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Low-thermal budget fabrication of two-dimensional Schottky diodes for broadband convolutional processing

NANO RESEARCH 2025年第1期18卷 94907049-1-94907049-1-94907049-9页

作者： Han, Zichao Tian, Shijia Wang, Han Sang, Weihui Gan, Yang Cao, Yi Tan, Feixia Li, Honghong Wang, Tinghao Yu, Yuan Songlu, Wenyu Wang, Yue Liu, Tao Xiang, Du Fudan Univ Inst Optoelect Shanghai Frontiers Sci Res Base Intelligent Optoel Shanghai 200433 Peoples R China Fudan Univ Dept Mat Sci Shanghai 200433 Peoples R China Fudan Univ Frontier Inst Chip & Syst State Key Lab Integrated Chips & Syst Shanghai 200438 Peoples R China Fudan Univ Sch Microelect Shanghai 200433 Peoples R China Fudan Univ Zhangjiang Fudan Int Innovat Ctr Shanghai 200433 Peoples R China

In-sensor broadband convolutional processing (BCP) holds great significance to the advancement of high-precision image recognition for remote sensing and environmental monitoring. Two-dimensional (2D) heterostructures offer abundant band alignment configurations with electrical tunability, which is promising to implement the in-sensor BCP at hardware level. Huge efforts have been devoted to developing 2D heterostructures based intelligent edge devices for BCP, which however either lack the potential for scalability and reproducibility, or require high processing temperature. Here, we demonstrate a PtSe2/WSe2 heterostructure based Schottky diode fabricated by using thermal-assisted conversion (TAC) technique, which converts the pre-deposited Pt films into PtSe2 via the controllable selenization process with complementary metal-oxide-semiconductor (CMOS) back-end-of-line (BEOL) compatible thermal budget. The TAC-PtSe2 in various thicknesses demonstrate high crystalline nature and low contact resistance (425 Omega center dot mu m), facilitating an atomically sharp interface and gate-tunable band alignment. Such characteristics give rise to polarity-changeable built-in electric field, resulting in a remarkable rectification ratio approaching similar to 10(5). Moreover, the positive-negative switching of the photoresponse with linear intensity dependence is achieved across a wide spectrum from ultraviolet to near-infrared light, which is desirable in constructing various convolution kernels for BCP tasks. A hyperspectral remote sensing image is adopted to demonstrate the BCP operations including edge detection and sharpness, where the outputs are of comparable quality with the simulated results. Our work envisions a CMOS-compatible approach for fabricating 2D Schottky diodes with tunable band alignment, offering a viable route for the scalable hardware implementation of broadband image processing units.

关键词： broadband convolutional processing thermal-assisted conversion two-dimensional heterostructures photovoltaic effect Schottky diodes

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Unsupervised Domain Adaptation Architecture Search with Self-Training for Land Cover Mapping

Unsupervised Domain Adaptation Architecture Search with Self...

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IEEE/CVF Conference on Computer Vision and pattern recognition (CVPR)

作者： Broni-Bediako, Clifford Xia, Junshi Yokoya, Naoto RIKEN Ctr Adv Intelligence Project AIP Tokyo Japan Univ Tokyo Grad Sch Frontier Sci Chiba Japan

ISBN: (纸本)9798350365474

Unsupervised domain adaptation (UDA) is a challenging open problem in land cover mapping. Previous studies show encouraging progress in addressing cross-domain distribution shifts on remote sensing benchmarks for land cover mapping. The existing works are mainly built on large neural network architectures, which makes them resource-hungry systems, limiting their practical impact for many real-world applications in resource-constrained environments. Thus, we proposed a simple yet effective framework to search for lightweight neural networks automatically for land cover mapping tasks under domain shifts. This is achieved by integrating Markov random field neural architecture search (MRF-NAS) into a self-training UDA framework to search for efficient and effective networks under a limited computation budget. This is the first attempt to combine NAS with self-training UDA as a single framework for land cover mapping. We also investigate two different pseudo-labelling approaches (confidence-based and energy-based) in self-training scheme. Experimental results on two recent datasets (OpenEarthMap & FLAIR #1) for remote sensing UDA demonstrate a satisfactory performance. With only less than 2M parameters and 30.16 G FLOPs, the best-discovered lightweight network reaches state-of-the-art performance on the regional target domain of OpenEarthMap (59.38% mIoU) and the considered target domain of FLAIR #1 (51.19% mIoU). The code is at https://***/cliffbb/UDA-NAS.

