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

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Bridging Task Boundaries: remote sensing image-Text Retrieval via Dictionary-Driven Adaptation

Bridging Task Boundaries: Remote Sensing Image-Text Retrieva...

引用

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

作者： Junwei Xu Tao Huang Zhenyu Wang Weisheng Dong Xin Li Hangzhou Institute of Technology Xidian University Hangzhou China the School of Artificial Intelligence Xidian University Xi’an China Department of Computer Science State University of New York at Albany New York USA

ISBN: (数字)9798350368741

ISBN: (纸本)9798350368758

Given image (or text), remote sensing image-text retrieval (RSITR) aims to retrieve corresponding text (or image) within diverse remote sensing data. However, due to the complex scenes and compact distribution of targets in remote sensing data, existing methods, particularly those leveraging large models like CLIP, often generate features with high intra-modal similarity and insufficient distinctive characteristics, thus resulting in suboptimal retrieval performance. To address these issues, we pro- pose a novel dictionary-based RSITR method that jointly models image and text feature estimation. Specifically, by incorporating a general dictionary and the corresponding sparse coefficients, our method more effectively captures the correlations between the learned features. Furthermore, we introduce adaptive weighted metric learning on a sample-by-sample basis to encourage the model to focus on more challenging negative samples, promoting fine-grained feature alignment. The extensive experiments on the RSICD and RSITMD datasets demonstrate the effectiveness of our method, demonstrating significant improvements in retrieval performance.

关键词： Measurement Adaptation models Dictionaries Semantics Estimation signal processing Feature extraction Data models Speech processing remote sensing

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The application of Gaussian-Rayleigh mixture model in remote sensing image segmentation

The application of Gaussian-Rayleigh mixture model in remote...

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6th International conference on Intelligent Computing and signal processing (ICSP)

作者： Xiaoxue Sun Yue Yang Yanhao Li Guoren Gao Mechanical Engineering Department Jilin Agriculture Science and Technology University Jilin Jilin China State Grid Liaoning Extra High Voltage Company Liaoning Anshan China

ISBN: (数字)9798350376548

ISBN: (纸本)9798350376555

This paper employs the Entropy-Max method to determine the optimal number of classes in an image, utilizes the Markov Random Field (MRF) method to complete the image segmentation, and addresses the computation of maximum a posteriori probability during the segmentation process using the Iterative Conditional Model (ICM). Three remote sensing images are used in the experiments, comparing the segmentation and fitting results using Gaussian Mixture Mode (GMM) and Gaussian-Rayleigh Mixture Model (GRMM).

关键词： Maximum a posteriori estimation image segmentation Technological innovation Computational modeling Fitting signal processing algorithms Mixture models signal processing Entropy remote sensing

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A Deep Neural Network Supported by Ground Verification Data for Active Fire Detection in MODIS

A Deep Neural Network Supported by Ground Verification Data ...

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

作者： Chen, Jinpeng Xie, Feifei Zheng, Chunkai College of Geodesy and Geomatics Shandong University of Science and Technology Qingdao China

ISBN: (纸本)9781510666351

At present, the use of MODIS to detect fires mainly uses a series of thresholds to identify fire. However, in areas with high heterogeneity, the division of thresholds is more difficult, resulting in many false detections of fire, and cold fire are easily missed. The emergence of deep learning technology has improved the classification accuracy of images, but the surface information of hotspots cannot be determined only by visual interpretation of MODIS images, and the acquisition of high-quality fire samples further limits the detection performance of deep learning methods. In order to solve the above problems, this paper proposes a deep neural network fire detection method based on ground truth data. First, the data structure of the US ground wildfire dataset is studied, a spatiotemporal matching strategy between ground data and remote sensing images is designed, and a priori sample is constructed. library. Second, robust classification features are selected combining the fire physical properties and the spectral distribution of the dataset. Finally, a DNN (Deep Neural Network) for MODIS fire detection is constructed according to the image characteristics. Application experiments were carried out in three typical scenarios and compared with MODIS fire products. The results show that the improved method improves the temporal and spatial adaptability of fire detection, and the extraction of small fire in farming areas is better than MODIS fire products;especially in the suburbs Significant reduction of spurious fire in region extraction, false positives decreased by 19.8%. This method provides a new reference for coarse resolution satellite data to detect fires, and effectively improves the efficiency of fire detection. © 2023 SPIE.

关键词： remote sensing

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Uniform Distribution of Multiple Features for remote sensing Optical image Matching 10

Uniform Distribution of Multiple Features for Remote Sensing...

