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检索条件"任意字段=Conference on Image and Signal Processing for Remote Sensing XXIX"

共 3572 条记录，以下是41-50 订阅

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A Fast Saturation Based Dehazing Framework with Accelerated Convolution and Attention Block

A Fast Saturation Based Dehazing Framework with Accelerated ...

引用

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

作者： Shuocheng Wang Jiaming Liu Yilian Zhong Ruoxi Zhu Jiazheng Lian Hao Zhang Yibo Fan State Key Laboratory of Integrated Chips and Systems Fudan University

ISBN: (数字)9798350368741

ISBN: (纸本)9798350368758

Real-time image dehazmg is crucial for applications such as autonomous driving, surveillance, and remote sensing, where haze can significantly reduce visibility. However, many deep learning algorithms are hindered by large model sizes, making real-time processing difficult to achieve. Several fast and lightweight dehazing networks rely on estimating K(x), but they often fail to deliver satisfying performance. In this paper, we present a novel fast dehazing framework built upon the saturation-based algorithm. We design a new convolution module called Feature Extraction Partial Convolution (FEPC), which is faster and achieves better performance than the vanilla 3×3 convolution. Additionally, we fully leverage the information redundancy between feature map channels by dividing it into two parts along the channel dimension and designing a Self-Cross Attention Block (SCAB). The reduction in channel count significantly reduces computational load and improves the framework’s speed. Through extensive experiments, our method demonstrates not only a fast inference speed but also superior dehazing performance, providing a promising solution for real-time practical deployment. Our code will be available at https://***/superwscZFSB-Dehazing-Framework.

关键词： Deep learning image quality Convolution Surveillance Redundancy signal processing algorithms Feature extraction Real-time systems Speech processing remote sensing

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Spatio-Semantic Prompt guided Adaptive Segment Anything for remote sensing Change Detection

Spatio-Semantic Prompt guided Adaptive Segment Anything for ...

引用

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

作者： Shenglong Hu Zhidong Han Gang Dong Lingyan Liang Dongchao Wen Kaihua Zhang NUIST IEIT Systems Co. Ltd.

ISBN: (数字)9798350368741

ISBN: (纸本)9798350368758

Existing leading remote sensing change detection (RSCD) often takes a semantic-agnostic learning paradigm, which uses a binary ground-truth mask as supervision for model training. Despite the demonstrated success, due to the intrinsic characteristic of extremely complicated scene changes in RS images, this paradigm is prone to be misled by irrelevant semantic category changes, leading to a noisy CD mask prediction. To address this issue, this paper presents a Spatio-Semantic Prompt (SSP) guided adaptive Segment Anything Model (SAM) for RSCD, dubbed as SSP-SAM. The SSP-SAM introduces sparse textual and dense mask prompts into SAM to encode the task-specific semantic knowledge for RSCD. Specifically, we first encode the powerful textual semantic knowledge using Contrastive Language-image Pre-training (CLIP) to determine the desired change semantic category. Then, we design a spatial dense prompt module that yields an attention map as prompt features to further refine the desired changed regions. Subsequently, we fine-tune the SAM through an adaptor to integrate the spatial-semantic prompt cues, yielding a coarse CD mask prediction. Finally, guided by the coarse CD mask, a multi-scale mask attention mechanism is adopted to learn the refined semantic representations of the changed targets, predicting the accurate CD mask. Extensive experiments on a variety of benchmark datasets demonstrate that the proposed SSP-SAM achieves state-of-the-art performance.

关键词： Training Adaptation models Accuracy Semantics Benchmark testing signal processing Sensors Noise measurement Speech processing remote sensing

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image and signal processing for remote sensing xxix

Image and Signal Processing for Remote Sensing XXIX

引用

image and signal processing for remote sensing xxix 2023

ISBN: (纸本)9781510666955

The proceedings contain 35 papers. The topics discussed include: a theoretical framework for unsupervised land cover change detection in dense satellite image time series;a class-driven hierarchical ResNet for classification of multispectral remote sensing images;large-scale LOD1 building extraction from a textured 3D Mesh of a scene;enhancing land cover maps with optical time series and ambiguous loss function;deep learning methodologies for chemical dispersion map reconstruction;monitoring of renewable energy sources with remote sensing, open data and field data in Bulgaria;detection of over-ground petroleum and gas pipelines from optical remote sensing images;and a remote sensing satellite image compression method based on conditional generative adversarial network.

