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2024 IEEE International conference on image processing, ICIP 2024 - Proceedings

2024 IEEE International Conference on Image Processing, ICIP...

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31st IEEE International conference on image processing, ICIP 2024

ISBN: (纸本)9798350349399

The proceedings contain 593 papers. The topics discussed include: MDBFUSION: a visible and infrared image fusion framework capable for motion deblurring;prune channel and distill: discriminative knowledge distillation for semantic segmentation;imbalanced data robust online continual learning based on evolving class aware memory selection and built-in contrastive representation learning;privacy-preserving visual cues communication for hearing-impaired people using deep learning;transformer-based clipped contrastive quantization learning for unsupervised image retrieval;attention enhancement with parallel groups for remote sensing object detection;cross-domain few-shot in-context learning for enhancing traffic sign recognition;and recurrent 3-D multi-level visual transformer for joint classification of heterogeneous 2-D and 3-D radiographic data.

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Prompting DirectSAM for Semantic Contour Extraction in remote sensing images

Prompting DirectSAM for Semantic Contour Extraction in Remot...

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2025 IEEE International conference on Acoustics, Speech, and signal processing, ICASSP 2025

作者： Miao, Shiyu Chen, Delong Liu, Fan Zhang, Chuanyi Gu, Yanhui Guo, Shengjie Zhou, Jun College of Computer Science and Software Engineering Hohai University Nanjing China Hong Kong University of Science and Technology Hong Kong School of Computer and Electronic Information Nanjing Normal University Nanjing China School of Information and Communication Technology Griffith University QLD Australia

ISBN: (纸本)9798350368741

The Direct Segment Anything Model (DirectSAM) excels in class-agnostic contour extraction. In this paper, we explore its use by applying it to optical remote sensing imagery, where semantic contour extraction-such as identifying buildings, road networks, and coastlines-holds significant practical value. Those applications are currently handled via training specialized small models separately on small datasets in each domain. We introduce a foundation model derived from DirectSAM, termed DirectSAM-RS, which not only inherits the strong segmentation capability acquired from natural images, but also benefits from a large-scale dataset we created for remote sensing semantic contour extraction. This dataset comprises over 34k image-text-contour triplets, making it at least 30 times larger than individual dataset. DirectSAM-RS integrates a prompter module: a text encoder and cross-attention layers attached to the DirectSAM architecture, which allows flexible conditioning on target class labels or referring expressions. We evaluate the DirectSAM-RS in both zero-shot and fine-tuning setting, and demonstrate that it achieves state-of-the-art performance across several downstream benchmarks. © 2025 IEEE.

关键词： remote sensing Semantic contour extraction Vision-language learning

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BOUNDARY-AWARE BIAS LOSS FOR TRANSFORMER-BASED AERIAL image SEGMENTATION MODEL 47

BOUNDARY-AWARE BIAS LOSS FOR TRANSFORMER-BASED AERIAL IMAGE ...

引用

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

作者： Zhang, Yan Jiang, Xue Liu, Siqi Hu, Bo Gao, Xinbo Chongqing Univ Posts & Telecommun Chongqing Key Lab Image Cognit Chongqing Peoples R China Peking Univ Sch Software & Microelect Beijing Peoples R China

ISBN: (纸本)9781665405409

Inspired by the tremendous success of the transformer-based model in natural language processing (NLP), many efforts introduce the transformer-based model into the image processing tasks. However, naive transformer models have to downsample the image resolution to satisfy computational restrictions, thus discarding the local information, which is catastrophic for high-performance remote sensing image segmentation. Hence, this paper proposes a novel trainable boundaryaware bias loss function to enhance transformer-based models of extracting local information. On the Challenging ISPRS Potsdam dataset, two representative transformer-based models achieve remarkable performance improvements, proving the effectiveness of the proposed method.

关键词： Aerial image Segmentation Loss Function Transformer Deep Learning remote sensing

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FREQUENCY-SPATIAL DOMAIN INFORMATION FUSION NETWORK FOR PAN-SHARPENING 31

FREQUENCY-SPATIAL DOMAIN INFORMATION FUSION NETWORK FOR PAN-...

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2024 International conference on image processing

作者： Zhao, Mengjiao Ma, Mengting Gao, Ao Zhang, Wei Zhejiang Univ Sch Software Technol Hangzhou Peoples R China Zhejiang Univ Coll Comp Sci & Technol Hangzhou Peoples R China Zhejiang Univ Innovat Ctr Yangtze River Delta Jiaxing Peoples R China

ISBN: (纸本)9798350349405;9798350349399

Pan-sharpening aims to fuse panchromatic (PAN) images with low-resolution multi-spectral (LR-MS) images to generate high-resolution multi-spectral (HR-MS) images. Despite the impressive performance of existing learning-based methods, they are constrained by coarse fusion strategies in frequency or spatial domain. In this paper, we discover that PAN images can provide all the spatial textures required for HR-MS images, while spectral information must be provided jointly by PAN and LR-MS images. Inspired by this, we propose a noval frequency-spatial domain information fusion network for pan-sharpening, called FSDNet. Specifically, we design a Dual-Domain Information processing Module (DDPM) to construct FSDNet. It consists of a Frequency Domain Feature processing Block (FDB), a Spatial Domain Information processing Block (SDB), and an Information Fusion Block (IFB). The FDB in the frequency domain uses the Adaptive Amplitude Fusion Block (AAFB) and convolution layers to finely modulate amplitude and phase components, exploring global information. The SDB uses cascaded residual blocks to capture and enhance local information in the spatial domain. The IFB based on invertible neural networks (INNs) introduces Multi-Scale Self-Attention Block (MSAB), achieves effective information fusion and reduce information loss. Extensive experiments on the QuickBird and GaoFen-2 datasets demonstrate the effectiveness and superiority of our method.

