Problem Given the number of times in which an unknown event has happened and failed: Required the chance that the probability of its happening in a single trial lies somewhere between any two degrees of probability that can be named. SECTION 1 Definition 1. Several events are inconsistent, when if one of them happens, none of the rest can. 2. Two events are contrary when one, or other of them must; and both together cannot happen. 3. An event is said to fail, when it cannot happen; or, which comes to the same thing, when its contrary has happened. 4. An event is said to be determined when it has either happened or failed. 5. The probability of any event is the ratio between the value at which an expectation depending on the happening of the event ought to be computed, and the value of the thing expected upon it’s 2 happening.

An essay towards solving a problem in the doctrine of chances. [Facsimil]

When exploited in remote sensing analysis, a reliable change rule with transfer ability can detect changes accurately and be applied widely. However, in practice, the complexity of land cover changes makes it difficult to use only one change rule or change feature learned from a given multi-temporal dataset to detect any other new target images without applying other learning processes. In this study, we consider the design of an efficient change rule having transferability to detect both binary and multi-class changes. The proposed method relies on an improved Long Short-Term Memory (LSTM) model to acquire and record the change information of long-term sequence remote sensing data. In particular, a core memory cell is utilized to learn the change rule from the information concerning binary changes or multi-class changes. Three gates are utilized to control the input, output and update of the LSTM model for optimization. In addition, the learned rule can be applied to detect changes and transfer the change rule from one learned image to another new target multi-temporal image. In this study, binary experiments, transfer experiments and multi-class change experiments are exploited to demonstrate the superiority of our method. Three contributions of this work can be summarized as follows: (1) the proposed method can learn an effective change rule to provide reliable change information for multi-temporal images; (2) the learned change rule has good transferability for detecting changes in new target images without any extra learning process, and the new target images should have a multi-spectral distribution similar to that of the training images; and (3) to the authors’ best knowledge, this is the first time that deep learning in recurrent neural networks is exploited for change detection. In addition, under the framework of the proposed method, changes can be detected under both binary detection and multi-class change detection.

Learning a Transferable Change Rule from a Recurrent Neural Network for Land Cover Change Detection

The Dempster-Shafer (D-S) theory has been gaining popularity in fields where incomplete knowledge is a factor. The author explores the application of the D-S theory in system reliability and safety. Inappropriate application of the D-S theory to safety-control policies can degrade plant safety. This is proven in two phases: (1) a unified combination rule for fusing information on plant states given by independent knowledge sources such as sensors or human operators is developed; and (2) combination rules cannot be chosen in an arbitrary manner, i.e., the best choice of combination rules depends on whether the safety-control policy is fault-warning or safety-preservation. >

Interdependence between safety-control policy and multiple-sensor

In this study we analyse coseismic GPS displacements and DInSAR data to constrain a dislocation model for the three largest earthquakes of the 1997 Umbria-Marche seismic sequence. The first two events, which occurred on September 26 at 00:33 GMT (Mw 5.7) and 09:40 GMT (Mw 6.0) respectively, are investigated using both GPS displacements and DInSAR interferograms. We discuss and compare the results of previous studies which separately modeled a smaller subset of geodetic data. We provide a dislocation model for these two earthquakes which fits well both GPS and DInSAR data and agrees with the results of seismological and geological investigations. The first event consists of a unilateral rupture towards the southeast with a uniform dislocation. The strike, rake and dip angles are those resulting from the CMT solution. The second event consists of an unilateral rupture towards the northwest and a variable slip distribution on the fault plane. The strike and the rake are consistent with the CMT solution, but the dip angle has been slightly modified to improve the simultaneous fit of GPS and DInSAR data. While the second rupture (09:40 GMT) arrived very close to the surface, the fit to geodetic data shows that the first rupture (00:33 GMT) is deeper (2 km), despite the more evident surface geological effects. The analysis of new SAR interferograms allows the identification of a 5–6 cm additional displacement caused by the October 3 (Mw 5.2) and 6 (Mw 5.4) seismic events.We use data from a new DInSAR interferogram to model the displacement field of the Sellano earthquake of October 14, 1997. For this event significant GPS measurements were not available. We tested two different fault plane geometries: a blind, planar fault (top depth = 2.4 km), and a curved (listric) fault reaching the surface. The two models provide a generally similar fit to the data, and show that most of the slip was released at depths greater than 2.4 km along a gently dipping (40°–45°) fault surface. They also show that a unilateral rupture does not allow fitting the interferometric fringes since there is evident surface deformation to the northwest of the hypocenter. Moreover, we suggest that the concentration of high residuals in the southern part of our uniform slip model may in fact indicate a certain slip variability in this area.We conclude that, despite the moderate magnitudes and the lack of significant surface faulting, the space geodetic data allowed to constrain dislocation models giving new insights in the rupture process of the three largest events of the sequence.

