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

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Azimuth Ambiguity Suppression for Single Look Complex image Based on Block Sparse Representation

Azimuth Ambiguity Suppression for Single Look Complex Image ...

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IET International Radar conference 2023, IRC 2023

作者： Li, Jieshuang Tao, Mingliang Li, Tao Tang, Chuheng Liu, Yanyang Wang, Ling Northwestern Polytechnical University Xi'an710072 China The Land Satellite Remote Sensing Application Center Ministry of Natural Resources of P.R. China Beijing100048 China Shanghai Satellite Engineering Institute Shanghai201108 China

ISBN: (纸本)9781839539954

Azimuth ambiguity affects image quality seriously, which poses a great challenge to post-processing methods. However, existing ambiguity suppression method mainly deals with the azimuth ambiguity in raw data or utilizing multi-channel information. This paper proposes a blind adaptive method for single-look complex (SLC) images with less prior information. The energy of azimuth ambiguity will be sparsely spread in each sub-look image by multi-look processing on the SLC image. Through robust principal component analysis, useful information embedded in each sub-look image is effectively reconstructed and the azimuth ambiguity is removed. By applying sub-look synthesis, the problem of azimuth resolution loss caused by sub-look images is effectively avoided. Experimental results demonstrate the effectiveness of the proposed method. © The Institution of Engineering & Technology 2023.

关键词： image analysis

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image Defogging based on Combined Sparse Gradient Minimization and CNN Architecture 2

Image Defogging based on Combined Sparse Gradient Minimizati...

引用

2nd International conference on Microwave, Optical and Communication Engineering, ICMOCE 2023

作者： Jana, Anisha Sahoo, Upendra Kumar National Institute of Technology Department of Electronics and Communication Engineering Odisha Rourkela India

ISBN: (纸本)9798350321609

Some environmental factors like haze or fog de-grades the quality of the image. These factors affect some real time processes such as object detection and recognition, automated vehicles and remote sensing which needs clear visible images for making critical decisions. Therefore, restoring the true image from the foggy image becomes significant. Now with the advancement in image processing, many image defogging and dehazing algorithms has been developed to improve the quality of the image. Many standard filtering techniques such as high boost filter, homomorphic filter can be used for image defogging but it fails to restore the foggy images completely so some advanced techniques like DCP, decomposition techniques, CNN based algorithms are used. The proposed work mainly focuses on CNN based algorithm for image defogging combined with SGM module for edge preservation. image quality assessment (IQA) is done to measure the quality of the defogged image. These performance metrics mainly includes mean squared error (MSE), structural similarity index metric (SSIM),peak signal to noise ratio (PSNR). © 2023 IEEE.

关键词： image enhancement

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Impact of Spectral Domain Features for Small Object Detection in remote sensing 7th

Impact of Spectral Domain Features for Small Object Detectio...

引用

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

作者： Giridharan, Urja Ramprasad, Neeraj Roy, Sukanta Omkar, S.N. Ramaiah Institute of Technology Bengaluru India Department of Aerospace IISc Bengaluru India

ISBN: (纸本)9783031398100

remote sensing is a vital technique for detecting and monitoring the physical characteristics of an area from a distance: typically, from an aircraft or satellite. Accurate object detection in remote sensing images is crucial since security, transportation, environmental monitoring, and rescue applications in the military and civilian sectors demand comprehensive, precise analysis and utilization of these images. Despite recent advances, small object detection in satellite images is challenging due to the lack of information raised from the area covered by small objects in the image. To address the problem of small object detection, this paper discusses the importance of feature space analysis. The need for distinctive feature space is equally important along with the choice of the model architecture. Focusing on feature space analysis, deep learning is used for the extraction of the granularity of data. Further, spectral context is combined with spatial features to reinforce feature information and thus, enhance the detection. Our strategy involves comparing two units: the Fourier transform unit and the Wavelet packet transform unit to extract spectral features. These units are placed in the EfficientDet to enhance the feature space and assist small object detection by providing context. Three models were trained and tested on DIOR dataset, which is a publicly available dataset. The intuition of adding the transform units to the existing model has shown a remarkable improvement of 20% in the mAP. With limited changes in the number of parameters and without much hyper parameter tuning, the mAP shows improvement. Initially an experiment was conducted on a small custom dataset to test the hypothesis of the impact of feature space in object detection and the models containing the transform units showed an improvement of 4% in the mAP. © IFIP International Federation for Information processing 2023.

关键词： remote sensing

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Open-Set remote sensing Object Detection Using Edge Information Extraction

Open-Set Remote Sensing Object Detection Using Edge Informat...