关键词： Pan-sharpening remote sensing

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RJB-Net: Residual Deep Learning with Joint Bilateral Denoising Network for remote sensing image Fusion 3

RJB-Net: Residual Deep Learning with Joint Bilateral Denoisi...

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3rd IEEE Asian conference on Innovation in Technology, ASIANCON 2023

作者： Patel, Krupa Misra, Indranil Patel, Narendra Moorthi, S. Manthira Dhar, Debajyoti Birla Vishvakarma Mahavidyalaya Engineering College Dept. of Computer Science and Engineering Anand India Signal and Image Processing Group Ahmedabad India

ISBN: (纸本)9798350302288

remote sensing image fusion amalgamate information from panchromatic and multispectral remote sensing images to generate an optimum representative image. In this paper, we have proposed a novel method for image fusion known as Residual Deep Learning with Joint Bilateral Denoising Network (RJB-Net). The RJB-Net procedure is initiated with panchromatic image denoising using a joint bilateral filter to suppress noise and retain edge information. RJB-Net model is trained at image patches covering all major spectral classes. The denoised panchromatic image along with multispectral data is input to the residual deep learning network for inference and generates a fused image. The remote sensing datasets used for RJB-Net training and evaluation are Indian Cartosat-1 panchromatic and Resourcesat-2 multispectral imagery. The experiment section compares the RJB-Net denoising output with prominent spatial domain image filtering methods both qualitatively and quantitatively. It is found that RJB-Net fused images inject the spatial information from panchromatic images in a controlled manner while retaining the spectra characteristics of multispectral data. The merging performance of RJB-Net is compared with state-of-the-art remote sensing image fusion techniques that range from classical to recent deep learning model. The proposed method RJB-Net generates superior results among all fusion methods at different feature targets and compared the performance both visually as well as with image quality metrics. © 2023 IEEE.

关键词： remote sensing

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THE UAV remote sensing VEHICLE TRACKING BASED ON IMPROVED FAIRMOT

THE UAV REMOTE SENSING VEHICLE TRACKING BASED ON IMPROVED FA...

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IET International Radar conference 2023, IRC 2023

作者： Fang, Hao Wu, Jiapeng Tang, Linbo School of Information and Electronics Beijing Institude of Technology Beijing China

ISBN: (纸本)9781839539954

Multi-object tracking (MOT) is an important problem in Earth-observation remote sensing task and has wide application. In recent years, UAV has been widely used in middle to high altitude remote sensing observation tasks, undertaking various tasks such as object detection and tracking, natural disaster monitoring and so on. The application of multi-object tracking to UAV platform has become one of the research trends. In this paper, a vehicle tracking algorithm based on UAV platform is proposed. In view of the size conflict between the network and the input image, bilinear interpolation method is adopted to resize the image and evaluate the loss. UAVDT dataset is used to train the algorithm and evaluate the model, thus achieving high-precision detection and tracking. At the same time, it has a high processing speed and achieves near-real-time performance. © The Institution of Engineering & Technology 2023.

关键词： Object tracking

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UAV remote sensing image Dehazing Based on Multi-Dimensional Saliency Awareness Unequal Network 48

UAV Remote Sensing Image Dehazing Based on Multi-Dimensional...

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

作者： Zheng, Ruohui Zhang, Libao Beijing Normal University School of Artificial Intelligence Beijing100875 China

ISBN: (纸本)9781728163277

Current UAV image haze removal methods often suffer from problems of insufficient dehazing and spectrum distortion, especially in regions with rich spectrum and texture information. In this paper, we propose a multi-dimensional saliency awareness unequal network to avoid texture loss and color distortions. First, we design an unequal network structure, which enhances local color and texture detail feature learning. Specifically, we propose a saliency dense block, which employs a saliency map to guide the network to pay attention to the regions with rich spectrum and texture information unequally. Second, a multi-dimensional saliency detection method is proposed. It fuses the features of different dimensions to extract the salient regions, to obtain the spectrum and texture information. Finally, a loss function combining mean square error and edge loss is defined to enhance the learning of the texture. The experimental results demonstrate that the proposed method is capable of restoring color information as well as reserving texture details, especially for salient regions of UAV hazy images. © 2023 IEEE.

关键词： image enhancement remote sensing image dehazing saliency analysis unmanned aerial vehicle

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Grassland botanical species identification based on MTMF model

Grassland botanical species identification based on MTMF mod...

