Multitemporal SAR and polarimetric SAR optimization and classification: Reinterpreting temporal coherence

In multitemporal synthetic aperture radar (SAR) and polarimetric SAR (PolSAR), 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.This work was supported in part by the National Natural Science Foundation of China under Grant 61871413, in part by the China Scholarship Council under Grant 2020006880033, and in part by the Project INTERACT funded by the Spanish MCIN/AEI/10.13039/501100011033 under Grant PID2020-114623RB-C32.Peer ReviewedPostprint (author's final draft

Multiple positive solutions to elliptic boundary blow-up problems

We prove the existence of multiple positive radial solutions to the sign-indefinite elliptic boundary blow-up problem $$\left\{\begin{array}{ll} \Delta u + \bigl(a^+(\vert x \vert) - \mu a^-(\vert x \vert)\bigr) g(u) = 0, & \; \vert x \vert < 1, \\ u(x) \to \infty, & \; \vert x \vert \to 1, \end{array} \right.$$ where $g$ is a function superlinear at zero and at infinity, $a^+$ and $a^-$ are the positive/negative part, respectively, of a sign-changing function $a$ and $\mu > 0$ is a large parameter. In particular, we show how the number of solutions is affected by the nodal behavior of the weight function $a$. The proof is based on a careful shooting-type argument for the equivalent singular ODE problem. As a further application of this technique, the existence of multiple positive radial homoclinic solutions to $$\Delta u + \bigl(a^+(\vert x \vert) - \mu a^-(\vert x \vert)\bigr) g(u) = 0, \qquad x \in \mathbb{R}^N,$$ is also considered

Crop classification of multitemporal PolSAR based on 3-D attention module with ViT

Multitemporal polarimertic SAR is considered to be very effective in crop classification and cultivated land detection, which has received much attention from researchers. Currently, for most multitemporal polarimetric SAR data classification methods, the simultaneous temporal–polarimetric–spatial feature extraction capability has not been exploited sufficiently. Also, the diversity of different time and different polarimetric features has not been taken into account sufficiently. In this letter, we propose a classification model that combines a dual-stream network as a temporal–polarimetric–spatial feature extraction module with vision transformer (ViT) called temporal–polarimetric–spatial transformer (TPST) to address the above problems. Second, a 3-D convolutional attention module that enables the network to weight the temporal dimension, polarimetric feature dimension and spatial dimension is developed, according to their importance. Experimental results on both the UAVSAR and RADARSAT-2 datasets show that the proposed method outperforms ResNet.This work was supported by the National Natural Science Foundation of China under Grant 62201027 and Grant 62271034.Peer ReviewedPostprint (author's final draft

DNN-based PolSAR image classification on noisy labels

Deep neural networks (DNNs) appear to be a solution for the classification of polarimetric synthetic aperture radar (PolSAR) data in that they outperform classical supervised classifiers under the condition of sufficient training samples. The design of a classifier is challenging because DNNs can easily overfit due to limited remote sensing training samples and unavoidable noisy labels. In this article, a softmax loss strategy with antinoise capability, namely, the probability-aware sample grading strategy (PASGS), is developed to overcome this limitation. Combined with the proposed softmax loss strategy, two classical DNN-based classifiers are implemented to perform PolSAR image classification to demonstrate its effectiveness. In this framework, the difference distribution implicitly reflects the probability that a training sample is clean, and clean labels can be distinguished from noisy labels according to the method of probability statistics. Then, this probability is employed to reweight the corresponding loss of each training sample during the training process to locate the noisy data and to prevent participation in the loss calculation of the neural network. As the number of training iterations increases, the condition of the probability statistics of the noisy labels will be constantly adjusted without supervision, and the clean labels will eventually be identified to train the neural network. Experiments on three PolSAR datasets with two DNN-based methods also demonstrate that the proposed method is superior to state-of-the-art methods.This work was supported in part by the National Natural Science Foundation of China under Grant 61871413 and Grant 61801015, in part by the Fundamental Research Funds for the Central Universities under Grant XK2020-03, in part by China Scholarship Council under Grant 2020006880033, and in part by Grant PID2020-114623RB-C32 funded by MCIN/AEI/10.13039/501100011033.Peer ReviewedPostprint (published version

Zircon to monazite phase transition in CeVO4

X-ray diffraction and Raman-scattering measurements on cerium vanadate have been performed up to 12 and 16 GPa, respectively. Experiments reveal that at 5.3 GPa the onset of a pressure-induced irreversible phase transition from the zircon to the monazite structure. Beyond this pressure, diffraction peaks and Raman-active modes of the monazite phase are measured. The zircon to monazite transition in CeVO4 is distinctive among the other rare-earth orthovanadates. We also observed softening of external translational Eg and internal B2g bending modes. We attributed it to mechanical instabilities of zircon phase against the pressure-induced distortion. We additionally report lattice-dynamical and total-energy calculations which are in agreement with the experimental results. Finally, the effect of non-hydrostatic stresses on the structural sequence is studied and the equations of state of different phases are reported.Comment: 45 pages, 8 figures, 8 table

