As one of the most challenging tasks of remote sensing big data mining, large-scale remote sensing image retrieval has attracted increasing attention from researchers. Existing large-scale remote ...sensing image retrieval approaches are generally implemented by using hashing learning methods, which take handcrafted features as inputs and map the high-dimensional feature vector to the low-dimensional binary feature vector to reduce feature-searching complexity levels. As a means of applying the merits of deep learning, this paper proposes a novel large-scale remote sensing image retrieval approach based on deep hashing neural networks (DHNNs). More specifically, DHNNs are composed of deep feature learning neural networks and hashing learning neural networks and can be optimized in an end-to-end manner. Rather than requiring to dedicate expertise and effort to the design of feature descriptors, we can automatically learn good feature extraction operations and feature hashing mapping under the supervision of labeled samples. To broaden the application field, DHNNs are evaluated under two representative remote sensing cases: scarce and sufficient labeled samples. To make up for a lack of labeled samples, DHNNs can be trained via transfer learning for the former case. For the latter case, DHNNs can be trained via supervised learning from scratch with the aid of a vast number of labeled samples. Extensive experiments on one public remote sensing image data set with a limited number of labeled samples and on another public data set with plenty of labeled samples show that the proposed remote sensing image retrieval approach based on DHNNs can remarkably outperform state-of-the-art methods under both of the examined conditions.
Reservoir landslides are typical geohazards with sophisticated thermo‐hydro‐mechanical interactions. However, direct observations of subsurface multi‐physical processes in bank slopes remain rare. ...Herein we present the design, implementation and evaluation of an innovative fiber‐optic nerve system based on weak‐reflection fiber Bragg grating to monitor a giant landslide located in the Three Gorges Reservoir region, China. The system is capable of measuring and imaging spatiotemporal distributions of temperature, moisture and strain along boreholes in near real‐time. Such visual profiles offer unique insights into the subsurface thermo‐hydro‐mechanical link between external conditions and geotechnical deformation, making it possible to decipher the substantial trigger of accelerated movements. Multi‐physical responses at depths of interest also enable us to better understand the evolution mechanism of reservoir landslides. Fiber‐optic nerve sensing can synergize with remote sensing and surface‐based technologies to build a space‐sky‐ground‐subsurface integrated monitoring system for landslides.
Plain Language Summary
Field monitoring of multi‐physical processes of reservoir landslides in the context of extreme weather events has remained challenging. Here we present the design, implementation and evaluation of a novel fiber‐optic nerve system (FONS) and the results and interpretation of recent FONS monitoring at the Outang landslide in the Three Gorges Reservoir region, China. The spatiotemporal profiles of subsurface temperature, moisture and strain are obtained, which enable us to understand the subsurface thermo‐hydro‐mechanical interactions. The deep‐seated slip surface has been successfully identified, together with a shallower one newly generated in the rear section of the landslide. We further verify that precipitation was the substantial trigger of accelerated deformation in the rear slope, and argue that short‐duration high‐intensity extreme rainfall was most likely to drive an abrupt movement. The monitoring system allows us to investigate the characteristics and mechanism of specific deformation acceleration events from daily to annual time series and relate the thermo‐hydro‐mechanical behaviors of sliding masses to creeping motion and progressive failure.
Key Points
The fiber‐optic nerve system with weak‐reflection fiber Bragg gratings produces multi‐physical spatiotemporal profiles at high resolutions
Thermo‐hydro‐mechanical responses at the slip surface uncover subsurface‐scale landslide kinematics, hydrological transport and retention
Short‐duration high‐intensity rainfalls promote landslide dynamics relative to uniform‐intensity rainfalls of similar amount and duration
In recent years, natural and anthropogenic geohazards have occured frequently all over the world, and field monitoring is becoming an increasingly important task to mitigate these risks. However, ...conventional geotechnical instrumentations for monitoring geohazards have a number of weaknesses, such as low accuracy, poor durability, and high sensitivity to environmental interferences. In this aspect, fiber Bragg grating (FBG), as a popular fiber optic sensing technology, has gained an explosive amount of attention. Based on this technology, quasi-distributed sensing systems have been established to perform real-time monitoring and early warning of landslides, debris flows, land subsidence, earth fissures and so on. In this paper, the recent research and development activities of applying FBG systems to monitor different types of geohazards, especially those triggered by human activities, are critically reviewed. The working principles of newly developed FBG sensors are briefly introduced, and their features are summarized. This is followed by a discussion of recent case studies and lessons learned, and some critical problems associated with field implementation of FBG-based monitoring systems. Finally the challenges and future trends in this research area are presented.