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10th International conference on Emerging Trends in Engineering and Technology - signal and Information processing, ICETET-SIP-22 2022

作者： Paul, Sourabh Udaysankar, D. Naidu, Yaswanth Reddy, Yogeswara Vellore Institute of Technology Chennai India Madanapalle Electronics and Communication Engineering Madanapalle India

ISBN: (纸本)9781665467414

Uniform distribution of selected features is one of the important factors in optical image matching. In this paper, an effective scheme is proposed to obtain a uniform distribution of the extracted features. At first, two different types of operators namely scale invariant feature transform (SIFT) and multi-scale Harris detector are employed to extract initial features from the optical images. Then, the well distributed and robust features are selected from these initial features and these are considered as final features. After that, the input optical images are matched through the feature matching process by using the selected features. The proposed scheme can obtain better distribution of matching pairs than the state-of-the-art methods. Experiments on three sets of remote sensing optical image pairs verify the performance of the developed algorithm. © 2022 IEEE.

关键词： Integrated optics image matching Transforms Information processing Optical detectors Feature extraction Optical imaging

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Research on Performance Evaluation of SAR and Optical image Fusion Methods Based on GF-3 Satellite images 11th

Research on Performance Evaluation of SAR and Optical Image ...

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

作者： Lu, Zheng Huang, Yufei Wang, Zhibin Hou, Wentao Zhou, Yashi Zhao, Liangbo Institute of Remote Sensing Satellite CAST Beijing100094 China

ISBN: (纸本)9789819721238

The imaging mechanisms of SAR sensors and optical ones are different. Accordingly, there are differences between features of SAR and optical images. The fusion methods for SAR satellite images and optical ones mainly include wavelet transform, HIS transform, PCA transform, and so on. Each transformation method has different performances on their fused images. In the paper, the performance evaluation method of SAR and optical image fusion methods based on real-world satellite images is studied. Firstly, SAR and optical image fusion process is introduced, including image preprocessing, image registration and image fusion. Secondly, various evaluation indicator, such as average gradient (AG), distortion, entropy, mutual information (MI), universal image quality index (UIQI) and edge mutual information (EMI) are provided. Finally, taking Yangzhong Island region in China as an experimental site, multiple image fusion methods were used to fuse a GF-3 satellite SAR image and an optical one. Evaluation criteria are provided for the applicability of image fusion methods. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2024.

关键词： image fusion

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AMIC: A Novel Hybrid image Compression for Multi-spectral Satellite imagery 3rd

AMIC: A Novel Hybrid Image Compression for Multi-spectral S...

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3rd International conference on Machine Learning, Cloud Computing and Intelligent Mining, MLCCIM 2024

作者： Nhat, Trinh Le Nhat, Tan Thai Nguyen, Luong Vuong Vu, Bui Cao Department of Artificial Intelligence FPT University Danang Viet Nam

ISBN: (纸本)9789819624676

Multi-spectral satellite imagery (MSSI) offers a crucial means of collecting environmental and geological data from dangerous, remote locations. However, the substantial volume of such imagery complicates its storage and transmission, making efficient image compression indispensable. Traditional compression methods such as Discrete Wavelet Transform (DWT), Discrete Cosine Transform (DCT), Consultative Committee for Space Data Systems (CCSDS), Bandelet, and JPEG 2000 have provided high reliability, simple implementation, and effectiveness, yet they lack sufficient adaptability. In contrast, Machine Learning (ML) offers a more adaptable, albeit complex and data-intensive, alternative. This paper proposed a hybrid approach, called AMIC, that combines the robustness of traditional methods with the flexibility of ML, using DWT to quantize image bands and ML to refine this process, thereby enhancing the compression quality with minimal computational load. The AMIC performance demonstrates high fidelity in compressed images, with Peak signal-to-Noise Ratio (PSNR) values ranging from 30dB to 42dB and Structural Similarity Index (SSIM) values above 0.78, ensuring excellent preservation of image details. The approach significantly optimizes compression and decompression times, achieving a reduction to 43.26% of the original size. This hybrid method ensures high-quality satellite image compression for improved transmission and storage. It broadly impacts real-time data processing across various fields, including environmental monitoring and remote sensing research. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2025.

关键词： Satellite imagery

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Reconstruction quality evaluation of Compressed sensing image Mapping Spectrometer

Reconstruction quality evaluation of Compressed Sensing Imag...

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conference on AOPC - Computing Imaging Technology

作者： Yang, Shuya Ding, Xiaoming Yuan, Hao Lu, Dunqiang Yan, Qiangqiang Tianjin Normal Univ Tianjin Key Lab Wireless Mobile Commun & Power Tr Tianjin 300387 Peoples R China Shaanxi Key Lab Opt Remote Sensing & Intelligent Xian 710119 Peoples R China Xian Inst Opt & Precis Mech CAS Key Lab Spectral Imaging Technol Xian 710119 Peoples R China

ISBN: (纸本)9781510672413;9781510672406

This paper uses traditional algorithms and deep learning algorithms to recover datacube obtained by CASSI and CSIMS in order to verify that CSIMS outperforms CASSI by comparing the Peak signal to Noise Ratio (PSNR), Structural Similarity (SSIM) and Relative spectral Quadratic Error (RQE) of the reconstructed datacube. The experimental results show that the datacube of CASSI and CSIMS can be both reconstructed by ADMM- TV algorithm which is the most effective among the traditional algorithms. PSNR of the reconstructed datacube of CASSI is 32.50 dB, while that of CSIMS is 35.53 dB, with an increase of 3.03 dB. By using deep learning algorithm, both systems improve substantially under the PnP-HSI network, with PSNR of CASSI growing to 38.85 dB and that of CSIMS growing to 41.97 dB, which can be seen that CSIMS is still 3.12 dB higher than CASSI.

