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Attention-based U-Net for building extraction from remote sensing images

Attention-based U-Net for building extraction from remote se...

引用

2022 International conference on image, signal processing, and Pattern Recognition, ISPP 2022

作者： Zhang, Qiong College of Geomatics and Geoinformation Guilin university of technology Guangxi Guilin China

ISBN: (纸本)9781510654846

To extract ground objects from remote sensing images based on deep learning method is one of the current research hotspots, and building information has attracted much attention as an important artificial feature. Convolutional neural networks have shown great potential for building extraction tasks. In view of the current research status, this paper proposes a lightweight improved network ECAU-Net that combines semantic segmentation network U-Net and efficient channel attention mechanism, and applies it to automatic extraction of buildings from high-resolution remote sensing images. Experiments show that the extraction method proposed in this paper shows good extraction results on the Massachusetts building data set, and the accuracy, recall, precision, and F1 score are better than the original U-Net. Therefore, this method does have certain advantages. © 2022 SPIE.

关键词： remote sensing

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RSI-Mix: Data Augmentation Method for remote sensing image Classification 7

RSI-Mix: Data Augmentation Method for Remote Sensing Image C...

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7th International conference on Intelligent Computing and signal processing, ICSP 2022

作者： Lu, Xingshun Zhang, Chao Ye, Qihong Wang, Chao Yang, Chuansheng Wang, Quanqing Zhejiang Ocean University School of Information Engineering Zhoushan China Zhejiang Ocean University Key Laboratory of Oceanographic Big Data Mining and Application of Zhejiang Province Zhoushan China

ISBN: (纸本)9781665478571

Data augmentation is a common strategy to improve the performance of computer vision tasks. Regrettably, current data augmentation methods are often designed for images in RGB format and few are studied for remote sensing images. In this paper, we find that the way in which remote sensing data are obtained provides a realistic possibility for cropping images to create new ones. Based on our analyses, we propose RSI-Mix for Sentinel-2 satellite image classification. RSI-Mix is designed to cut and paste two remote sensing images of the same category according to random masks. The key inspiration of RSI-Mix is that the classification of remote sensing images is not strictly based on image texture but based on band features. The information fusion of the same area from different sources is beneficial to make an area contain more band features. Experiments show that the model with RSI-Mix is more stable and has higher performance. © 2022 IEEE.

关键词： image classification

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Learning To Generate 3d Representations of Building Roofs Using Single-View Aerial imagery 48

Learning To Generate 3d Representations of Building Roofs Us...

引用

48th IEEE International conference on Acoustics, Speech and signal processing, ICASSP 2023

作者： Khomiakov, Maxim Mahou, Alejandro Valverde Sánchez, Alba Reinders Frellsen, Jes Andersen, Michael Riis Technical University of Denmark Denmark

ISBN: (纸本)9781728163277

We present a novel pipeline for learning the conditional distribution of a building roof mesh given pixels from an aerial image, under the assumption that roof geometry follows a set of regular patterns. Unlike alternative methods that require multiple images of the same object, our approach enables estimating 3D roof meshes using only a single image for predictions. The approach employs the PolyGen, a deep generative transformer architecture for 3D meshes. We apply this model in a new domain and investigate the sensitivity of the image resolution. We propose a novel metric to evaluate the performance of the inferred meshes, and our results show that the model is robust even at lower resolutions, while qualitatively producing realistic representations for out-of-distribution samples. © 2023 IEEE.

关键词： 3D Reconstruction aerial imagery buildings generative models remote sensing

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Digital image Tamper-Forgery Detection in Security 2

Digital Image Tamper-Forgery Detection in Security

引用

2nd International conference on Recent Trends in Microelectronics, Automation, Computing, and Communications Systems, ICMACC 2024

作者： Kolekar, Sachin M. Pimpalkar, Aarti P. More, Rahul P. Hirve, Sumit A. Gulame, Mayuresh B. Thorat, Nilesh N. MIT Art Design and Technology University CSE Department Pune India

ISBN: (纸本)9798350366570

Any industry that employs digital photos has concerns about image security. images from the crime scene, biometric pictures, suspect photos, and other sources have historically been used by public safety and forensics. The application of digital photographs in this discipline has significantly increased since the advent of digital imaging. Digital image processing has made it easier to alter photos while also inspiring the development of various new forensic investigation techniques. The public's easy access to several snipping and image altering applications raises questions about the veracity of digital pictures. It is used as documentation for a variety of reasons and provides strong evidence in a wide range of crimes. As picture modification and editing tools have advanced, so too have the methods for generating and editing photos. The two most popular techniques for photo counterfeiting are image splicing and copy-and-move forgeries. To conceal or display an incorrect scenario, an image can be spliced, or merged from two photos. It is possible to copy and paste a portion of one image into another. This study looks at the various types of digital image frauds and the tools used to identify them. The techniques currently in use for identifying fake images were examined. © 2024 IEEE.