Infrared small target detection technology is one of the key technologies in the field of computer vision. In recent years, several methods have been proposed for detecting small infrared targets. ...However, the existing methods are highly sensitive to challenging heterogeneous backgrounds, which are mainly due to: 1) infrared images containing mostly heavy clouds and chaotic sea backgrounds and 2) the inefficiency of utilizing the structural prior knowledge of the target. In this article, we propose a novel approach for infrared small target detection in order to take both the structural prior knowledge of the target and the self-correlation of the background into account. First, we construct a tensor model for the high-dimensional structural characteristics of multiframe infrared images. Second, inspired by the low-rank background and morphological operator, a novel method based on low-rank tensor completion with top-hat regularization is proposed, which integrates low-rank tensor completion and a ring top-hat regularization into our model. Third, a closed solution to the optimization algorithm is given to solve the proposed tensor model. Furthermore, the experimental results from seven real infrared sequences demonstrate the superiority of the proposed small target detection method. Compared with traditional baseline methods, the proposed method can not only achieve an improvement in the signal-to-clutter ratio gain and background suppression factor but also provide a more robust detection model in situations with low false-positive rates.
Sparse-view Reconstruction can be used to provide accelerated low dose CT imaging with both accelerated scan and reduced projection/back-projection calculation. Despite the rapid developments, image ...noise and artifacts still remain a major issue in the low dose protocol. In this paper, a deep learning based method named Improved GoogLeNet is proposed to remove streak artifacts due to projection missing in sparse-view CT reconstruction. Residual learning is used in GoogLeNet to study the artifacts of sparse-view CT reconstruction, and then subtracts the artifacts obtained by learning from the sparse reconstructed images, finally recovers a clear correction image. The intensity of reconstruction using the proposed method is very close to the full-view projective reconstructed image. The results indicate that the proposed method is practical and effective for reducing the artifacts and preserving the quality of the reconstructed image.
Recently, infrared small target detection problem has attracted substantial attention. Many works based on local low-rank model have been proven to be very successful for enhancing the ...discriminability during detection. However, these methods construct patches by traversing local images and ignore the correlations among different patches. Although the calculation is simplified, some texture information of the target is ignored, and targets of arbitrary forms cannot be accurately identified. In this paper, a novel target-aware method based on a non-local low-rank model and saliency filter regularization is proposed, with which the newly proposed detection framework can be tailored as a non-convex optimization problem, therein enabling joint target saliency learning in a lower dimensional discriminative manifold. More specifically, non-local patch construction is applied for the proposed target-aware low-rank model. By combining similar patches, we reconstruct them together to achieve a better generalization of non-local spatial sparsity constraints. Furthermore, to encourage target saliency learning, our proposed saliency filtering regularization term based on entropy is restricted to lie between the background and foreground. The regularization of the saliency filtering locally preserves the contexts from the target and surrounding areas and avoids the deviated approximation of the low-rank matrix. Finally, a unified optimization framework is proposed and solved with the alternative direction multiplier method (ADMM). Experimental evaluations of real infrared images demonstrate that the proposed method is more robust under different complex scenes compared with some state-of-the-art methods.