引用

signal, Information and Data processing (ICSIDP), IEEE International conference on

作者： Xiaozhe Li Sihang Dang Yifei Sun Xiaoyue Jiang Shuliang Gui Xiaoyi Feng School of Electronics and Information Northwestern Polytechnical University Xi’an China School of Communication and Information Engineering Chongqing University of Posts and Telecommunications Chongqing China

ISBN: (数字)9798331515669

ISBN: (纸本)9798331515676

Typically, remote sensing object detection is limited to a closed-set detection environment. A problem is that when detecting the new untrained but valuable objects, they are incorrectly classified as known classes or background. In this paper, a method is proposed for open-set detection of remote sensing objects, which assigns robust pseudo-labels to unknown classes and trains the network to recognize new classes. The main idea is to combine the feature information of remote sensing objects and select regions in the image that are likely to contain unknown classes to form pseudo-labels. Through supervised learning, the network can distinguish and detect both known and unknown classes. remote sensing objects are observed from an Earth observation perspective, with rich edge information. Pseudo-labels for unknown classes are obtained using image convolution features and object edge information. Experimental results show that this method outperforms existing methods in open-set target detection for remote sensing images.

关键词： Earth Convolution image edge detection Supervised learning Object detection Information retrieval Data processing Robustness Sensors remote sensing

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Lightweight YOLOv4 Algorithm for remote sensing image Detection 14

Lightweight YOLOv4 Algorithm for Remote Sensing Image Detect...

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14th International conference on signal processing Systems, ICSPS 2022

作者： Ma, Li He, Tong Sun, Yong Hu, Bin-Bin College of Information Liaoning University Shenyang China

ISBN: (纸本)9798350336313

remote sensing images have the characteristics of complex backgrounds, high resolution, and small targets. Although the existing object detection algorithms can improve the detection accuracy, there are generally problems such as a large number of model parameters, high computational cost, and poor real-time performance. Aiming at the above problems, this paper designs a lightweight object detection algorithm GSC-YOLO based on YOLOv4 to achieve fast and accurate detection of remote sensing images. First, Ghostnet is used as the feature extraction network of GSC-YOLO to reduce the number of parameters and improve the detection speed;Secondly, the improved shuffle attention mechanism is introduced in the prediction head to make the model pay attention to important information and improve the detection accuracy;Finally, the Confidence Propagation Cluster algorithm CP-Cluster is used to post-process the prediction frame to improve the object recognition. Taking the preprocessed DOTA dataset as the experimental object, the experimental results show that the GSC-YOLO algorithm has a detection accuracy of 93.44%, a detection speed of 58 frames per second, and a model size of 43.65MB. Compared with the remote sensing image object detection algorithm based on YOLOv4, the detection accuracy is increased by 3.93%, the detection speed is increased by 1.87 times, and the model size is reduced by 5.62 times, which is more suitable for deployment on devices with limited resources. © 2022 IEEE.

关键词： Object recognition

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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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image Fusion Based on Discrete Cosine Transform with High Compression 7

Image Fusion Based on Discrete Cosine Transform with High Co...

引用

7th International conference on signal and image processing, ICSIP 2022

作者： Alhatami, Emadalden Bhatti, Uzair Aslam Huang, Mengxing Feng, Si Ling Hainan University School of Information and Communication Engineering Haikou China

ISBN: (数字)9781665495639

ISBN: (纸本)9781665495639

The fusion of images is defined as an alignment of important information from diverse sensors using various mathematical models to generate a single compound image. The fusion of images is used for integrating the complementary multi-temporal, multi-view, and multi-sensor information into a single image with improved image quality and by keeping the integrity of essential features. It is considered a vital pre-processing phase for several applications such as robot vision, aerial, satellite imaging, medical imaging, and robot or vehicle guidance. This paper proposed an enhancement image compression algorithm on a discrete cosine transform (DCT). Then it uses the DCT transformation in the remote sensing image fusion domain and a remote sensing image fusion method with an improved proposed algorithm. The method in this paper has an acceptable compromise between enhancing the spatial resolution of the fused image and maintaining the spectral information. Besides, it is shown that there is no need for codec operations when performing image fusion in the compressed domain such as JPEG. This reduces the time to obtain the fused image and effectively suppresses the problem of dealing with blocky image processing;blockage often occurs. Furthermore, the improved algorithm has better spectral retention capability under the premise of more explicit fusion images and is more suitable for applications requiring higher spectral fidelity. © 2022 IEEE.

关键词： image fusion

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Models for Exploring the Benefits of using Discrete Wavelet Transformation in HSI 11

Models for Exploring the Benefits of using Discrete Wavelet ...

