Exchanging Dual Encoder-Decoder: A New Strategy for Change Detection with Semantic Guidance and Spatial Localization

Change detection is a critical task in earth observation applications. Recently, deep learning-based methods have shown promising performance and are quickly adopted in change detection. However, the widely used multiple encoder and single decoder (MESD) as well as dual encoder-decoder (DED) architectures still struggle to effectively handle change detection well. The former has problems of bitemporal feature interference in the feature-level fusion, while the latter is inapplicable to intraclass change detection and multiview building change detection. To solve these problems, we propose a new strategy with an exchanging dual encoder-decoder structure for binary change detection with semantic guidance and spatial localization. The proposed strategy solves the problems of bitemporal feature inference in MESD by fusing bitemporal features in the decision level and the inapplicability in DED by determining changed areas using bitemporal semantic features. We build a binary change detection model based on this strategy, and then validate and compare it with 18 state-of-the-art change detection methods on six datasets in three scenarios, including intraclass change detection datasets (CDD, SYSU), single-view building change detection datasets (WHU, LEVIR-CD, LEVIR-CD+) and a multiview building change detection dataset (NJDS). The experimental results demonstrate that our model achieves superior performance with high efficiency and outperforms all benchmark methods with F1-scores of 97.77%, 83.07%, 94.86%, 92.33%, 91.39%, 74.35% on CDD, SYSU, WHU, LEVIR-CD, LEVIR- CD+, and NJDS datasets, respectively. The code of this work will be available at https://github.com/NJU-LHRS/official-SGSLN

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

A Fine-Grained Unsupervised Domain Adaptation Framework for Semantic Segmentation of Remote Sensing Images

Unsupervised domain adaptation (UDA) aims at adapting a model from the source domain to the target domain by tackling the issue of domain shift. Cross-domain segmentation of remote sensing images (RSIs) remains a big challenge due to the unique properties of RSIs. On the one hand, the divergence of data distribution in different local regions leads to negative transfer by directly applying the global alignment method in RSIs. On the other hand, the underlying category-level structure in the target domain is often ignored, which confuses the decision of semantic boundaries on the dispersed category features caused by large intraclass variance and small interclass variance in RSIs. In this study, we propose a novel fine-grained adaptation framework combining two stages of global-local alignment and category-level alignment to solve the above-mentioned problems. In the first stage of global-local adaptation, an attention map is derived from an intermediate discriminator and focuses on hard-to-align regions to mitigate negative transfer due to global adversarial learning. In the second stage of category-level adaptation, the category feature compact module is utilized to address the issue of dispersed features in the target domain attained by the cross-domain network, which will facilitate the fine-grained alignment of categories. Experiments under various scenarios, including geographic location variation and spectral band composition variation, demonstrate that the local adaptation and category-level adaptation of RSIs are complementary in the cross-domain segmentation, and the integrated framework helps achieve outstanding performance for UDA semantic segmentation of RSIs

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