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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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ISAR Refocusing Algorithm for Maneuvering Ship Targets SAR Data Based on Parametric Sparse Bayesian Learning

ISAR Refocusing Algorithm for Maneuvering Ship Targets SAR D...

引用

IET International Radar conference 2023, IRC 2023

作者： Xiong, Shichao Wang, Haobo Ni, Jiacheng Zhang, Hongwei Lv, Benyuan Yuan, Hang Luo, Ying Zhang, Qun Information and Navigation College Air Force Engineering University Xi'an China Key Laboratory of Wave Scattering and Remote Sensing Information Fudan University Shanghai China

ISBN: (纸本)9781839539954

SAR imaging of maneuvering ship targets under complex environment is a challenge problem. This paper proposed a ISAR refocusing algorithm for SAR data of maneuvering ship targets based on parametric SBL. The signal model of ship fluctuation in atrocious sea state is analysed firstly. The Doppler chirp rates of scatters vary with the position of scatters. The Doppler chirp rates is set as the parameters of the observation matrix. Then the hierarchical SBL model with Laplace prior is constructed. Variational inference and Quasi-Newton methos are applied to achieve Bayesian inference and updating parameters of the observation matrix, respectively. the experiment results have demonstrated that the proposed algorithm can obtain the focused image of maneuvering ship targets under the situation of low SNR and incomplete data. © The Institution of Engineering & Technology 2023.

关键词： signal to noise ratio

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Unsupervised semantic segmentation of radar sounder data using contrastive learning 28

Unsupervised semantic segmentation of radar sounder data usi...

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conference on image and signal processing for remote sensing XXVIII

作者： Donini, Elena Amico, Mattia Bruzzone, Lorenzo Bovolo, Francesca Fdn Bruno Kessler Ctr Digital Soc Via Sommarive 18 I-38123 Trento Italy Univ Trento Dept Informat Engn & Comp Sci Sommarive 5 I-38123 Trento Italy

ISBN: (纸本)9781510655386;9781510655379

Radar Sounders (RSs) are active sensors widely used for planetary exploration and Earth observation that probe the subsurface in a non-intrusive way by acquiring vertical profiles, called radargrams. Radargrams contain information on subsurface geology and are analyzed with neural networks for segmentation and target detection. However, most of these methods rely on supervised training, which requires a large amount of labeled data that is hard to retrieve. Hence, a need emerges for a novel method for unsupervised radargram segmentation. This paper proposes a novel method for unsupervised radargram segmentation by analyzing semantically meaningful features extracted from a deep network trained with a contrastive logic. First, the network (encoder) is trained using a pretext task to extract meaningful features (query). Considering a dictionary of possible features (keys), the encoder training loss can be defined as a dictionary look-up problem. Each query is matched to a key in a large and consistent dictionary. Although such a dictionary is not available for RS data, it is dynamically computed by extracting meaningful features with another deep network called the momentum encoder. Secondly, deep feature vectors are extracted from the encoder for all radargram pixels. After the feature selection, the feature vectors are binarized. Since pixels of the same class are expected to have similar feature vectors, we compute the similarity between the feature vectors to generate a cluster of pixels for each class. We applied the proposed method to segment radargrams acquired in Greenland by the MCoRDS-3 sensor, achieving good overall accuracy.

关键词： Unsupervised semantic segmentation Contrastive learning image processing Radar sounder Subsurface sensing

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A Task-Specific remote sensing image Manipulation Based on Deep Detection Network and Mask Seam Carving 7

A Task-Specific Remote Sensing Image Manipulation Based on D...

引用

7th International conference on Frontiers of signal processing, ICFSP 2022

作者： Xu, Ruijie Ma, Fei Xin, Mengxuan Huang, Liang Hu, Jin Cui, Yinzhe Beijing University of Chemical Technology College of Information Science and Technology Beijing China

ISBN: (数字)9781665481588

ISBN: (纸本)9781665481588

It is usually necessary to remove the important man-made objects about state security or change their GPS locations, before the remote sensing images are released to the public. Seam Carving, a technique based on content-aware, can be used to deliberately manipulate images. Nevertheless, it's still a tough challenge to generate Seam Carving forgeries without aliasing artifacts. This paper presents a task-specific Seam Carving approach based on ternary mask map. First, we introduce a deep detection network to detect the targets containing sensitive information. Then we convert the detection results into the ternary mask maps, where the ternaries respectively represent the targets that should be deleted, preserved and pending. Finally, we redesign the energy function of Seam Carving so that it can fix the seam's path based on the mask maps. The proposed method is evaluated on a large collection of images, demonstrating that the improved algorithm can better delete specific objects or change their GPS locations with well-maintained structure and shape of other important content. © 2022 IEEE.

