Deep Hashing Based on Class-Discriminated Neighborhood Embedding

Deep-hashing methods have drawn significant attention during the past years in the field of remote sensing (RS) owing to their prominent capabilities for capturing the semantics from complex RS scenes and generating the associated hash codes in an end-to-end manner. Most existing deep-hashing methods exploit pairwise and triplet losses to learn the hash codes with the preservation of semantic-similarities which require the construction of image pairs and triplets based on supervised information (e.g., class labels). However, the learned Hamming spaces based on these losses may not be optimal due to an insufficient sampling of image pairs and triplets for scalable RS archives. To solve this limitation, we propose a new deep-hashing technique based on the class-discriminated neighborhood embedding, which can properly capture the locality structures among the RS scenes and distinguish images class-wisely in the Hamming space. An extensive experimentation has been conducted in order to validate the effectiveness of the proposed method by comparing it with several state-of-the-art conventional and deep-hashing methods. The related codes of this article will be made publicly available for reproducible research by the community

Projection of land surface temperature considering the effects of future land change in the Taihu Lake Basin of China

Land surface temperature (LST) is an important environmental parameter that is significantly affected by land use and landscape composition. Despite the recent progress in LST retrieval algorithms and better knowledge of the relationship between LST and land coverage indices, predictive studies of future LST patterns are limited. Here, we project LST patterns in the Taihu Lake Basin to the year 2026 based on projected land use pattern and simulated land coverage indices that include normalized difference built-up index (NDBI), normalized difference vegetation index (NDVI) and normalized difference water index (NDWI). We derived the spatiotemporal LST patterns in the Taihu Lake Basin from 1996 to 2026 using thermal infrared data from Landsat imagery. A CA-Markov model was applied to project the 2026 land use pattern in the basin based on spatial driving factors, using the 2004 land use as the initial state. We simulated the NDBI, NDVI and NDWI indices for 2026 using the projected land use patterns, and then generated the 2026 LST in the study area. Our results showed that LST has been increasing and the warming areas have been expanding since 1996, especially in the Su-Xi-Chang urban agglomeration. The mean LST in Su-Xi-Chang has increased from 31 degrees C in 2004 and has risen to about 33 degrees C in 2016, and the projection suggests that LST will reach about 35 degrees C in 2026. Our results also suggest that mean LST increased by 2 degrees C per decade in this highly urbanized area between 1996 and 2026. We present a preliminary method to produce future LST patterns and provide reasonable LST scenarios in the Taihu Lake Basin, which should help develop and implement management strategies for mitigating the effects of urban heat island

Deep Metric Learning Based on Scalable Neighborhood Components for Remote Sensing Scene Characterization

With the development of convolutional neural networks (CNNs), the semantic understanding of remote sensing (RS) scenes has been significantly improved based on their prominent feature encoding capabilities. While many existing deep-learning models focus on designing different architectures, only a few works in the RS field have focused on investigating the performance of the learned feature embeddings and the associated metric space. In particular, two main loss functions have been exploited: the contrastive and the triplet loss. However, the straightforward application of these techniques to RS images may not be optimal in order to capture their neighborhood structures in the metric space due to the insufficient sampling of image pairs or triplets during the training stage and to the inherent semantic complexity of remotely sensed data. To solve these problems, we propose a new deep metric learning approach, which overcomes the limitation on the class discrimination by means of two different components: 1) scalable neighborhood component analysis (SNCA) that aims at discovering the neighborhood structure in the metric space and 2) the cross-entropy loss that aims at preserving the class discrimination capability based on the learned class prototypes. Moreover, in order to preserve feature consistency among all the minibatches during training, a novel optimization mechanism based on momentum update is introduced for minimizing the proposed loss. An extensive experimental comparison (using several state-of-the-art models and two different benchmark data sets) has been conducted to validate the effectiveness of the proposed method from different perspectives, including: 1) classification; 2) clustering; and 3) image retrieval. The related codes of this article will be made publicly available for reproducible research by the community

