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

共 3583 条记录，以下是31-40 订阅

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Frequency-Domain Guided Multiple Parallel Kernels Network for Low-Light remote sensing image Enhancement

Frequency-Domain Guided Multiple Parallel Kernels Network fo...

引用

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

作者： Jingxuan Zhou Hao Li Jinlong Wang Xiongxin Tang Fanjiang Xu Institute of Software Chinese Academy of Sciences University of Chinese Academy of Sciences Beijing China

ISBN: (数字)9798350368741

ISBN: (纸本)9798350368758

Due to dark environments, optical aberrations, etc, the remote sensing images are often submerged under low contrast degradation, which greatly hinders their practical applications for agricultural management and other related tasks. The surface features of remote sensing images are often continuously distributed in space, thus, the sizes of the network’s receptive fields and its ability to learn long-range dependencies are crucial for restoring low-light remote sensing images. Existing methods based on CNN provide limited receptive fields, while Transformer-based methods are constrained by their quadratic computational complexity. To cope with these issues, we propose a novel low-light remote sensing image enhancement network that combines multi-scale receptive fields with frequency-domain attention. Specifically, this network employs multiple parallel kernels of varying sizes to learn multi-scale local features in the spatial domain and complements frequency-domain information to learn global long-range correlations, which achieves local-global feature extraction and further facilitates subsequent degraded images enhancement. We have conducted extensive experiments to demonstrate that our network outperforms existing methods quantitatively and achieves exceptional visual performance, which fully highlights the effectiveness and superiority of our method in enhancing low-light remote sensing images.

关键词： Visualization Frequency-domain analysis Transforms Speech enhancement Feature extraction Transformers image restoration Kernel remote sensing image enhancement

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M2F2Net: Multi-stage Mixed Feature Fusion Network For remote sensing Change Detection

M2F2Net: Multi-stage Mixed Feature Fusion Network For Remote...

引用

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

作者： Binhao Gu Lei Song Youyong Kong Binjie Gu Southeast University Nanjing China Jiangnan University Wuxi China

ISBN: (数字)9798350368741

ISBN: (纸本)9798350368758

remote sensing image change detection (CD) seeks to analyze and discern changes in surface objects through the use of multi-temporal remote sensing imagery. However, as image resolution advances, existing methods often fall short in capturing comprehensive visual feature representations, and their networks are prone to spatial degradation. This results in incomplete boundary detection and difficulties in identifying subtle changes. To overcome these challenges, this paper introduces a Siamese U-Net architecture incorporating Multistage Mixed Feature Fusion (M 2 F 2 Net). The proposed model leverages a convolutional neural network (CNN) as the main encoder for local feature extraction, while employing a Transformer-based auxiliary encoder to capture global features. Furthermore, we introduce a Feature Fusion Module (FFM) to facilitate the efficient integration of local and global information. Additionally, we propose a novel convolutional unit and a Spatial Attention Module (SAM) designed to enhance the extraction of image features. Experimental results confirm that the proposed approach delivers substantial improvements across multiple evaluation criteria, while offering a superior accuracy compared to existing state-of-the-art change detection methods.

关键词： Degradation Visualization image resolution Convolution Semantics Feature extraction Transformers Convolutional neural networks Speech processing remote sensing

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TriFormer: Triple Branch Heterogeneous Transformers for UHD image Restoration

TriFormer: Triple Branch Heterogeneous Transformers for UHD ...

引用

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

作者： Xinchen Ma Yue Lu School of Communication and Electronic Engineering East China Normal University Shanghai China

ISBN: (数字)9798350368741

ISBN: (纸本)9798350368758

Ultra-high-definition (UHD) image restoration is becoming a critical research area due to the increasing demand for high-quality visual content in various applications, including autonomous driving, remote sensing, digital entertainment, etc. However, UHD image restoration tasks present notable challenges, including the significant computational burden posed by the large image size scale, the difficulty in preserving fine details and textures, and the increased susceptibility to noise and artifacts during the restoration process. In this paper, we propose a novel triple-branched optimized heterogeneous transformer for UHD image restoration, named TriFormer. Specifically, our method embeds a high-resolution CNN branch for high-frequency features, a low-resolution pixel-wise attention branch for low-frequency features, and a channel-wise attention branch for feature fusion. We perform experiments across two UHD image restoration tasks: enhancing low-light images and deblurring. Results demonstrate that our model outperforms state-of-the-art approaches both quantitatively and qualitatively. The code will be made available at https://***/Chloe-mxxxxc/TRIFORMER.