引用

11th IEEE International conference on signal processing and Integrated Networks, SPIN 2024

作者： Vatsavayi, Valli Kumari Bobbili, Charishma Jyothi, Vadisila Andhra University College of Engineering Dept. of CS and SE Visakhapatnam India MVGR College of Engineering Dept. of CSE-AIML Vizianagaram India

ISBN: (纸本)9798350308433

The application of Hyper Spectral images is increasing day-by-day, with the development of remote sensing technology. With the rapid development of Deep Learning technology, many research work focus on Classification of Hyper Spectral images based on both spectral and spatial features extraction. It is the task of correctly predicting the class values of different pixel values present in remotely sensed HSI data. In order to achieve the accurate classification of ground features, Feature Extraction is one of the crucial step which increases the accuracy of learned models by extracting relevant features from the input data. As the HSI image consists of hundreds of continuous spectral bands, we need an effective way to extract the spectral features of the HSI images (other than CNN techniques). In this paper, we exploit two different types of Discrete wavelet transformation techniques like Haar, Daubechies (Db4) for spectral feature extraction. This in turn reduces the dimensionality of data. These spectral features are then linked to four layers of 2D CNN to extract the spatial features. The extracted features from the wavelet fusion CNN are provided further for classification. Initially factor Analysis method was used to reduce the dimensions of the HSI input data. Our experimental results conclude the better accuracy method among these, through a comparative analysis with other state-of-The-Art methods. We use Overall Accuracy, Kappa Coefficient and Average Accuracy as a Performance measures on 3 benchmark datasets of Indian Pines, Salina Scene, University of Pavia. © 2024 IEEE.

关键词： image classification

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COMPARATIVE ANALYSIS OF SAR SHIP DETECTION METHODS BASED ON DEEP LEARNING

COMPARATIVE ANALYSIS OF SAR SHIP DETECTION METHODS BASED ON ...

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IET International Radar conference 2023, IRC 2023

作者： Wei, Tong Wang, Taoyang Dong, Tiancheng Jing, Maoqiang Chen, Shili Xu, Congan Liu, Yu Wu, Junfeng Gao, Long School of Remote Sensing and Information Engineering Wuhan University Wuhan China State Key Laboratory of Information Engineering in Surveying Mapping and Remote Sensing Wuhan University Wuhan China Information Fusion Institute Naval Aviation University Yantai China

ISBN: (纸本)9781839539954

Synthetic Aperture Radar (SAR) is notable for its continuous observation capability, irrespective of the time of day. Advancements in SAR satellite technology have led to a surge in SAR image data, aiding in image interpretation. SAR-based ship detection is pivotal for traffic monitoring and fishery management, becoming a crucial aspect of SAR image interpretation. Despite some success with traditional image processing methods, several challenges remain unresolved. This article delves into six diverse mainstream deep learning models, evaluating their detection efficacy on SAR images, and conducting a thorough comparison and analysis to provide readers with a solid reference for model selection across varying scenarios. The conclusions drawn are as follows: the models 'Explore the limits of Visual representation at scAle using only publicly accessible data (EVA)' and the 'You Look Only Once (YOLO)' series networks demonstrate superior performance in SAR ship detection, with YOLOv8 standing out remarkably by achieving a precision rate exceeding 90%, while keeping the false alarm and miss rates within a 10% threshold. Conversely, Faster Region-Convolutional Neural Networks (Faster R-CNN) and Single Shot MultiBox Detector (SSD) exhibit subpar performance, with precision rates below 75% and a notable miss rate of 78.94% in SSD's case. © The Institution of Engineering & Technology 2023.

关键词： Convolutional neural networks

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Patch-based Cross-domain Adaptation for Few-shot remote sensing Scene Recognition

Patch-based Cross-domain Adaptation for Few-shot Remote Sens...

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signal, Information and Data processing (ICSIDP), IEEE International conference on

作者： Chengzhi He Yu Liu Zhizhuo Jiang Yaowen Li Chenggang Yan Bolun Zheng Shenzhen International Graduate School Tsinghua University Shenzhen China Department of Electronic Engineering Tsinghua University Beijing China Intelligent Information Processing Laboratory Hangzhou Dianzi University Hangzhou China

ISBN: (数字)9798331515669

ISBN: (纸本)9798331515676

Recognizing new classes from a few labeled remote sensing images presents a significant challenge. Existing methodologies assume that training and testing datasets come from identical domains. Collecting training datasets of remote sensing images from each domain is practically unfeasible. To tackle this challenge, we propose a strategy that leverages image patch feature embedding and cross-adaptation mechanism for cross-domain few-shot classification. Initially, the supplied image is segmented into patches of the same size. Subsequently, feature embeddings are extracted and reinforced with prototypical triplet loss to enhance robustness. A unified module then facilitates cross-adaptation between the support and query sets, producing classification features that effectively represent the relationship between them, thus better adapting to few-shot remote sensing classification challenges with domain shifts. Our proposed method’s excellence and generality are confirmed by comprehensive experiments.

关键词： Training image segmentation Adaptation models image recognition Feature extraction Data processing Robustness Labeling remote sensing Testing

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