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Diverse image Captioning via Sequential Variational Transformer and Muti-feature Fusion 24

Diverse Image Captioning via Sequential Variational Transfor...

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7th International conference on signal processing and Machine Learning, SPML 2024

作者： Chen, Siyu Xiang, Bo Liu, Junrong Aerial Survey and Remote Sensing Bureau of China Coal Geology Administration Xi'an China School of Computer Science and Technology China University of Mining and Technology Jiangsu Xuzhou China

ISBN: (纸本)9798400717192

The transformer-based image captioning models have shown remarkable performance based on the powerful sequence modeling capability. However, most of them focus only on learning deterministic mappings from image space to caption space, i.e., learning how to improve the accuracy of predicting average"captions, which generally leads to common words, repeated phrases and single sentence. In this paper, we propose a novel multi-feature fusion based sequential variational transformer for diverse image captioning (MF-SVT-DIC), aiming to learn one-to-many projections and improve the diversity and accuracy of generated captions simultaneously. Specifically, we incorporate sequential variational inference into the traditional transformer-based captioning model to model the word-level diversity. Meanwhile, we design a fusion module to take advantage of both grid features and region features, facilitating the generation of fine-grained captions. The experimental results demonstrate that our method achieves significant gains in term of both diversity and accuracy compared with the state-of-the-art diverse image captioning models. © 2024 ACM.

关键词： image fusion

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Full-scale assessment of pansharpening: why literature indexes may give contradictory results and how to avoid such an inconvenience 29

Full-scale assessment of pansharpening: why literature index...

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conference on image and signal processing for remote sensing XXix

作者： Alparone, Luciano Garzelli, Andrea Lolli, Simone Zoppetti, Claudia Univ Florence Dept Informat Engn I-50139 Florence Italy Univ Siena Dept Informat Engn & Math I-53100 Siena Italy Tito Scalo Consiglio Nazl Ric CCNR IMAA I-85050 Potenza Italy Univ Politecn Cataluna CommSensLab Barcelona 08034 Spain

ISBN: (纸本)9781510666955;9781510666962

In this paper, we wish to explain the contradiction of quality assessments of pansharpening carried out at full and reduced spatial scales. It seems that at full scale, methods based on component substitution (CS) are quantitatively poorer than the other methods, but this depends on the intrinsic space varying misregistration between the two datasets. At reduced scale, the local shifts are divided by the MS-to-Pan scale ratio and thus they tend to vanish. The problem of full-scale quality indexes is that they were originally validated on aerial multispectral (MS) data, with synthetic panchromatic (Pan) and thus total absence of misregistration. In the presence of local misregistration due to inaccurate information of the height of the imaged surface, CS methods locally align the lowpass MS components towards the sharpening Pan, thereby preserving the geometry of the scene;all the other methods produce fading contours because of shifts. The favorable property of CS, however, impacts against the (spectral) consistency property of Wald's protocol, developed when the misalignments between MS and Pan was a small fraction of the pixel size, and hence negligible. In this perspective, methods that do not shift the original MS information are better, even though the visual quality of fading contours is worse. After exposing and explaining the contradiction between full- and reduced-scale assessments, we perform an in-depth analysis of the spectral and spatial consistency indexes of three widespread full-scale protocols: QNR, KQNR and HQNR. We investigate the robustness to shifts of all consistency indexes and propose to couple the spectral index and the spatial index that are least sensitive to shifts. In this way, the ranking of methods of reduced-scale assessments is preserved in full-scale assessments.

关键词： Full-scale assessment image fusion pansharpening satellite acquisition geometry very high resolution (VHR) multispectral (MS) images

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DSDN-Net: An Effective Network for Semantic Segmentation in Open-Pit Coal Mining Areas for Land Cover Recognition

DSDN-Net: An Effective Network for Semantic Segmentation in ...

引用

International conference on Acoustics, Speech, and signal processing (ICASSP)

作者： Jiaqi Li Ming Ma College of Computer Science-College of Software Inner Mongolia University Hohhot China

ISBN: (数字)9798350368741

ISBN: (纸本)9798350368758

Currently, existing methods in land cover recognition in open-pit coal mining areas face the issue of insufficient accuracy due to multiscale and blurred boundaries when processing remote sensing images. This paper introduces a remote sensing image semantic segmentation network, DSDN-Net, to tackle the issues. DSDN-Net adopts MobileNetV2 as the backbone, and a spatial pyramid pooling structure, Dynamic Snake Dense-ASPP (DSDN-ASPP), which enhances the model’s receptive field based on cross-layer connections is designed to increase the model’s receptive field by introducing dynamic snake convolution and utilizing depthwise convolutions with different kernel sizes, allowing the model to focus more on spatial features in remote sensing images. To handle blurred boundaries in remote sensing image, the decoder of DSDN-Net incorporates a Convolutional Block Attention Module (CBAM) to enhance precision. A dataset containing 5440 remote sensing images from the open-pit coal mining area is constructed using high-resolution remote sensing image. Experimental results on the open-pit coal mining area dataset demonstrate that the proposed DSDN-Net outperforms existing methods in multiple performance metrics.