A Geostatistical Filter for Remote Sensing Image Enhancement

In this paper, a new method was investigated to enhance remote sensing images by alleviating the point spread function (PSF) effect. The PSF effect exists ubiquitously in remotely sensed imagery. As a result, image quality is greatly affected, and this imposes a fundamental limit on the amount of information captured in remotely sensed images. A geostatistical filter was proposed to enhance image quality based on a downscaling-then-upscaling scheme. The difference between this method and previous methods is that the PSF is represented by breaking the pixel down into a series of sub-pixels, facilitating downscaling using the PSF and then upscaling using a square-wave response. Thus, the sub-pixels allow disaggregation as an attempt to remove the PSF effect. Experimental results on simulated and real data sets both suggest that the proposed filter can enhance the original images by reducing the PSF effect and quantify the extent to which this is possible. The predictions using the new method outperform the original coarse PSF-contaminated imagery as well as a benchmark method. The proposed method represents a new solution to compensate for the limitations introduced by remote sensors (i.e., hardware) using computer techniques (i.e., software). The method has widespread application value, particularly for applications based on remote sensing image analysis

Effects of Altitude on Fire Smoke Diffusion in Semi-Lateral Smoke Exhaust Highway Tunnels

This paper aims to study the effects of altitude and the size of smoke outlet on fire smoke diffusion and discharge in semi-lateral smoke exhaust highway tunnels. At first, simulations of semi-lateral smoke exhaust highway tunnels were carried out in FDS (Fire Dynamics Simulator), then the distribution laws of temperature, CO concentration, smoke mass flow, and visibility in the tunnel under the conditions of different altitudes and smoke outlet areas were analyzed to figure out the effects of altitude and size of smoke outlet on fire smoke diffusion and discharge in the said tunnels. The results suggest that, in case of the same fire source power, the velocity of smoke diffusion increases with the altitude; for curves of different altitudes, the tunnel roof temperature features are basically the same, that is, the higher the altitude, the higher the temperature at the tunnel roof. When the fire source power is 20 MW, the smoke mass flow at the smoke outlet decreases with the increase of altitude, but the CO concentration grows with it, indicating that the smoke exhaust efficiency is higher in high-altitude areas. When the altitude reaches 4200 m and the fire source power is 20 MW, with the increase of smoke outlet area, the smoke discharge effect of the tunnel shows an upward trend, taking both the smoke discharge effect and economy into consideration; the smoke outlet should take a size of 4 x 3 m

Safety and efficacy of short-term dual antiplatelet therapy combined with intensive rosuvastatin in acute ischemic stroke

Objective: To investigate the safety and efficacy of short-term (7-day) Dual Antiplatelet Therapy (DAPT) with intensive rosuvastatin in Acute Ischemic Stroke (AIS). Methods: In this study, patients with AIS in the emergency department of the hospital from October 2016 to December 2019 were registered and divided into the control group (Single Antiplatelet Therapy [SAPT] + rosuvastatin) and the study group (7-day DAPT + intensive rosuvastatin) according to the therapy regimens. The generalized linear model was used to compare the National Institute of Health Stroke Scale (NIHSS) scores between the two groups during the 21-day treatment. A Cox regression model was used to compare recurrent ischemic stroke, bleeding events, Statin-Induced Liver Injury (SILI), and Statin-Associated Myopathy (SAM) between the two groups during the 90-day follow-up. Results: Comparison of NIHSS scores after 21-day treatment: NIHSS scores in the study group decreased significantly, 0.273-times as much as that in the control group (Odds Ratio [OR] 0.273; 95% Confidence Interval [95% CI] 0.208–0.359; p < 0.001). Comparison of recurrent ischemic stroke during the 90-day follow-up: The therapy of the study group reduced the risk of recurrent stroke by 65% (7.76% vs. 22.82%, Hazard Ratio [HR] 0.350; 95% CI 0.167–0.730; p = 0.005). Comparison of bleeding events: There was no statistical difference between the two groups (7.79% vs. 6.71%, HR = 1.076; 95% CI 0.424–2.732; p = 0.878). No cases of SILI and SAM were found. Conclusions: Short-term DAPT with intensive rosuvastatin effectively relieved the clinical symptoms and significantly reduced the recurrent stroke for patients with mild-to-moderate AIS within 90 days, without increasing bleeding events, SILI and SAM