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Learning Deep Frequency Degradation Prior for remote sensing Spatio-temporal Fusion

Learning Deep Frequency Degradation Prior for Remote Sensing...

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

作者： Yiting Bian Shenglong Hu Huihui Song Kaihua Zhang Nanjing University of Information Science and Technology Nanjing China

ISBN: (数字)9798350368741

ISBN: (纸本)9798350368758

Existing deep learning-based remote sensing spatiotemporal fusion (STF) relies on a data-driven paradigm without considering the degradation prior modeling from the coarseto fine-resolution images. This makes the learned model easy to overfit to the training dataset, resulting in poor domain generalization across different datasets. To this end, this paper presents a deep frequency degradation prior to STF, dubbed as DeepFDP. The DeepFDP is based on a statistical observation that the frequency feature distributions of the tokens from the coarse- and fine-resolution images in different datasets have a low intra-resolution variance and a high inter-resolution variance with compact well-separated clusters. Therefore, the DeepFDP first designs a frequency proximity module to learn the mapping function between the token frequency representations of the coarse- and fine-resolution images, which is to narrow their feature distribution difference. Since the statistical properties of the token frequency representations are independent of the land-cover classes, the DeepFDP can be learned on a training set of limited image pairs without extra-supervision signals, which has a favorable zero-shot generalization capability across different datasets. Then, to faithfully recover the land-surface details, a high-frequency feature modulation module is designed that uses the fine-resolution image as guidance to progressively learn the multi-scale residual features in a coarse-to-fine fashion, yielding the fused features with rich high-frequency details. Finally, the progressively-fused features at each stage are fed into a hybrid fusion module, yielding the fine-resolution image prediction. Extensive evaluations on LGC and CIA datasets demonstrate favorable performance of the DeepFDP over state-of-the-art methods. Especially, the DeepFDP also shows a good zero-shot generalization performance when training on LGC and testing on CIA.

关键词： Degradation Training Deep learning Frequency modulation Acoustics Spatiotemporal phenomena Sensors Speech processing remote sensing Testing

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Group-wise Semantic-enhanced Interaction Network for remote sensing Spatio-Temporal Fusion

Group-wise Semantic-enhanced Interaction Network for Remote ...

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

作者： Baoluo Zhu Shenglong Hu Huihui Song Kaihua Zhang Nanjing University of Information Science and Technology Nanjing China

ISBN: (数字)9798350368741

ISBN: (纸本)9798350368758

remote sensing spatio-temporal fusion (STF) aims at fusing temporally-dense coarse-resolution images and temporally-sparse fine-resolution images to reconstruct high spatio-temporal resolution images. Multi-band remote sensing images are often accepted as inputs for STF that have complementary characteristics for high-fidelity land surface reconstruction, yet the existing STF framework often treats different bands uniformly without considering the statistical correlation between different bands, resulting in unsatisfying results. To address this problem, this paper presents a group-wise semantic-enhanced interactive network for STF, dubbed as GSINet. Based on statistical observations, the feature correlation between the visible-light group and the invisible-light group is weak, while the intra-group correlation is strong. Therefore, the GSINet first separates the inputs into visible-light and invisible-light groups, which are fed into different branches with independent encoders for feature extraction and fusion. Afterwards, to address the issue of land cover changes between the prediction coarse- and reference fine-resolution images, a Semantic-Enhancement Fusion Module (SEFM) is designed to interact with the features from the same group with enhanced semantic information captured in an unsupervised learning manner. Then, the semantic-enhanced fused features from different bands are fed into an Interleaved Cross-attention Module (ICM) for further fusion. Finally, the output fusion features fully encode the intra- and inter-group information, which are fed into the decoder, reconstructing the spatio-temporal high-resolution images. Extensive experiments on CIA and LGC benchmark datasets demonstrate that the GSINet outperforms a variety of state-of-the-art methods in terms of multiple metrics.