A
bstract
We explore the generalized covariant entropy bound in the theory where Einstein gravity is perturbed by quadratic curvature terms, which can be viewed as the first-order quantum correction ...to Einstein gravity. By replacing the Bekenstein-Hawking entropy with the holographic entanglement entropy of this theory and introducing two reasonable physical assumptions, we demonstrate that the corresponding Generalized Covariant Entropy Bound is satisfied under a first-order approximation of the perturbation from the quadratic curvature terms. Our findings suggest that the entropy bound and the Generalized Second Law of black holes are satisfied in the Einstein gravity under the first-order perturbation from the quadratic curvature corrections, and they also imply that the generalized covariant entropy bound may still hold even after considering the quantum correction of gravity, but in this case, we may need to use holographic entanglement entropy as the formula for gravitational entropy.
Mussel foot proteins (Mfps) show strong adhesion to underwater substrates, making mussels tightly cling to reefs to withstand the sea current. Therefore, Mfps-inspired tissue adhesives have aroused ...much research interest, but tough underwater biological tissue adhesion is still a great challenge. Herein, we report a tough and reversible wet tissue-selective adhesive hydrogel made of poly(acrylic acid-co-catechol) and chitosan (CS). It provides negatively charged -COO
, positively charged -NH
, catechol group and hydrophobic alkyl chain, resemble amino acids, catechol and hydrophobic units in Mfps. Due to the covalent/electrostatic attraction/π-π/cationic-π/hydrogen bonding, in addition to the hydrophobic interaction from the long hydrophobic alkyl chain of the catechol derivative, the hydrogel has a high cohesion strength and toughness, i.e., tensile stress, fracture strain and fracture toughness of ∼0.57 MPa, 2510% and 6620 J m
, respectively. As a tissue adhesive, its adhesion bonding to the porcine skin surface is so strong that its adhesion strength is almost equal to the tearing strength of the hydrogel. The 180-degree peeling adhesion energy of the hydrogel to blood-wetted porcine skin is notably ∼1010 J m
. It can tightly and seamlessly adhere to the porcine small intestine, and has a bursting pressure of up to 520 mmHg. The hydrogel can be handily debonded from the porcine skin surface in the presence of aqueous solution at pH 8.0, and its adhesiveness is reversible for at least 20 cycles. It is supposed that the synergistic interactions of the adhesive catechol group, displacement of water on the wet skin surface by the positively charged -NH
groups of CS and the water-repelling potential of the hydrophobic unit of the catechol derivative, the protection of the catechol group from oxidation into a less adhesive quinone group, and the energy dissipation capacity of the mechanically tough hydrogel contribute to the strong and repeatable wet tissue adhesion.
The chemo-free concept represents a new direction for managing adult patients with Ph-positive acute lymphoblastic leukemia (Ph + ALL). The tyrosine kinase inhibitors (TKIs), blinatumomab and ...venetoclax serve as the backbone of chemo-free regimens; several prospective studies involving these drugs have demonstrated high remission rates and promising, albeit short, survival outcomes. This review summarizes the latest updates on chemo-free regimens in the treatment of adult patients with Ph + ALL, presented at the 2023 ASH annual meeting.
Detecting small targets in a complex background is always carried out by suppressing the background. The top-hat transformation is mainly utilized for background suppression in target detection. Many ...modified top-hat transformation methods are based on the different structures of the structural elements. However, there are two limitations. One is that the structural elements cannot sufficiently consider the contrast information between the target and surrounding area to enhance the target. Another is that structural elements should be set in advance and cannot adaptively suppress complex backgrounds. In this article, our proposed top-hat transformation is designed from two cases. First, an adaptive structural element based on a guided filter kernel is proposed for capturing the local features in infrared images for background suppression. Second, a balanced ring shape is used for two structural elements of top-hat transformation, which can utilize the contrast information between the target and background for target enhancement. More than 500 infrared target images are used in our experiment. The experimental results show that our algorithm achieves better performance in signal-to-clutter ratio gain, background suppression factor, and detection accuracy when compared with recent popular baseline methods.
PDF
