CMID: A Unified Self-Supervised Learning Framework for Remote Sensing Image Understanding

Self-supervised learning (SSL) has gained widespread attention in the remote sensing (RS) and earth observation (EO) communities owing to its ability to learn task-agnostic representations without human-annotated labels. Nevertheless, most existing RS SSL methods are limited to learning either global semantic separable or local spatial perceptible representations. We argue that this learning strategy is suboptimal in the realm of RS, since the required representations for different RS downstream tasks are often varied and complex. In this study, we proposed a unified SSL framework that is better suited for RS images representation learning. The proposed SSL framework, Contrastive Mask Image Distillation (CMID), is capable of learning representations with both global semantic separability and local spatial perceptibility by combining contrastive learning (CL) with masked image modeling (MIM) in a self-distillation way. Furthermore, our CMID learning framework is architecture-agnostic, which is compatible with both convolutional neural networks (CNN) and vision transformers (ViT), allowing CMID to be easily adapted to a variety of deep learning (DL) applications for RS understanding. Comprehensive experiments have been carried out on four downstream tasks (i.e. scene classification, semantic segmentation, object-detection, and change detection) and the results show that models pre-trained using CMID achieve better performance than other state-of-the-art SSL methods on multiple downstream tasks. The code and pre-trained models will be made available at https://github.com/NJU-LHRS/official-CMID to facilitate SSL research and speed up the development of RS images DL applications.Comment: Accepted by IEEE TGRS. The codes and models are released at https://github.com/NJU-LHRS/official-CMI

Twins:Device-free Object Tracking using Passive Tags

Without requiring objects to carry any transceiver, device-free based object tracking provides a promising solution for many localization and tracking systems to monitor non-cooperative objects such as intruders. However, existing device-free solutions mainly use sensors and active RFID tags, which are much more expensive compared to passive tags. In this paper, we propose a novel motion detection and tracking method using passive RFID tags, named Twins. The method leverages a newly observed phenomenon called critical state caused by interference among passive tags. We contribute to both theory and practice of such phenomenon by presenting a new interference model that perfectly explains this phenomenon and using extensive experiments to validate it. We design a practical Twins based intrusion detection scheme and implement a real prototype with commercial off-the-shelf reader and tags. The results show that Twins is effective in detecting the moving object, with low location error of 0.75m in average

Analysis of shared ceRNA networks and related-hub genes in rats with primary and secondary photoreceptor degeneration

IntroductionPhotoreceptor degenerative diseases are characterized by the progressive death of photoreceptor cells, resulting in irreversible visual impairment. However, the role of competing endogenous RNA (ceRNA) in photoreceptor degeneration is unclear. We aimed to explore the shared ceRNA regulation network and potential molecular mechanisms between primary and secondary photoreceptor degenerations.MethodsWe established animal models for both types of photoreceptor degenerations and conducted retina RNA sequencing to identify shared differentially expressed long non-coding RNAs (lncRNAs), microRNAs (miRNAs), and messenger RNAs (mRNAs). Using ceRNA regulatory principles, we constructed a shared ceRNA network and performed function enrichment and protein–protein interaction (PPI) analyses to identify hub genes and key pathways. Immune cell infiltration and drug–gene interaction analyses were conducted, and hub gene expression was validated by quantitative real-time polymerase chain reaction (qRT-PCR).ResultsWe identified 37 shared differentially expressed lncRNAs, 34 miRNAs, and 247 mRNAs and constructed a ceRNA network consisting of 3 lncRNAs, 5 miRNAs, and 109 mRNAs. Furthermore, we examined 109 common differentially expressed genes (DEGs) through functional annotation, PPI analysis, and regulatory network analysis. We discovered that these diseases shared the complement and coagulation cascades pathway. Eight hub genes were identified and enriched in the immune system process. Immune infiltration analysis revealed increased T cells and decreased B cells in both photoreceptor degenerations. The expression of hub genes was closely associated with the quantities of immune cell types. Additionally, we identified 7 immune therapeutical drugs that target the hub genes.DiscussionOur findings provide new insights and directions for understanding the common mechanisms underlying the development of photoreceptor degeneration. The hub genes and related ceRNA networks we identified may offer new perspectives for elucidating the mechanisms and hold promise for the development of innovative treatment strategies

Structure design and performance analysis of aerostatic thrust bearing with compound restrictors