KalmanNet:Neural Network Aided Kalman Filtering for Partially Known Dynamics

Real-time state estimation of dynamical systems is a fundamental task in signal processing and control. For systems that are well-represented by a fully known linear Gaussian state space (SS) model, the celebrated Kalman filter (KF) is a low complexity optimal solution. However, both linearity of the underlying SS model and accurate knowledge of it are often not encountered in practice. Here, we present KalmanNet, a real-time state estimator that learns from data to carry out Kalman filtering under non-linear dynamics with partial information. By incorporating the structural SS model with a dedicated recurrent neural network module in the flow of the KF, we retain data efficiency and interpretability of the classic algorithm while implicitly learning complex dynamics from data. We numerically demonstrate that KalmanNet overcomes nonlinearities and model mismatch, outperforming classic filtering methods operating with both mismatched and accurate domain knowledge.</p

Female brain size affects the assessment of male attractiveness during mate choice

Mate choice decisions are central in sexual selection theory aimed to understand how sexual traits evolve and their role in evolutionary diversification. We test the hypothesis that brain size and cognitive ability are important for accurate assessment of partner quality and that variation in brain size and cognitive ability underlies variation in mate choice. We compared sexual preference in guppy female lines selected for divergence in relative brain size, which we have previously shown to have substantial differences in cognitive ability. In a dichotomous choice test, large-brained and wild-type females showed strong preference for males with color traits that predict attractiveness in this species. In contrast, small-brained females showed no preference for males with these traits. In-depth analysis of optomotor response to color cues and gene expression of key opsins in the eye revealed that the observed differences were not due to differences in visual perception of color, indicating that differences in the ability to process indicators of attractiveness are responsible. We thus provide the first experimental support that individual variation in brain size affects mate choice decisions and conclude that differences in cognitive ability may be an important underlying mechanism behind variation in female mate choice

Raman Spectroscopy of magneto-phonon resonances in Graphene and Graphite

The magneto-phonon resonance or MPR occurs in semiconductor materials when the energy spacing between Landau levels is continuously tuned to cross the energy of an optical phonon mode. MPRs have been largely explored in bulk semiconductors, in two-dimensional systems and in quantum dots. Recently there has been significant interest in the MPR interactions of the Dirac fermion magnetoexcitons in graphene, and a rich splitting and anti-crossing phenomena of the even parity E2g long wavelength optical phonon mode have been theoretically proposed and experimentally observed. The MPR has been found to crucially depend on disorder in the graphene layer. This is a feature that creates new venues for the study of interplays between disorder and interactions in the atomic layers. We review here the fundamentals of MRP in graphene and the experimental Raman scattering works that have led to the observation of these phenomena in graphene and graphite

A rare and exclusive endoperoxide photoproduct derived from thiacalix[4]arene crown-shaped derivative bearing 9,10-substituted anthracene moiety

A rare and exclusive endoperoxide photoproduct was quantitatively obtained from a thiacalix[4]arene crown-shaped derivative upon irradiation at λ=365 nm; the structure was unambiguously confirmed by 1H/13C NMR spectroscopy and X-ray crystallography. The prerequisites for the formation of the endoperoxide photoproduct have also been discussed. Furthermore, the photochemical reaction rate could be greatly enhanced in the presence of the thiacalix[4]arene platform because it served as a host to capture oxygen

Fluvial transport of suspended sediment and organic carbon during flood events in a large agricultural catchment in southwest France.

Water draining from a large agricultural catchment of 1 110 km2 in southwest France was sampled over an 18-month period to determine the temporal variability in suspended sediment (SS) and dissolved (DOC) and particulate organic carbon (POC) transport during flood events, with quantification of fluxes and controlling factors, and to analyze the relationships between discharge and SS, DOC and POC. A total of 15 flood events were analyzed, providing extensive data on SS, POC and DOC during floods. There was high variability in SS, POC and DOC transport during different seasonal floods, with SS varying by event from 513 to 41 750 t; POC from 12 to 748 t and DOC from 9 to 218 t. Overall, 76 and 62% of total fluxes of POC and DOC occurred within 22% of the study period. POC and DOC export from the Save catchment amounted to 3090 t and 1240 t, equivalent to 1·8 t km−2 y−1 and 0·7 t km−2 y−1, respectively. Statistical analyses showed that total precipitation, flood discharge and total water yield were the major factors controlling SS, POC and DOC transport from the catchment. The relationships between SS, POC and DOC and discharge over temporal flood events resulted in different hysteresis patterns, which were used to deduce dissolved and particulate origins. In both clockwise and anticlockwise hysteresis, POC mainly followed the same patterns as discharge and SS. The DOC-discharge relationship was mainly characterized by alternating clockwise and anticlockwise hysteresis due to dilution effects of water originating from different sources in the whole catchment