关键词： Land Cover Mapping Neural Architecture Search (NAS) remote sensing image Segmentation Unsupervised Domain Adaptation (UDA)

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Deblur Capsule Networks 26th

Deblur Capsule Networks

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26th Iberoamerican Congress on pattern recognition (CIARP)

作者： Santos, Daniel Felipe S. Pires, Rafael G. Papa, Joao P. Sao Paulo State Univ Dept Comp Bauru SP Brazil

ISBN: (纸本)9783031490170;9783031490187

Blur is often caused by physical limitations of the image acquisition sensor or by unsuitable environmental conditions. Blind image deblurring recovers the underlying sharp image from its blurry counterpart without further knowledge regarding the blur kernel or the sharp image itself. Traditional deconvolution filters are highly dependent on specific kernels or prior knowledge to guide the deblurring process. This work proposes an end-to-end deep learning approach to address blind image deconvolution in three stages: (i) it first predicts the blur type, (ii) then it deconvolves the blurry image by the identified and reconstructed blur kernel, and (iii) it deep regularizes the output image. Our proposed approach, called Deblur Capsule Networks, explores the capsule structure in the context of image deblurring. Such a versatile structure showed promising results for synthetic uniform camera motion and multi-domain blind deblur of general-purpose and remote sensing image datasets compared to some state-of-the-art techniques.

关键词： Deblurring remote sensing Capsules

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Multi-scale Rotating Target Detection in High Resolution remote sensing image Based on Improved YOLOv5

Multi-scale Rotating Target Detection in High Resolution Rem...

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2023 International Conference on image, Signal processing, and pattern recognition, ISPP 2023

作者： Li, Jiapeng Wu, Jiaqi Liu, Qi Zhang, Zheng North China University of Technology Beijing100144 China China Institute of Water Resources and Hydropower Research Beijing100038 China

ISBN: (纸本)9781510666351

As the remote sensing image information rapidly becomes abundant, it is a challenge for the detection of tiny targets with dense distribution. Therefore, a multi-scale rotating object detection model based on the improved YOLOv5 is proposed in this paper. Firstly, because of adding a prediction feature layer to the network, the detection precision of tiny targets has substantially increased. Secondly, the loss between rotation anchors can be fitted at a high precision due to the compressed loss function aiming at the calculation of the IoU loss of rotation is proposed. Finally, a hybrid prior bounding box strategy is applied in the feature prediction layer to suit the targets to be detected in different sizes. Experiments conducted on the DOTA dataset indicates that this method significantly exceeded the original YOLOv5. It has extraordinary performances for the task of object detection in optical remote sensing image fields. © 2023 SPIE.

关键词： Object detection

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A semi-supervised network for object segmentation of remote sensing images 12

A semi-supervised network for object segmentation of remote ...

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SPIE 12th International Symposium on Multispectral image processing and pattern recognition, MIPPR 2023

作者： Zhang, Zhixin Liu, Jia Mou, Handong Liu, Tianhang Network & Information Center Changjiang Water Resources Commission Wuhan430010 China Center of Technology Innovation for Digital Enablement of River Basin Management Wuhan430010 China Smart Changjiang Innovation Team of Changjiang Water Resources Commission China School of Artificial Intelligence and Automation Huazhong University of Science and Technology Wuhan430074 China

ISBN: (数字)9781510674943

ISBN: (纸本)9781510674936

Recently, object segmentation of remote sensing images has achieved great progress in many fields, such as transportation, natural resource, ecology, et al. A lot of works mainly performed object segmentation in fully-supervised mode. However, training models in such mode usually need craft large-scale annotations, which is usually an expensive work and costs much time. In this paper, a novel semi-supervised network for object segmentation of remote sensing images is proposed, which is only fed with a small amount of labeled data and relatively more unlabeled data. Rather than using the same architecture as previous semi-supervised works, we exploit two networks with different architectures, i.e. CNN and Transformer, as the cross-supervised models. Moreover, three types of loss functions, namely fully-supervised loss, cross-supervised loss and consistency loss, are introduced to enhance the model's robustness. The effectiveness of our proposed method is evaluated on two annotated remote sensing datasets, outperforming several state-of-The-Art semi-supervised approaches. © COPYRIGHT SPIE. Downloading of the abstract is permitted for personal use only.