Aerostatic thrust bearing compensated by multi-orifices and porous material restrictor simultaneously is proposed to improve the static performance of the bearing. Load Carrying Capacity (LCC), stiffness and the flow field characteristics of the bearing are obtained by Computational Fluid Dynamic (CFD) simulation. The influences of supply pressure, orifice number, orifice diameter, orifice distribution, porous material thickness and permeability coefficient on the bearing performance are analysed. It is indicated that LCC and stiffness of the bearing with compound restrictors are much higher than those of the bearing with porous material restrictor or multi-orifice restrictor if gas film thickness is in rational range. The bearing with compound restrictors has better stability than that of the bearing with multi-orifice restrictor. Moreover, the optimum bearing parameters with compound restrictors are given to improving the performance of aerostatic thrust bearing

Multiscale Optimized Segmentation of Urban Green Cover in High Resolution Remote Sensing Image

The urban green cover in high-spatial resolution (HR) remote sensing images have obvious multiscale characteristics, it is thus not possible to properly segment all features using a single segmentation scale because over-segmentation or under-segmentation often occurs. In this study, an unsupervised cross-scale optimization method specifically for urban green cover segmentation is proposed. A global optimal segmentation is first selected from multiscale segmentation results by using an optimization indicator. The regions in the global optimal segmentation are then isolated into under- and fine-segmentation parts. The under-segmentation regions are further locally refined by using the same indicator as that in global optimization. Finally, the fine-segmentation part and the refined under-segmentation part are combined to obtain the final cross-scale optimized result. The green cover objects can be segmented at their specific optimal segmentation scales in the optimized segmentation result to reduce both under- and over-segmentation errors. Experimental results on two test HR datasets verify the effectiveness of the proposed method

FDFF-Net: A Full-Scale Difference Feature Fusion Network for Change Detection in High-Resolution Remote Sensing Images

Deep-learning techniques have made significant advances in remote sensing change detection task. However, it remains a great challenge to detect the details of changed areas from high-resolution remote sensing images. In this study, we propose a full-scale difference feature fusion network (FDFF-Net) for change detection, which can alleviate pseudochanges and reduce the loss of change details during detection. In the encoding stage, a dense difference fusion module is proposed to effectively mine and fuse the multiple differences for each feature level between bitemporal images, leading to a substantial reduction in missed detection of change areas. Additionally, the different levels of difference features are aggregated through a full-scale skip connection, allowing the network to detect multiple changed objects with various sizes. In the decoding stage, a strip spatial attention module is designed to enhance the perception of the change areas, which improves the ability to detect detailed changes. The experiments on three change detection datasets, CDD, LEVIR-CD, and S2Looking, demonstrate that FDFF-Net outperforms the compared state-of-the-art methods and can detect more complete changes of small objects and clear contours of changed areas

Which CAM is Better for Extracting Geographic Objects? A Perspective From Principles and Experiments

As a method of deep learning interpretability, class activation mapping (CAM) is efficient and convenient for extracting geographic objects supervised by image-level labels. However, in addition to the inherent problem of inaccuracy and incompleteness of CAM, we have to deal with the spectral and spatial variance of geographic objects when applying CAM methods to remote sensing images. To explore the capabilities of CAM methods on extracting various geographic objects, we make a comprehensive comparison of five commonly-used CAM methods, including original CAM, GradCAM, GradCAM++, SmoothGradCAM++, and ScoreCAM, in four aspects: efficiency; accuracy; effectiveness on dealing with the spectral and spatial variance; and performance of delineating different geographic object categories. The results demonstrate that the original CAM, GradCAM, and GradCAM++ achieves the highest efficiency, accuracy, and integrity for extracting geographic objects, respectively, which can help us choose the appropriate CAM methods according to the specific requirements of different extraction tasks. Benefiting from the capability in extracting various geographic objects and adaptability in complex scenes, GradCAM achieves the best performance in dealing with the spectral and spatial variance problem and shows the advantage of capturing object details and keeping object completeness at the same time. In addition to the comparison experiments and suggestions, we also provide the principle explanations of the performance differences. The findings of this article could contribute to a deep understanding of different CAM methods and benefit to selecting suitable CAM methods for extracting geographic objects from the perspectives of both principles and experiments

Exact speed tracking realization of the single shaft micro-turbine system via higher-order sliding mode observer

We present in this paper, a composite control approach to realize the exact speed trajectory tracking for the single shaft micro-turbine (MT) system. The composite controller is developed by incorporating a higher-order sliding mode observer and a feedback domination technique. By mean of the proposed method, asymptotic stability of the MT system can be ensured even in the presence of mismatched disturbances. It is illustrated, by numerical simulations, how this handy machinery is feasible for the practical disturbed MT system to realize the exact tracking, rather than a practical tracking objective.Published versio