关键词： image recognition Accuracy Convolution Target recognition Semantic segmentation Land surface signal processing algorithms Feature extraction Coal mining remote sensing

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7th International conference on Computer, Communication and signal processing, ICCCSP 2023

7th International Conference on Computer, Communication and ...

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7th International conference on Computer, Communication and signal processing, ICCCSP 2023

ISBN: (纸本)9783031398100

The proceedings contain 26 papers. The special focus in this conference is on Computer, Communication, and signal processing. The topics include: Forecast of Movie Sentiment Based on Multi Label Text Classification on Rotten Tomatoes Using Multiple Machine and Deep Learning Technique;an Ensemble Approach to Hostility Detection in Hindi Tweets;an Optimized Framework for Diabetes Mellitus Diagnosis Using Grid Search Based Support Vector Machine;inquisition of Vision Transformer for Content Based Satellite image Retrieval;impact of Spectral Domain Features for Small Object Detection in remote sensing;application of Phonocardiogram and Electrocardiogram signal Features in Cardiovascular Abnormality Recognition;analyzing Cricket Biomechanical Parameters Through Keypoint Detection and Tracking;multi-feature Based Sea–land Segmentation for Multi-spectral and Panchromatic remote-sensing imagery;deoxyribonucleic Acid Cryptography Based Least Significant Byte Steganography;detection and Estimation of Diameter of Retinal Vessels;a Simple Hybrid Local Search Algorithm for Solving Optimization Problems;Performance Study of RIS Assisted NOMA Based Wireless Network with Passive IoT Communication;multi-channel Man-in-the-Middle Attacks Against Protected Wi-Fi Networks and Their Attack Signatures;a Study in Analysing the Critical Determinants of Internet of Things (IoT) Based Smart processing for Sustainable Supply Chain Management;IOT Enabled Rover for remote Survey of Archeological Areas;an Embedded System for Smart Farming Using Machine Learning Approaches;an Intrusion Detection System for Securing IoT Based Sensor Networks from Routing Attacks;automated Summarization of Gastrointestinal Endoscopy Video;interpretation of Feature Contribution Towards Diagnosis of Diabetic Retinopathy from Exudates in Retinal images;false Positive Reduction in Mammographic Mass Detection;Collaborative CNN with Multiple Tuning for Automated Coral Reef Classification.

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2023 5th International Symposium on signal processing Systems, SSPS 2023

2023 5th International Symposium on Signal Processing System...

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5th International Symposium on signal processing Systems, SSPS 2023

ISBN: (纸本)9798400700040

The proceedings contain 11 papers. The topics discussed include: accurate shadow height measurement technology of the SAR image;millimeter wave radar fall detection algorithm based on improved transformer;an end-to-end learning based covolutional neural network for single image defogging algorithm;ornaments and barlines recognition of numbered musical notation using YOLOv5;study on hyperspectral remote sensing images of GF-5 de-blurring based on sparse representation;design and implementation of target tracking system in low illumination environment based on FPGA;SAR image geometry correction technology based on block parallel signal processing;speech recognition method based on deep learning of artificial intelligence: an example of BLSTM-CTC model;and high precision reference measurement technology for mechanical scanning radar.

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A FAST BACK-PROJECTION ALGORITHM BASED ON EQUIVALENT SLANT RANGE MODEL FOR HIGH-SQUINT SAR IMAGING

A FAST BACK-PROJECTION ALGORITHM BASED ON EQUIVALENT SLANT R...

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IET International Radar conference 2023, IRC 2023

作者： Xu, GaoTian Ca, Tianyi Li, Yachao Zhang, Tinghao National Key Laboratory of Radar Signal Processing Xidian University Xi'an China Beijing Institute of Remote-Sensing Equipment Beijing China

ISBN: (纸本)9781839539954

Because back-projection algorithm (BPA) is independent of the azimuth-invariant hypothesis of the echo signal, it has significant advantages for synthetic aperture radar (SAR) imaging with arbitrary trajectory. Although Cartesian fast back-projection algorithm (CFBPA) is proposed to improve the efficiency of BPA, for high-squint SAR (HS-SAR) imaging, the geometric configuration of HS-SAR greatly enhances the space-variant property of the spectrum, which leads to the ineffectively compressed spectrum and decreases the efficiency of the sub-image merging in CFBPA processing. In this paper, an equivalent slant range model (ESRM) based on hybrid coordinate system (HCS) is proposed for HS-SAR imaging. Benefiting from the designed ESRM, the space-variant property of the wavenumber spectrum can be significantly degraded to improve performance in spectrum compression and accelerates the sub-image merging. Promising results from simulation experiments are provided to validate the high performance of the proposed algorithm. © The Institution of Engineering & Technology 2023.

关键词： image enhancement

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MVITP: Multi-View image-Text Perception for Few-Shot remote sensing image Classification

MVITP: Multi-View Image-Text Perception for Few-Shot Remote ...