关键词： Visualization Technological innovation Noise Graphics processing units Entertainment industry Computer architecture Transformers image restoration Speech processing remote sensing

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Enhancing remote sensing Vision-Language Models for Zero-Shot Scene Classification

Enhancing Remote Sensing Vision-Language Models for Zero-Sho...

引用

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

作者： Karim El Khoury Maxime Zanella Benoît Gérin Tiffanie Godelaine Benoît Macq Saïd Mahmoudi Christophe De Vleeschouwer Ismail Ben Ayed UCLouvain Belgium UMons Belgium ÉTS Montreal Canada

ISBN: (数字)9798350368741

ISBN: (纸本)9798350368758

Vision-Language Models for remote sensing have shown promising uses thanks to their extensive pretraining. However, their conventional usage in zero-shot scene classification methods still involves dividing large images into patches and making independent predictions, i.e., inductive inference, thereby limiting their effectiveness by ignoring valuable contextual information. Our approach tackles this issue by utilizing initial predictions based on text prompting and patch affinity relationships from the image encoder to enhance zero-shot capabilities through transductive inference, all without the need for supervision and at a minor computational cost. Experiments on 10 remote sensing datasets with state-of-the-art Vision-Language Models demonstrate significant accuracy improvements over inductive zero-shot classification. Our source code is publicly available on Github: https://***/elkhouryk/RS-TransCLIP

关键词： Scene classification Accuracy Sensitivity Computational modeling Source coding signal processing algorithms Computational efficiency Speech processing remote sensing Software development management

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A GNSS-IR Aided Multispectral Satellite Data Fusion for Meter-Level Wide-Area Volumetric Soil Moisture Estimation

A GNSS-IR Aided Multispectral Satellite Data Fusion for Mete...

引用

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

作者： Nicolás Padrón Sergiy Vorobyov School of Electrical Engineering Aalto University Espoo Finland Department of Information and Communications Engineering Aalto University Espoo Finland

ISBN: (数字)9798350368741

ISBN: (纸本)9798350368758

Earth Observation (EO) data is captured with different instruments and available in multiple formats. The complementation of two passive remote sensing approaches in local small areas is performed here to produce a single, large-area coverage, volumetric soil moisture (VSM) solution. The sensing approaches under consideration are GNSS Interferometric Reflectometry (GNSS-IR), which is a local land-based microwave remote sensing approach that exploits ground-reflected navigation signals; and also multispectral satellite imagery, which is a space-based optical remote sensing approach that allows to analyze the spectral response of surface materials. The focus is on the processing of GNSS-IR outputs aiding a multispectral model using Landsat-8 data. The aim is to provide an accurate, cost-effective solution with wide-area coverage. Landsat-8 spectral indexes highly correlated with soil moisture are fused with GNSS-IR VSM on multiple terrain types. The proposed solution is demonstrated and verified against data from the Soil Moisture Active Passive (SMAP) satellite mission over large-areas.

关键词： Earth Optical interferometry Global navigation satellite system Artificial satellites Satellites image resolution Soil moisture Estimation remote sensing signal resolution

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TMANet: Triple Multi-Scale Attention based Network with Boundary Association Loss for Superpixel Segmentation

TMANet: Triple Multi-Scale Attention based Network with Boun...

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

作者： Ziyi Zhang Shijie Lian Hua Li Hainan University Haikou China Huazhong University of Science and Technology Wuhan China

ISBN: (数字)9798350368741

ISBN: (纸本)9798350368758

Superpixel segmentation with deep learning has been proposed in recent years and is widely employed to reduce the input image primitives for subsequent computer vision tasks. In this paper, we propose a Triple Multi-Scale Attention based Network (TMANet) for superpixel segmentation. First, aiming to extract more detailed context information, we design a Triple Multi-Scale Attention (TMA) to adapt the varying object scale and reduce inevitable redundant information in the encoder. Moreover, we observe that the dark parts with low probability in the association map generated by the TMANet are closer to the superpixel boundary. Therefore, we devise Boundary Association (BA) loss based on the association map to obtain fine boundaries and contours. Extensive experiments on public datasets show that TMANet outperforms the state-of-the-art methods to a certain extent. The application in saliency object detection of remote sensing also demonstrates the superiority of the proposed method.

关键词： Deep learning image segmentation Computer vision Object detection signal processing Acoustics Data mining Speech processing remote sensing

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

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

ISBN: (数字)9798350368741

ISBN: (纸本)9798350368758

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.

关键词： Training image segmentation Roads Semantics Optical fiber networks Optical imaging Optical sensors Speech processing Optical signal processing remote sensing

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Joint Semantic Segmentation of Optical and SAR image in Hazy Environments via Cross-modal Information Rectification and Cross-attention Fusion

Joint Semantic Segmentation of Optical and SAR Image in Hazy...