关键词： Crime

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Analysis of Factors Affecting image Quality of Optical Maneuvring Imaging Satellite 11th

Analysis of Factors Affecting Image Quality of Optical Maneu...

引用

11th International conference on signal and Information processing, Network and Computers, ICSINC 2023

作者： Shao, Jun Wang, Xiaoyan Lv, Zheng Li, Xiaojin Zhong, Huimin China Center for Resources Satellite Data and Application Beijing100094 China

ISBN: (纸本)9789819721191

Because the imaging conditions have a great influence on the image quality of optical maneuvring imaging satellite, it is necessary to analyze the influencing factors to ensure the image quality. In this paper, the influence of imaging geometric parameters on the quality of high resolution multi-mode images is analyzed. The main manifestation is image supersaturation;Then, the influence of atmospheric conditions on image quality is analyzed. The main performance is image blur. The atmospheric parameters of the camera observation area are obtained synchronously by atmospheric synchronous correction instrument, and the image is clear after atmospheric correction;Finally, the effect of the side-looking angle on the Ground Sample Distance of the optical satellite is analyzed. The Ground Sample Distance decreases with the increase of the side-looking angle, and some suggestions are given to meet the user's application requirements. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2024.

关键词： image quality

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A visual window convolutional attention mechanism based building extraction method for remote sensing images 3

A visual window convolutional attention mechanism based buil...

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3rd International conference on Advanced Algorithms and signal image processing, AASIP 2023

作者： Wang, Han Li, Jingwen Tang, Wenkang College of Geomatics and Geoinformation Guilin University of Technology Guilin541004 China Ecological Spatiotemporal Big Data Perception Service Laborator Guilin Guangxi541004 China

ISBN: (纸本)9781510668522

To solve the problem of low accuracy and slow speed of building extraction due to complex semantic information of remote sensing image, Visual Window Convolutional Attention-Net(VWCA-Net) is constructed in this paper. VWCA-Net is based on an encoding-decoding framework, with the ResNet-50 used as the encoding backbone network in the encoding phase;the Visual Window Convolutional Attention Fusion (VWCAF) module is used to fuse shallow and deep features in the decoding phase. The VWCAF module first filters the feature maps containing buildings and generates Mask by windowing the sliced deep feature maps and fusing visual features, and then fuses the weights of CBAM to remove the features that do not contain the building feature maps, thus making the building boundary segmentation more accurate and faster. The experiment show that the Recall and mIoU of VWCA-Net on WHU dataset are better than SegNeXt network by 0.87% and 0.89%, respectively, but the computational effort is reduced by 17.38 FLOPs/G, which is a more effective and practical application method for building extraction. © COPYRIGHT SPIE. Downloading of the abstract is permitted for personal use only.

关键词： Decoding

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Evolutionary Algorithms Based Feature Selection for remote sensing image Classification 8

Evolutionary Algorithms Based Feature Selection for Remote S...

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8th International conference on Frontiers of signal processing, ICFSP 2023

作者： Amri, Mohammed Bilel Yedjour, Dounia Larabi, Mohammed El Amin Centre des Techniques Spatiales Agence Spatiale Algérienne Arzew Algeria Université Mohamed Boudiaf USTO-MB Département Informatique Laboratoire SIMPA Oran Algeria Université des Sciences et de la Technologie D'Oran Mohamed Boudiaf USTO-MB Oran Algeria Algerian Space Agency Algeria

ISBN: (纸本)9798350308792

The amount of spatial datasets currently available has fundamentally increased and become an essential source of information for extremely important and strategic decisions. Due to the redundant and unused features, the direct usage of raw data might significantly affect the final decisions. To solve this issue, feature selection (FS), as a crucial preprocessing step, is used. It seeks to pick out the most relevant information in order to reduce the data dimensionality and the computational resources, and then increases the accuracy. This paper proposes the Wrapper Feature Selection (WFS) technique for remote sensing data classification. More specifically, evolutionary algorithms as Particle Swarm Optimization (PSO) and Genetic Algorithms (GA) have been utilized to pinpoint the optimum features of the Support Vector Machine (SVM) classifier. To this end, hyperspectral images from Earth Observing-1 (EO-1) have been used to test this strategy.1 our work demonstrates that a good performance can be achieved for FS and image classification. © 2023 IEEE.