关键词： Snapshot spectral imaging Compression sensing image mapper Deep learning

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Method and implementation of region proposals extraction for complex scenes

Method and implementation of region proposals extraction for...

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IET International Radar conference 2023, IRC 2023

作者： Pang, Kaiyu Jing, Donglin Han, Yuqi Deng, Chenwei Beijing Institute of Technology Beijing China

ISBN: (数字)9781837240982

ISBN: (纸本)9781839539954

Land remote sensing images are large in width, low in effective object density and complex in distribution. Using the existing detection strategies to finely process each pixel of the remote sensing images is difficult to meet the detection needs of high accuracy and fast speed for some tasks. It is necessary to extract regions proposals effectively. To this end, this paper designs a classification network based on embedded linear expansion features to achieve fast and accurate screening of region proposals. Specifically, we first optimally design the embedded linear expansion feature module to achieve lightweight convolution;secondly, we design the residual structure of the embedded linear expansion feature through the stacking module, to effectively extract feature;finally, we construct a classification network based on the embedded linear expansion feature through the combination of the proposed residual structure, which can classify the sliced image quickly and extract the region proposals effectively. The test results on the public dataset DOTA show that the proposed method with suitable detection has a significant improvement in accuracy and speed compared to the baseline model YOLOV4, with a 4% improvement in mAP and a reduction in computation to 16.8 times of the original. © The Institution of Engineering & Technology 2023.

关键词： image segmentation

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FANM: Fuzzy-Aware Nested Mamba for Infrared Dense Small Target Detection 2

FANM: Fuzzy-Aware Nested Mamba for Infrared Dense Small Targ...

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

作者： Sui, Yi Zhi, Xiyang Huang, Yuanxin Liang, Xiaoyang Lu, Zheng Zhang, Wei Harbin Institute of Technology Research Center for Space Optical Engineering Harbin China China Academy of Space Technology Institute of Remote Sensing Satellite Beijing China

ISBN: (纸本)9798331515669

The task of infrared dense small target detection aims to accurately locate densely distributed thermal radiation targets in complex scenes. However, in complex scenes, dense small targets often suffer from occlusion, shadows, and blurriness, leading to unclear detection bounding boxes and inaccurate localization. To address the aforementioned issues, this paper introduces an innovative approach for infrared dense small target detection, termed the Fuzzy-Aware Nested Mamba (FANM) network. Specifically, the network takes a single-frame infrared image as its input and leverages shallow, middle, and deep Mamba networks to fully exploit and integrate local and global multi-scale features. The extracted features are subsequently passed to a feature pyramid network (FPN) to balance the spatial resolution and semantic information of each feature map layer, ensuring that feature maps of different scales can be effectively utilized. Then, the output features at each level are fed into the detection head, which consists of two branches: one for regressing the positions of arbitrarily distributed bounding boxes, and the other for predicting the quality of the bounding boxes. The proposed fuzzy perceptual loss function is used to optimize these branches, ultimately outputting images with accurately detected target bounding boxes. Experiments were performed on the DMIST dataset, which includes images with varying numbers of targets, and the results validate the effectiveness of our method. © 2024 IEEE.

关键词： Infrared imaging

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Scale-Spectral-Spatial Attention Network for Hyperspectral image Classification 30

Scale-Spectral-Spatial Attention Network for Hyperspectral I...

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30th IEEE signal processing and Communications Applications conference (SIU)

作者： Derbashi, Usama Aptoula, Erchan Gebze Tech Univ Comp Engn Dept Kocaeli Turkey Gebze Tech Univ Inst Informat Technol Kocaeli Turkey

ISBN: (数字)9781665450928

ISBN: (纸本)9781665450928

Attention networks enable neural networks to focus on the most beneficial parts of their input. In the context of remote sensing image classification, studies about spatial, spectral and spatial-spectral attention networks have already been reported. In this paper, a network integrating a scale-based attention module, in addition to spatial-spectral attention is proposed. The scale-space has been produced via alpha-trees, in order for the network to focus on the most useful scales. It is tested with two real hyperspectral datasets, where it achieves a performance improvement.

关键词： Attention network machine learning pixel classification hyperspectral images tree representation

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