The application of improved signal summing method into the spacecraft force limited vibration test

This paper provides an improved signal summing method for the spacecraft force limited vibration test system with eight force transducers. The key point for this method is to change the combination way of the signals coming out of the eight force transducers while the formulas inside the signal conditioning amplifier have been used skillfully. This method had been successfully adopted in the spacecraft force limited vibration test and the accuracy requirements of key force and moment signals have been met. And this method has been proved to be a very powerful tool for providing the critical force and moment data used to determine the force limited profile during the spacecraft dynamic test

Refining the shallow slip deficit

Geodetic slip inversions for three major (M_w > 7) strike-slip earthquakes (1992 Landers, 1999 Hector Mine and 2010 El Mayor–Cucapah) show a 15–60 per cent reduction in slip near the surface (depth < 2 km) relative to the slip at deeper depths (4–6 km). This significant difference between surface coseismic slip and slip at depth has been termed the shallow slip deficit (SSD). The large magnitude of this deficit has been an enigma since it cannot be explained by shallow creep during the interseismic period or by triggered slip from nearby earthquakes. One potential explanation for the SSD is that the previous geodetic inversions lack data coverage close to surface rupture such that the shallow portions of the slip models are poorly resolved and generally underestimated. In this study, we improve the static coseismic slip inversion for these three earthquakes, especially at shallow depths, by: (1) including data capturing the near-fault deformation from optical imagery and SAR azimuth offsets; (2) refining the interferometric synthetic aperture radar processing with non-boxcar phase filtering, model-dependent range corrections, more complete phase unwrapping by SNAPHU (Statistical Non-linear Approach for Phase Unwrapping) assuming a maximum discontinuity and an on-fault correlation mask; (3) using more detailed, geologically constrained fault geometries and (4) incorporating additional campaign global positioning system (GPS) data. The refined slip models result in much smaller SSDs of 3–19 per cent. We suspect that the remaining minor SSD for these earthquakes likely reflects a combination of our elastic model's inability to fully account for near-surface deformation, which will render our estimates of shallow slip minima, and potentially small amounts of interseismic fault creep or triggered slip, which could ‘make up’ a small percentages of the coseismic SSD during the interseismic period. Our results indicate that it is imperative that slip inversions include accurate measurements of near-fault surface deformation to reliably constrain spatial patterns of slip during major strike-slip earthquakes

Corrections to "A Novel Multitemporal Deep Fusion Network (MDFN) for Short-Term Multitemporal HR Images Classification"

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High-Rankness Regularized Semi-Supervised Deep Metric Learning for Remote Sensing Imagery

Deep metric learning has recently received special attention in the field of remote sensing (RS) scene characterization, owing to its prominent capabilities for modeling distances among RS images based on their semantic information. Most of the existing deep metric learning methods exploit pairwise and triplet losses to learn the feature embeddings with the preservation of semantic-similarity, which requires the construction of image pairs and triplets based on the supervised information (e.g., class labels). However, generating such semantic annotations becomes a completely unaffordable task in large-scale RS archives, which may eventually constrain the availability of sufficient training data for this kind of models. To address this issue, we reformulate the deep metric learning scheme in a semi-supervised manner to effectively characterize RS scenes. Specifically, we aim at learning metric spaces by utilizing the supervised information from a small number of labeled RS images and exploring the potential decision boundaries for massive sets of unlabeled aerial scenes. In order to reach this goal, a joint loss function, composed of a normalized softmax loss with margin and a high-rankness regularization term, is proposed, as well as its corresponding optimization algorithm. The conducted experiments (including different state-of-the-art methods and two benchmark RS archives) validate the effectiveness of the proposed approach for RS image classification, clustering and retrieval tasks. The codes of this paper are publicly available.EC/H2020/734541/EU/TOOLS FOR MAPPING HUMAN EXPOSURE TO RISKY ENVIRONMENTAL CONDITIONS BY MEANS OF GROUND AND EARTH OBSERVATION DATA/EOXPOSUR