关键词： image segmentation

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Morphological reconstruction algorithms for urban monitoring using satellite data: proper selection of the marker and mask images

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INTERNATIONAL JOURNAL OF remote sensing 2022年第2期43卷 674-697页

作者： Valdiviezo-N, Juan C. Hernandez-Lopez, Francisco J. Tellez-Quinones, Alejandro CONACYT CentroGeo Dept Remote Sensing Merida 97302 Yucatan Mexico CONACYT CIMAT Dept Comp Sci Unidad Merida Merida Yucatan Mexico

Morphological processing has found several applications in image analysis and pattern recognition. Some of these techniques, known as morphological reconstruction algorithms, have been employed for land cover classification in remote sensing data. In this paper, we analyse the mathematical foundations, applications, and limitations of reconstruction by dilation and by erosion oriented to urban extraction, using Sentinel-2 satellite data. Different techniques oriented to the proper determination of the marker and mask images, the basis for reconstruction, are proposed in this manuscript. In addition, in order to diminish the long computation time required for reconstruction, two parallel implementations using Multi-core and GPU, are proposed. According to our research, these algorithms can be considered as effective and non-supervised solutions for urban extraction applications based on multispectral satellite imagery.

关键词： Extraction

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PSTNet: Progressive Sampling Transformer Network for remote sensing image Change Detection

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IEEE JOURNAL OF SELECTED TOPICS IN APPLIED EARTH OBSERVATIONS AND remote sensing 2022年 15卷 8442-8455页

作者： Song, Xinyang Hua, Zhen Li, Jinjiang Shandong Technol & Business Univ Sch Informat & Elect Engn Yantai 264005 Peoples R China Inst Network Technol Yantai 264000 Peoples R China Shandong Technol & Business Univ Sch Comp Sci & Technol Yantai 264005 Peoples R China

remote sensing change detection (CD) is to use multitemporal remote sensing data to extract change information by using a variety of image processing and pattern recognition methods, and quantitatively analyze and determine the characteristics and processes of surface changes. In recent research on CD, how to more accurately segment objects and how to extract and effectively link spatiotemporal information are important parts. To achieve this, we propose a progressive sampling (PS) transformer network for remote sensing image CD, which continuously extracts and optimizes feature information in an iterative manner, so that pixels can establish better connections in the spatial domain to model the context. Our intuition is that, through this iterative sampling method, the parts of interest in the image can be gradually extracted. This allows subsequent processing to be more focused on useful areas, which in turn reduces interference from uninteresting parts, and the information after PS will be generalize into several tokens containing rich semantic information. Using the excellent modeling ability of the transformer, the optimized tokens are mapped back to the original image features to achieve the purpose of segmenting accurate difference images. We conducted extensive experiments on three CD datasets, LEVIR-CD, DSIFN-CD, and WHU-CD, and achieved evaluation scores of 90.73/84.11, 80.10/68.93, and 91.67/85.15 on F1-score and IoU metrics, respectively. Notably, the convolutional neural network (CNN) backbone of our network uses only a simplified ResNet model, without using structurally complex frameworks, such as FPN and Unet, but our model uses PS module and transformer to achieve better performance than the recent advanced CD models.