关键词： remote sensing

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Impact of Dataset Characteristics on the Accuracy of SAR image Target Detection

Impact of Dataset Characteristics on the Accuracy of SAR Ima...

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

作者： Jinwei Han Lihong Kang Xiaohan Zhang Jing Tian Xiangwei Xing Graduate School Space Engineering University Beijing China The Beijing Remote Sensing Information Institute Beijing China

ISBN: (数字)9798331515669

ISBN: (纸本)9798331515676

The quality of a dataset significantly impacts the performance of algorithm models, yet a unified standard for SAR image dataset production remains absent. Currently, there is no consensus on the optimal image type, bit depth, and preprocessing methods to maximize algorithm effectiveness. This paper explores how these factors—image type, bit depth, and preprocessing techniques—affect target detection models by creating and training multiple SAR image datasets. The results reveal that 8-bit Tagged image File Forma (TIFF) images, enhanced with linear stretching of the maximum and minimum values, achieve the best performance. These findings offer a valuable reference for establishing standards in the production of future SAR image datasets.

关键词： Training Accuracy Production Object detection Minimax techniques Radar polarimetry Resource management Standards Synthetic aperture radar remote sensing

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ACCURATE HEAD POSE ESTIMATION BASED ON MULTI-STAGE REGRESSION 29

ACCURATE HEAD POSE ESTIMATION BASED ON MULTI-STAGE REGRESSIO...

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IEEE International conference on image processing (ICIP)

作者： Liu, Yinchuan Gong, Yufei Lu, Zheng Zhang, Xuetao Xi An Jiao Tong Univ Inst Artificial Intelligence & Robot Xian Peoples R China Xi An Jiao Tong Univ Sch Software Engn Xian Peoples R China China Acad Space Technol Inst Remote Sensing Satellite Beijing Peoples R China

ISBN: (数字)9781665496209

ISBN: (纸本)9781665496209

This paper proposes a method for head pose estimation from a single image. We employ a multi-stage regression strategy. To overcome the discontinuity of Euler angles and quaternions and avoid the additional constraints required to directly regress the rotation matrix, we apply a continuous 6D representation to the head pose estimation problem. Each stage of the network regresses two 1x3 vectors, which are then transformed into a 3 x 3 rotation matrix by this continuous 6D representation. To better perceive the difference in rotation angles, we adopt the Riemann distance to measure the closeness between the network-estimated rotation matrix and the ground truth rotation matrix corresponding to the head pose. Experiments show that our method achieves the state-of-the-art on BIWI dataset and performs favorably on AFLW2000 dataset.

关键词： Head pose estimation 6D rotation representation multi-stage regression Riemann distance

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image Editing based on Diffusion Model for remote sensing image Change Captioning

Image Editing based on Diffusion Model for Remote Sensing Im...

引用

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

作者： Miaoxin Cai He Chen Can Li Shuyu Gan Liang Chen Yin Zhuang National Key Laboratory of Science and Technology on Space-Born Intelligent Information Processing Beijing Institute of Technology Beijing China

ISBN: (数字)9798331515669

ISBN: (纸本)9798331515676

remote sensing image Change Captioning (RSICC) is a task that utilizes natural language to describe changes in remote sensing images of the same area captured at different times. However, the significant temporal intervals between multi-temporal images, the infrequency of observable changes, and the limitations on observation locations make it difficult to acquire and annotate a large and diverse dataset for analyzing change in multi-temporal images. The scarcity of labeled data hinders the training of RSICC models, leading to poor generalization. Compared to annotated registered bi-temporal image, single-temporal data is easier to obtain. Therefore, to tackle the issue of poor generalization of RSICC models under limited annotated sample conditions, a text-guided image pairs generation (TGIPG) method is proposed to create synthetic RSICC datasets from single-temporal data and randomly sampled text instructions via a diffusion-based controllable image editing model. This approach generates more valid pairs of multi-temporal samples to address the constraints of limited change information. Specifically, this method utilizes language instructions to introduce change information into the diffusion process, gradually transforming the pre-phase image into the post-phase image. Our experiments on the LEVIR-CC dataset show that synthetic data can significantly enhance the performance of any RSICC model, with a restricted number of training samples, by employing this plug-and-play TGIPG method.