关键词： Surface reconstruction Correlation Land surface Benchmark testing Speech enhancement signal processing remote sensing image reconstruction Unsupervised learning Surface treatment

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Dynamic Sparse Encoding and Cross-Temporal Attention for remote sensing image Change Detection

Dynamic Sparse Encoding and Cross-Temporal Attention for Rem...

引用

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

作者： Shaoxiong Lin Tao Lei Tongfei Liu Shuxin Zhang Chongdan Min Asoke K. Nandi Shaanxi Joint Laboratory of Artificial Intelligence Shaanxi University of Science and Technology Xi’an China Department of Electronic and Electrical Engineering Brunel University London London United Kindom

ISBN: (数字)9798350368741

ISBN: (纸本)9798350368758

Due to the inherent inductive bias of operations, convolutional neural networks (CNN) cannot model global information of remote sensing (RS) images. In contrast, Transformer-based methods can establish long-range dependencies of images through self-attention (SA) mechanism, but it faces the challenges of computational complexity and memory requirements, but also ignores the exploration on the feature redundancy removal of RS images. To address these two issues, we propose a network based on dynamic sparse encoding and cross-temporal collaborative attention (DSECTCA-Net) for RS image change detection (CD). First, we implement dynamic sparse encoding (DSE) by designing hierarchical sparse Transformer module (HSTM), which decreases the correlation calculation of the SA mechanism and effectively reduces the computational complexity and parameter amount of Transformer. Secondly, we propose cross-temporal collaborative attention (CTCA) to model RS images in time series and fully explore the interactivity between dual-temporal RS images, so as to better extract the global understanding of visual scenes. Extensive experiments on two large-scale public RS datasets show that the proposed method not only provides higher detection accuracy, but also achieves lower computational complexity and required storage space than most popular CD networks.

关键词： Visualization image coding Correlation Computational modeling Collaboration Feature extraction Transformers Convolutional neural networks Computational complexity remote sensing

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MDDNet: Multilevel Difference-Enhanced Denoise Network for Unsupervised Change Detection in SAR images

MDDNet: Multilevel Difference-Enhanced Denoise Network for U...

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

作者： He Zong Erlei Zhang Xinyu Li Hongming Zhang Jinchang Ren College of Information Engineering Northwest A&F University Yangling China School of Computing and Engineering Robert Gordon University Aberdeen United Kingdom

ISBN: (数字)9798350368741

ISBN: (纸本)9798350368758

Change detection in synthetic aperture radar (SAR) images is a hot yet highly challenging task in remote sensing. Existing unsupervised SAR change detection methods often struggle with inherent speckle noise and insufficiently utilize pseudo-labels, particularly neglecting uncertain areas. In this paper, we propose a multilevel difference-enhanced denoise dual-branch network (MDDNet), comprising representation learning and change detection branches. First, fuzzy c-means clustering is employed to generate pseudo-labels, categorizing the image areas as changed, nochanged, and uncertain. Second, we design a denoise representation loss function in the representation learning branch to maximize the use of pseudo-labels, while mitigating speckle noise. Furthermore, a multilevel difference computation module is proposed to focus on changes in ground objects and capture more comprehensive change information. Experimental results on three public SAR datasets show that the proposed method outperforms six state-of-the-art methods, achieving the best performance with an average overall accuracy of 98.86% and an average Kappa coefficient of 89.36%.

关键词： Representation learning Noise Speckle Radar imaging Feature extraction Radar polarimetry Radar signal processing Speech processing Synthetic aperture radar remote sensing

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Spatial-Frequency Information Interaction Diffusion for SAR Colorization

Spatial-Frequency Information Interaction Diffusion for SAR ...