Modeling coseismic displacements resulting from SAR interferometry and

Synthetic aperture radar (SAR) is more sensitive to the dielectric properties and structure of the targets and less affected by weather conditions than optical sensors, making it more capable of detecting changes induced by management practices in agricultural fields. In this study, the capability of C-band SAR data for detecting crop seeding and harvest events was explored. The study was conducted for the 2019 growing season in Temiskaming Shores, an agricultural area in Northern Ontario, Canada. Time-series SAR data acquired by Sentinel-1 constellation with the interferometric wide (IW) mode with dual polarizations in VV (vertical transmit and vertical receive) and VH (vertical transmit and horizontal receive) were obtained. interferometric SAR (InSAR) processing was conducted to derive coherence between each pair of SAR images acquired consecutively in time throughout the year. Crop seeding and harvest dates were determined by analyzing the time-series InSAR coherence and SAR backscattering. Variation of SAR backscattering coefficients, particularly the VH polarization, revealed seasonal crop growth patterns. The change in InSAR coherence can be linked to change of surface structure induced by seeding or harvest operations. Using a set of physically based rules, a simple algorithm was developed to determine crop seeding and harvest dates, with an accuracy of 85% (n = 67) for seeding-date identification and 56% (n = 77) for harvest-date identification. The extra challenge in harvest detection could be attributed to the impacts of weather conditions, such as rain and its effects on soil moisture and crop dielectric properties during the harvest season. Other factors such as post-harvest residue removal and field ploughing could also complicate the identification of harvest event. Overall, given its mechanism to acquire images with InSAR capability at 12-day revisiting cycle with a single satellite for most part of the Earth, the Sentinel-1 constellation provides a great data source for detecting crop field management activities through coherent or incoherent change detection techniques. It is anticipated that this method could perform even better at a shorter six-day revisiting cycle with both satellites for Sentinel-1. With the successful launch (2019) of the Canadian RADARSAT Constellation Mission (RCM) with its tri-satellite system and four polarizations, we are likely to see improved system reliability and monitoring efficiency.

Detection of Crop Seeding and Harvest through Analysis of Time-Series Sentinel-1 Interferometric SAR Data

A new approach is presented to estimate the synthetic aperture radar (SAR) interferometric phase via the joint subspace projection. In this letter, a new formulation is proposed to transform the cost function minimization problem into the problem of finding the roots of a second-order polynomial whose coefficients are evaluated via the joint noise subspace. The proposed method enjoys a substantially reduced computational complexity and improves the estimation of the interferometric phase. Results obtained using real SAR data show the effectiveness of the proposed method. In the change detection application, the method offers much better separation between the changed and unchanged coherence pixels than existing filters.

InSAR Phase Filtering via Joint Subspace Projection Method: Application in Change Detection

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.

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

La multiplication des satellites de teledetection et l�utilisation de plusieurs capteurs pour l�observation de la terre ont permis l�acquisition d�une multitude d�images presentant des caracteristiques spatiale, spectrale et temporelle differentes L�extraction des informations utiles, liees a la nature physique des surfaces observees, fait appel a differentes techniques, approches et methodes de traitement d�images numeriques Parmi ces procedures figure la fusion de donnees Cette methode permet d�exploiter le caractere redondant et complementaire contenu dans les donnees satellitaires et doit prendre en compte des sources d�information de plus en plus nombreuses et variees

Développement de modèles de fusion et de classification contextuelle d'images satellitaires par la théorie de l'évidence et la théorie du raisonnement plausible et paradoxal

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.

https://www.jpier.org/pierc/pierc32/10.12070305.pdf

Aichouche Belhadj-Aissa

Papers

InSAR Phase Filtering via Joint Subspace Projection Method: Application in Change Detection

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

Développement de modèles de fusion et de classification contextuelle d'images satellitaires par la théorie de l'évidence et la théorie du raisonnement plausible et paradoxal

Improving Ccd Performance by the Use of Local Fringe Frequencies

A novel interpolation method for InSAR atmospheric wet delay correction