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

作者： Huang, Mengge Wang, Xinhong Ma, Lingling Ouyang, Guangzhou Liu, Wei Zhu, Xiaohua Niu, Yifang Tang, Lingli Aerospace Information Research Institute Chinese Academy of Sciences Beijing100094 China Key Laboratory of Quantitative Remote Sensing Information Technology Chinese Academy of Sciences Beijing100094 China School of Electronic Electrical and Communication Engineering University of Chinese Academy of Science Beijing100049 China Institute of Botany Chinese Academy of Sciences Beijing100093 China

ISBN: (纸本)9781510666351

Grassland plays an important role in regional economic development and terrestrial ecosystem security. remote sensing technology is fast, efficient and low cost. The use of remote sensing technology to discriminate grassland species is an important way to monitor the population dynamics and botanical community succession in grassland. Such information is conducive to the timely and accurate detection of changes in the grassland ecological environment and provides an important reference for the scientific management of grassland ecosystems and the construction of an ecologically aware civilization. In this paper, based on the UAV hyperspectral remote sensing image of natural grassland in Inner Mongolia and the linear spectral mixture model, the MTMF (mixture tuned matched filtering) model was used for grass species identification, and the results were verified with FVC (fractional vegetation cover) estimation. The results showed that this method could achieve effective identification and extraction of the dominant species of Leymus chinensis in the study area. © 2023 SPIE.

关键词： Unmanned aerial vehicles (UAV)

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A Dual-Branch Network Based on ViT and Mamba for Semantic Segmentation of remote sensing image 2

A Dual-Branch Network Based on ViT and Mamba for Semantic Se...

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2nd IEEE International conference on signal, Information and Data processing, ICSIDP 2024

作者： An, Ke Wang, Ying Chen, Liang Wang, Yupie Beijing Institute of Technology National Key Laboratory for Space-Born Intelligent Information Processing Beijing China Shanghai Academy of Spaceflight Technology Shanghai China

ISBN: (纸本)9798331515669

Semantic segmentation of remote sensing images has significant applications across various scenarios. The prevailing frameworks include Convolutional Neural Network (CNN) and Transformer. However, CNN is limited by the receptive field of convolutions, while the Transformer is constrained by computational complexity, which restricts attention calculations to local windows and fails to effectively address long-range dependency modeling. The efficient Mamba architecture, characterized by linear complexity, offers a promising solution to these challenges. Inspired by Mamba, we propose a dual-branch network based on ViT and Mamba. The Vision Transformer (ViT) branch incorporates the Swin Transformer to model spatial details while maintaining computational complexity within acceptable bounds. Complementarily, the Mamba branch efficiently captures global context and long-range dependencies. Additionally, to suppress noise and conflicting information arising from the fusion of features from different frameworks, we design the Cross-Model Fusion Module (CMFM) and the Cross-Model Relevance Loss (CMRLoss) to achieve semantic consistency in the fusion process. The comprehensive experimental findings on the commonly utilized GaoFen-2 and iSAID datasets clearly illustrate the advantages of our proposed approach compared to the leading methods in the field. © 2024 IEEE.

关键词： Semantic Segmentation

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TransCGAN-based Parameter Extraction Framework for SAR image Simulation 2

TransCGAN-based Parameter Extraction Framework for SAR Image...

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2nd IEEE International conference on signal, Information and Data processing, ICSIDP 2024

作者： Deng, Sidan He, Jingfei Mao, Yongfei Zhao, Liangbo Chen, Liang Shi, Hao Beijing Institute of Technology National Key Laboratory of Science and Technology on Space-Born Intelligent Information Processing Chongqing Innovation Center Chongqing China Beijing Institute of Technology National Key Laboratory of Science and Technology on Space-Born Intelligent Information Processing Beijing China China Academy of Space Technology Institute of Remote Sensing Satellite Beijing China Beijing Institute of Technology National Key Laboratory of Science and Technology on Space-Born Intelligent Information Processing Chongqing Innovation Center Beijing China

ISBN: (纸本)9798331515669

Synthetic Aperture Radar (SAR) images have a wide range of applications due to their all-weather and all-day working conditions. However, SAR images with different scenarios and imaging conditions are insufficient or even rare, which is required in specific SAR image tasks. Fortunately, SAR image simulation technology can provide ample simulated images under these conditions at a low cost, addressing the scarcity of specific real SAR data. Accurate parameters are crucial for obtaining high-quality simulated images. However, it is time-consuming and labour-intensive to adjust parameters manually, and it often fails to achieve accurate simulation parameters. To tackle this problem, this paper proposes a TransCGAN-based SAR image simulation method that combines deep learning with traditional methods. By training TransCGAN, a conditional generative adversarial network (CGAN) integrated with Transformer architecture, a mapping between SAR images and simulation parameters is established. This enables the extraction of simulation parameters directly from the real SAR image, guided by the corresponding real SAR image. Ultimately, the parameters are subsequently converted into simulated SAR images via simulation software. Experimental results demonstrate that our TransCGAN-based method can effectively extract accurate simulation parameters from real SAR images, resulting in simulated images holding high similarity to real images. © 2024 IEEE.

关键词： Generative adversarial networks

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