mountainous areas. In this paper we propose an approach based on an empirical linear model for compensating stratified atmospheric phase delay in differential interferograms. Compared with conventional empirical linear model, the influence of turbulent APS is taken into consideration in our work. With this, the modelled stratified APS is more accurate. We test our algorithm using Envisat dataset and Sentinel dataset over Tenerife island (Spain). The performance of the approach is compared with Global Atmospheric Models (GAMs).

One of the main applications of SAR interferometry is the monitoring of hazard risks related with urban subsidence and landslide instabilities. The different sensors available offer different carrier frequencies, image swaths and resolutions. Depending on the extension of the area and phenomena to monitor, one particular mode can be more suitable than others. In this paper the benefits of high-resolution X-band for landslide monitoring is addressed. The selected mode is the experimental Staring-Spotlight of TerraSAR/TanDEM-X and the area under study the landslide of El Forn de Canillo (Andorra) that is perfectly oriented for orbital DInSAR processing. The paper demonstrates the benefits of high resolution data and the processing challenges that have to be addressed.

In this paper, the process of evacuation in teaching building is considered. The concept of steady degree based on cellular automata and potential field is introduced and it can describe the behavior tendency of evacuees during the evacuation process. With the help of steady degree, the model simulates the indoor evacuation behavior. To reduce the congestion and evacuation time, a multi-objective optimization model considering steady degree and evacuation clearance time is proposed. Finally, an experiment in the Teaching Building No.1 of Wuhan University of Technology is carried out. The results show that this model can reduce the clearance time of emergency evacuation in teaching building compared to other models.

A self-adaptive hybrid differential evolution with simulated annealing algorithm, termed SaDESA, is proposed. In the novel SaDESA, the choice of learning strategy and several critical control parameters are not required to be pre-specified. During evolution, the suitable learning strategy and parameters setting are gradually self-adapted according to the learning experience. The performance of the SaDESA is evaluated on the set of 25 benchmark functions provided by CEC2005 special session on real parameter optimization. Comparative study exposes the SaDESA algorithm as a competitive algorithm for a global optimization.