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International conference on Acoustics, Speech, and signal processing (ICASSP)

作者： Chen Yang Tongtong Liu Didi Jiao Wenhui Li College of Computer Science and Technology Jilin University Changchun China

Few-shot learning has been extensively applied in current remote sensing image classification, enabling rapid identification of new classes by leveraging prior knowledge effectively. However, current methods mainly rely on image modality to address the issue of low intra-class similarity and high interclass similarity, while the utilization of multimodal methods in remote sensing tasks remains largely unexplored. Therefore, we propose a novel framework for few-shot remote sensing image classification, named multi-view image-text perception (MVITP). Specifically, it leverages maximum mutual information across multiple views to train an image encoder and generate image features. A text encoder is employed to generate text features. Next, we introduce a multimodal fusion encoder to capture the similarity between image features and text features. Finally, class predictions are further made by computing the similarity between the support set and the query set. We conduct experiments on three remote sensing datasets, demonstrating the outstanding performance of MVITP.

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MULTI-TEMPORAL CHANGE DETECTION BASED ON CHINA'S DOMESTIC HYPERSPECTRAL remote sensing SATELLITE imageS 28

MULTI-TEMPORAL CHANGE DETECTION BASED ON CHINA'S DOMESTIC HY...

引用

conference on image and signal processing for remote sensing XXVIII

作者： Lu, Xuanning Liu, Sicong Du, Kechen Tongji Univ Coll Surveying & Geo Informat Shanghai 200092 Peoples R China

ISBN: (纸本)9781510655386;9781510655379

Comparing with the multispectral remote sensing image, hyperspectral image (HSI) has higher spectral resolution, a near continuous spectral signature, thus can represent fine spectral variations that occurred in the temporal domain. This allows more spectral changes to be detected, especially major changes that reflected on the overall spectral signature (associating with the abrupt land-cover transitions), as well as subtle changes that reflect only on a portion of the spectral signature (associating with the change of physicochemical properties of the land-cover classes). Currently, there are some available hyperspectral change detection (CD) data sets. However, they have the following drawbacks. First, there is a lack of diversity in the data source;all data sets were created using the Hyperion sensor mounted on the E0-1 satellite. Second, these data sets mainly concentrate on the river and agriculture scenes, which lose their diversity for representing different land-covers. In this paper, we construct three new change detection data sets by using the multitemporal images acquired by the China's new generation of hyperspectral satellites, i.e., OHS, GF-5 and ZY1-02D. These data sets present various event-driven land-cover changes, such as new building construction, crop replacements, and the expansion of energy facilities. Then a novel unsupervised hyperspectral change detection approach is proposed based on the intrinsic image decomposition (IID). Experimental results confirmed the effectiveness of the proposed approach in terms of higher overall accuracy by comparing with the reference techniques.

关键词： hyperspectral images change detection GF-5 ZY1-02D OHS intrinsic image decomposition

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VSIP 2023 - Proceedings of the 2023 5th International conference on Video, signal and image processing

VSIP 2023 - Proceedings of the 2023 5th International Confer...

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5th International conference on Video, signal and image processing, VSIP 2023

ISBN: (纸本)9798400709272

The proceedings contain 33 papers. The topics discussed include: dangerous goods detection in x-ray security inspection images based on improved YOLOv7;a lightweight anchor-free detector using cross-dimensional interactive feature balance for SAR ship detection;network intrusion detection based on federated learning with inherited private models;deep learning-based experimental system design for fatigue driving detection;combined classification of hyperspectral and LiDAR data based on dual-channel cross-transformer;a video classification algorithm based on visual-audio cross-attention;research on hyperspectral image classification based on improved deep cross-domain few-shot learning;multi-object detection and classification in construction sites based on YOLOv5;and joint classification of multi-source remote sensing data based on multi-scale features and attention mechanism.

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COARSE-TO-FINE UNSUPERVISED CHANGE DETECTION FOR remote sensing imageS VIA OBJECT-BASED MRF AND INCEPTION UNET 47

COARSE-TO-FINE UNSUPERVISED CHANGE DETECTION FOR REMOTE SENS...

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47th IEEE International conference on Acoustics, Speech and signal processing (ICASSP)

作者： Hou, Xuan Bai, Yunpeng Shi, Haonan Li, Ying Northwestern Polytech Univ Sch Comp Sci Natl Engn Lab Integrated AeroSp Ground Ocean Big Shaanxi Prov Key Lab Speech Image Informat Proc Xian 710129 Peoples R China Aberystwyth Univ Fac Business & Phys Sci Dept Comp Sci Aberystwyth SY23 3DB Dyfed Wales

ISBN: (纸本)9781665405409

With the rapid development of various satellite sensor techniques, remote sensing imagery has been an important source of data in change detection applications. This paper aims to propose an unsupervised change detection method based on Object-based Markov Random Filed (OMRF) and Inception UNet (IUNet). Our method first utilizes a difference image (DI) obtained from two bi-temporal images as the initial feature, and proposes the OMRF algorithm based on homogeneous region to pre-classify the DI thus derive the coarse change map. The IUNet is then constructed to extract the points with high confidence from the coarse change map for training. Eventually, the trained model is fed to classify the original feature, then the final change map is obtained. Experimental results indicate that our method yields great detection results even without supervision.

关键词： change detection object-based markov random filed coarse-to-fine model unsupervised learning

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