关键词： Feature extraction Context modeling Semantics remote sensing Data mining Current transformers Task analysis Attention mechanisms change detection (CD) deep convolutional network dual-time satellite remote sensing images progressive sampling (PS) transformers

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Multichannel image classification based on adaptive attribute profiles

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pattern recognition LETTERS 2025年 187卷 107-114页

作者： Alves, Wonder A. L. Campos, Wander S. Gobber, Charles F. Silva, Dennis J. Hashimoto, Ronaldo F. Univ Nove Julho Informat & Knowledge Management Grad Program Sao Paulo Brazil Univ Sao Paulo Inst Math & Stat Dept Comp Sci Sao Paulo Brazil Univ Groningen Bernoulli Inst Math Comp Sci & Artificial Intellig Groningen Netherlands

Morphological Attribute Profiles serve as powerful tools for extracting meaningful features from remote sensing data. The construction of Morphological Attribute Profiles relies on two primary parameters: the choice of attribute type and the definition of a numerical threshold sequence. However, selecting an appropriate threshold sequence can be a difficult task, as an inappropriate choice can lead to an uninformative feature space. In this paper, we propose a semi-automatic approach based on the theory of Maximally Stable Extremal Regions to address this challenge. Our approach takes an increasing attribute type and an initial sequence of thresholds as input and locally adjusts threshold values based on region stability within the image. Experimental results demonstrate that our method significantly increases classification accuracy through the refinement of threshold values.

关键词： Attribute profile Automatic attribute profile Semi-automatic attribute profile

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An Improved Bilateral Filtering Based on Curvature Feature for Point Cloud Denoising in Road Surface Detection 12

An Improved Bilateral Filtering Based on Curvature Feature f...

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SPIE 12th International Symposium on Multispectral image processing and pattern recognition, MIPPR 2023

作者： Chen, Si Hubei Key Laboratory of Optical Information and Pattern Recognition Wuhan430205 China Hubei Engineering Research Center of Video Image and HD Projection Wuhan430205 China School of Electrical and Information Technology Wuhan Institute of Technology Wuhan430205 China

ISBN: (数字)9781510675001

ISBN: (纸本)9781510674998

To effectively remove noise from the point clouds obtained for road surface defect detection while preserving the feature information, a bilateral filtering algorithm based on curvature features is proposed for denoising road surface defect point clouds. First, divide the noise points in the point cloud into several regions based on their distribution characteristics and curvature features. Then use statistical filtering to remove noise points in the obvious noise area and the flat area. Finally, an improved bilateral filtering algorithm is applied to smooth the noise points in the complex detail area. © 2024 SPIE.

关键词： Surface defects

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Aircraft type recognition in 3D-view optical image with contour segmentation

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Multimedia Tools and Applications 2024年第18期83卷 54495-54520页

作者： Liang, Zhixiang Li, Yanshan Yu, Rui Zhang, Kaihao Guangdong Key Laboratory of Intelligent Information Processing Shenzhen University Shenzhen518061 China Institute of Intelligent Information Processing Shenzhen University Shenzhen518061 China College of Engineering and Computer Science Australian National University Canberra Australia

Aircraft type recognition has been researched deeply for plenty years on remote sensing and radar signal processing fields because of military needs. However, with the rapid development of UAV visualization technology in recent years, the advanced optical sensors on high-altitude drones are able to capture high-definition aircraft images of various aircraft flight postures, which brings aircraft type recognition into a brand-new research field. Different from remote sensing images, images with aircrafts in flight have larger intra-class difference caused by flight posture variation, which is 3D-view and more challenging for aircraft type recognition. Facing the new challenges, we propose a aircraft type recognition framework in 3D-view optical images. The framework consists of two stages: a coarse stage for aircraft contour segmentation and a fine stage for aircraft contour template matching. At the coarse stage, we propose a instance segmentation network CP-Deepsnake with a novel loss function CP loss which improves the accuracy of extracted aircraft contours by supervising the global distribution of contour points during contour deformation. At the fine stage, we utilize contour template matching to realize aircraft type recognition in 3D-view images. Based on IDSC contour feature descriptor, we propose a fast contour template matching approach with a new matching evaluation criterion and establish an aircraft contour template database for aircraft contour template matching. To train and test our aircraft type recognition framework, we build two datasets with 3D-view aircraft images of 10 aircraft types. Experiments show that our method achieves considerable recognition accuracy on the aircraft type testing dataset. Besides, we evaluate the proposed CP-Deepsnake network on two challenging instance segmentation public datasets SBD and KINS, where it compares favorably against state-of-the-art methods, which means our CP-Deepsnake network can be extended to more ins

关键词： Aircraft

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