关键词： Genetic algorithms

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AN UNSUPERVISED CROSS-MODAL HASHING METHOD ROBUST TO NOISY TRAINING image-TEXT CORRESPONDENCES IN remote sensing 29

AN UNSUPERVISED CROSS-MODAL HASHING METHOD ROBUST TO NOISY T...

引用

IEEE International conference on image processing (ICIP)

作者： Mikriukov, Georgii Ravanbakhsh, Mahdyar Demir, Beguem Tech Univ Berlin Berlin Germany

ISBN: (数字)9781665496209

ISBN: (纸本)9781665496209

The development of accurate and scalable cross-modal image-text retrieval methods, where queries from one modality (e.g., text) can be matched to archive entries from another (e.g., remote sensing image) has attracted great attention in remote sensing (RS). Most of the existing methods assume that a reliable multi-modal training set with accurately matched text-image pairs is existing. However, this assumption may not always hold since the multi-modal training sets may include noisy pairs (i.e., textual descriptions/captions associated to training images can be noisy), distorting the learning process of the retrieval methods. To address this problem, we propose a novel unsupervised cross-modal hashing method robust to the noisy image-text correspondences (CHNR). CHNR consists of three modules: 1) feature extraction module, which extracts feature representations of image-text pairs;2) noise detection module, which detects potential noisy correspondences;and 3) hashing module that generates cross-modal binary hash codes. The proposed CHNR includes two training phases: i) meta-learning phase that uses a small portion of clean (i.e., reliable) data to train the noise detection module in an adversarial fashion;and ii) the main training phase for which the trained noise detection module is used to identify noisy correspondences while the hashing module is trained on the noisy multi-modal training set. Experimental results show that the proposed CHNR outperforms state-of-the-art methods.

关键词： cross-modal retrieval unsupervised contrastive learning remote sensing caption-noise

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UHV transmission line monitoring based on satellite remote sensing

UHV transmission line monitoring based on satellite remote s...

引用

2023 International conference on image, signal processing, and Pattern Recognition, ISPP 2023

作者： Zhang, Yunfeng Zhang, Peng Shen, Jianliang Wei, Wenli Yue, Lingping Zhang, Jun State Grid Huzhou Power Supply Company Zhejiang Huzhou313000 China Center of Mass Entrepreneurship Innovation State Grid Zhejiang Electric Power Co. Ltd Zhejiang Hangzhou310000 China NARI-TECH Nanjing Control Systems Ltd Jiangsu Nanjing210000 China

ISBN: (纸本)9781510666351

UHV transmission lines have problems such as high impedance and low loss. This paper mainly introduces the monitoring method of UHV transmission and distribution line based on satellite remote sensing technology, aiming at avoiding or reducing property losses caused by line problems and maintaining people's normal life. In this paper, the experimental system construction and quantitative analysis methods are mainly used, and the results of two kinds of line detection are compared by comparison. The experimental data shows that in different types of fault lines, the test time of fault is within 0.1s. © 2023 SPIE.

关键词： Electric lines

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Accelerating a Two-Stage Object Detector for High Quality In-Orbit remote sensing 28

Accelerating a Two-Stage Object Detector for High Quality In...

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conference on image and signal processing for remote sensing XXVIII

作者： Kang, Jaemin Kwon, Jinse Kim, Hyungshin Chungnam Natl Univ Daejeon South Korea

ISBN: (纸本)9781510655386;9781510655379

Object detection from remote sensing images has been performed on the ground. Recently, on-board object detection has been studied only to show its feasibility with single-stage detectors. However, highly accurate models such as two stage detectors are compute intensive so that they are too slow to run on power-constrained on-board computers. In this paper, we propose a speed-up method for two-stage detectors. Two-stage detectors extract features and ROIs(Region of Interest) in the first stage and then classify them at the second stage. This structure gives high accuracy but induces large inference latency. In remote sensing images from satellites, object size is small relative to the whole image. Based on this characteristic, we propose to exclude features related to the large objects in the first stage. To verify our concept, we have selected various R-CNN models as two-stage object detectors. We have implemented our methods on two NVIDIA Jetson boards. We have achieved 1.8x speed up in inference latency with 5% accuracy drop with the small object dataset.

关键词： On-board Object detection Feature Pyramid Network Accelerate Two-stage Detector RoIAlign Small object DOTA Dataset

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