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

作者： Xupei Zhang Hanlin Qin Jingjing Li Jinni Geng Zihan Gao Yue Yu School of Optoelectronic Engineering Xidian University Xi’an China Xi’an Key Laboratory of Infrared Technology and System Xi’an China

ISBN: (数字)9798350368741

ISBN: (纸本)9798350368758

The inherent speckle noise and grayscale characteristics of synthetic aperture radar (SAR) images pose challenges to information perception and interpretation. To address this issue, we propose a novel conditional diffusion model with spatial-frequency information interaction for SAR colorization, namely SFI4SC. Specifically, we introduce a learnable wavelet transform module to obtain the global frequency information of the input SAR image and then take it as a guiding condition to assist the diffusion model for SAR colorization. Additionally, we improved the network structure for the diffusion model by designing and introducing a spatial and frequency information interaction module to achieve multidimensional information interaction for the network inputs, further enhancing perceptual details and geometric structures of the SAR colorization results. Experimental results in the real-world remote sensing dataset show that our approach can successfully enhance visual clarity and informational content while preserving the unique characteristics of the SAR images. The code will be made publicly available.

关键词： Wavelet transforms Measurement Visualization Noise Speckle Diffusion models Radar polarimetry Speech processing Synthetic aperture radar remote sensing

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GEMD-UNet: Graph Structure Enhanced Multi-dimensional Learning Unet for Cloud Detection

GEMD-UNet: Graph Structure Enhanced Multi-dimensional Learni...

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

作者： Jianing Chen Chuhao Chen Junze Yang Wei Li Rahul Yadav Wenqi Zheng College of Computer Science and Technology Harbin Engineering University China

ISBN: (数字)9798350368741

ISBN: (纸本)9798350368758

Cloud detection (CD) in remote sensing images is commonly used in satellite imaging and laser communication. UNet-based methods with multi-level feature caching and interaction learning, are popular for superior CD performance. However, most current CD methods focus on spatial feature enhancement through global feature learning, which often overlooks the detailed perception of color, shape, and boundaries due to insufficient channel interactions. Additionally, these methods tend to increase computational requirements. To ensure flexible feature attention, enhancing both structural and detailed understanding, while maintaining low computational complexity. In this paper, we propose Graph Structure Knowledge Enhanced Multi-Dimensional Learning U-shaped Network (GEMD-UNet) for CD. Specifically, we utilize tensor dimension transformations to cleverly employ two fundamental learnable parameters alternately for spatial/channel relationship learning. Furthermore, to enhance feature knowledge capacity and focus more on local detail, we employ local window partitioning to divide hidden features into multiple independent learning groups and inject two different physically meaningful graph structures knowledge in a non-Euclidean space into features. Experimental results demonstrate that GEMD-UNet outperforms previous SOTA methods on both HRC-WHU and L8SPARCS datasets. Code can be available at: https://***/mxx123321/cloud_model.

关键词： Representation learning Tensors Satellites Shape image color analysis Neural networks signal processing Feature extraction Speech processing remote sensing

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Optical Authenticity in Pushbroom System for Spectral Information Protection

Optical Authenticity in Pushbroom System for Spectral Inform...

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

作者： Pablo Gomez Roman Jacome Emmanuel Martinez Hans Garcia Henry Arguello Department of Electrical Electronics and Telecommunications Engineering Universidad Industrial de Santander Bucaramanga Colombia Department of Systems and Informatics Engineering Universidad Industrial de Santander Bucaramanga Colombia

ISBN: (数字)9798350368741

ISBN: (纸本)9798350368758

In remote sensing and environmental monitoring, the authenticity of the collected hyperspectral image (HSI) is critical since unauthorized changes could cause inaccurate evaluation. Traditional approaches ensure HSI integrity after the acquisition, leaving the data vulnerable to unauthorized access or modification before applying a protective strategy. In response, this work presents a pushbroom HSI system that integrates an authentication key on the acquisition process, ensuring the key imperceptibility without affecting performance in spectral classification tasks. In addition, this work develops an algorithm designed to simulate attacks through class modifications in the HSI. Additionally, a detection algorithm is proposed to identify modifications by comparing the signed HSI with the altered version, effectively recognizing the embedded key. The proposed method was tested using simulated data and acquired data in the optical laboratory. Results demonstrate that the system maintains classification accuracy, further enhancing imperceptibility. Additionally, modifications are detectable by the key with 99% accuracy.

关键词： Integrated optics Accuracy Authentication signal processing algorithms Optical imaging Classification algorithms Optical sensors Optical signal processing Speech processing Hyperspectral imaging

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