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

作者： Xinyue Fan Libao Zhang School of Artificial Intelligence Beijing Normal University Beijing China

ISBN: (数字)9798350368741

ISBN: (纸本)9798350368758

Semantic segmentation is crucial in remote sensing image processing. In recent years, semantic segmentation using optical and SAR images for multi-modal fusion is gaining attention for its good results. The current research primarily encompasses two problems: 1) Existing fusion methods are designed for clear images and struggle in harsh weather. 2) Current fusion methods insufficiently capture multi-modal information correlation. This paper presents a joint semantic segmentation of optical and SAR in hazy environments network that incorporates channel fusion for feature enhancement and cross-attention for feature fusion, enabling efficient segmentation of hazy optical images. First, we design a channel-guided crossmodal information correction module. This module regards fog as noise and uses high-confidence features of one modality to calibrate the other modality, thereby reducing the impact of fog. Secondly, to address the issue of insufficient fusion of multimodal information, we introduce a cross-attention fusion module to effectively combine the complementary information from two streams leveraging the large receptive field enabled by self-attention. The implementation results show that this method has better results for the semantic segmentation of blurry remote sensing images.

关键词： Integrated optics Semantic segmentation Optical fiber networks Optical imaging Adaptive optics Radar polarimetry Optical sensors Optical noise Optical signal processing remote sensing

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UV-Mamba: A DCN-Enhanced State Space Model for Urban Village Boundary Identification in High-Resolution remote sensing images

UV-Mamba: A DCN-Enhanced State Space Model for Urban Village...

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

作者： Lulin Li Ben Chen Xuechao Zou Junliang Xing Pin Tao School of Computer Technology and Applications Qinghai University Xining China School of Computer Science and Technology Beijing Jiaotong University Beijing China Department of Computer Science and Technology Tsinghua University Beijing China

ISBN: (数字)9798350368741

ISBN: (纸本)9798350368758

Due to the diverse geographical environments, intricate landscapes, and high-density settlements, the automatic identification of urban village boundaries using remote sensing images remains a highly challenging task. This paper proposes a novel and efficient neural network model called UV-Mamba for accurate boundary detection in high-resolution remote sensing images. UV-Mamba mitigates the memory loss problem in lengthy sequence modeling, which arises in state space models (SSM) with increasing image size, by incorporating deformable convolutions (DCN). Its architecture utilizes an encoder-decoder framework and includes an encoder with four deformable state space augmentation (DSSA) blocks for efficient multi-level semantic extraction and a decoder to integrate the extracted semantic information. We conducted experiments on two large datasets showing that UV-Mamba achieves state-of-the-art performance. Specifically, our model achieves 73.3% and 78.1% IoU on the Beijing and Xi’an datasets, respectively, representing improvements of 1.2% and 3.4% IoU over the previous best model while also being 6× faster in inference speed and 40× smaller in parameter count. Source code and pre-trained models are available at https://***/Devin-Egber/UV-Mamba.

关键词： Deformable models Location awareness image segmentation Source coding Semantics Neural networks Data mining Sustainable development Speech processing remote sensing

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Experimental Results of Local Backprojection for Monostatic THz SAR Imaging

Experimental Results of Local Backprojection for Monostatic ...

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International Workshop on Mobile Terahertz Systems (IWMTS)

作者： Yevhen Ivanenko Aman Batra Viet Thuy Vu Mats I. Pettersson Thomas Kaiser Blekinge Institute of Technology Karlskrona Sweden University of Duisburg-Essen Duisburg Germany

ISBN: (数字)9798350365832

ISBN: (纸本)9798350365849

The use of THz frequencies has enabled the opportunity to perform synthetic-aperture radar (SAR) imaging at the sub-mm level. It is of great interest for applications where high-resolution remote sensing in short range is required. However, with the increase of the operating frequencies from microwave to THz, the SAR image formation algorithms work with a larger amount of data and become more sensitive to phase errors that can be caused by insufficient signal sampling rate or physical factors that cause platform deviations. This motivates the use of fast image formation capable to handle phase errors. In this paper, we present the experimental results on the performance of the local backprojection (LBP) algorithm for processing THz SAR signals. The LBP algorithm has been tested with the real data in the frequency range 0.325–0.5 THz. The results demonstrate the efficiency of the LBP algorithm for the SAR scene reconstruction and highlight the necessity of the use of two times higher signal upsampling to achieve reconstruction accuracy similar to the global backprojection algorithm.

关键词： Terahertz wave imaging Radar remote sensing Accuracy Heuristic algorithms signal sampling Approximation algorithms Radar polarimetry Reliability image reconstruction remote sensing

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