关键词： Training Semantics Natural languages Diffusion processes Diffusion models Data models Spatial databases Sensors remote sensing Synthetic data

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Robust Spatiotemporal Fusion of Satellite images via Convex Optimization 48

Robust Spatiotemporal Fusion of Satellite Images via Convex ...

引用

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

作者： Isono, Ryosuke Naganuma, Kazuki Ono, Shunsuke Tokyo Institute of Technology Japan

ISBN: (纸本)9781728163277

Spatiotemporal fusion (ST fusion) is a feasible solution to resolve a tradeoff between the temporal and spatial resolutions of satellite images. Although many ST fusion methods have been proposed, most methods have not been developed that explicitly take noise in observed images into account, despite the inevitable influence of noise caused by the observation equipment and environment. In this paper, we propose an optimization-based ST fusion method that is robust to noise. First, we introduce observation models for noisy satellite images and make certain assumptions on the relationship between the observed images and the target high-resolution image. Next, based on these models and assumptions, we formulate the fusion problem as a constrained optimization problem and develop an efficient algorithm based on a primal-dual splitting method for solving the problem. The performance of the proposed method was verified using simulated and real data, and the results illustrate that our method outperforms state-of-the-art ST fusion methods for both noiseless and noisy satellite images. © 2023 IEEE.

关键词： constrained optimization primal-dual splitting method remote sensing Spatiotemporal fusion

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MULTI-LABEL AERIAL image CLASSIFICATION BASED ON image-SPECIFIC CONCEPT GRAPHS 29

MULTI-LABEL AERIAL IMAGE CLASSIFICATION BASED ON IMAGE-SPECI...

引用

IEEE International conference on image processing (ICIP)

作者： Lin, Dan Chen, Zhikui Zhao, Liang Wang, Kai Dalian Univ Technol Sch Software Technol Liaoning Peoples R China Key Lab Ubiquitous Network & Serv Software Liaoni Liaoning Peoples R China

ISBN: (数字)9781665496209

ISBN: (纸本)9781665496209

Multi-label aerial image classification (MAIC) is a fundamental but challenging task for computer vision-based remote sensing applications. Existing MAIC models suffer from the insufficient semantic information of image and label representations. To this end, we integrate commonsense knowledge into the MAIC task and propose a novel Knowledge-augmented Concept Graph Learning (KCGL) framework. KCGL first collects relevant semantic concepts for each label from a commonsense knowledge graph ConceptNet. With the guidance of semantic concepts, an image decoupling module is employed to extract concept-specific image features from the input image. Then, KCGL constructs an individual concept graph for each image, in which nodes are corresponding to concept-specific image features and edges are their relations extracted from ConceptNet. Finally, the classification probability on each label is computed in the specific concept graph via a GCN-based encoder-decoder model. Experimental results prove that the proposed KCGL outperforms existing state-of-the-art MAIC models on two aerial image datasets.

关键词： Multi-label image classification image decoupling Knowledge graph Graph convolutional network

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Real-time image Recognition System based on Ultra-Low-Latency Spiking Neural Networks

Real-time Image Recognition System based on Ultra-Low-Latenc...

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

作者： Bin Lan Xi Zhang Heng Dong Ming Xu Beijing Institute of Technology Chongqing Innovation Center Chongqing China Beijing Institute of Technology Beijing China Sichuan TianFu New Area Beijing Institute of Technology Innovation Equipment Research Institute Chengdu China National Key Laboratory of Science and Technology on Space-Born Intelligent Information Processing(SBIIP) Beijing China

ISBN: (数字)9798331515669

ISBN: (纸本)9798331515676

remote sensing technology plays an important role in many tasks such as natural disaster detection, weather and climate monitoring and military defense. Currently, remote sensing image processing predominantly relies on Convolutional Neural Networks (CNNs) and Transformers, which require a huge amount of multiplication and addition operations, bringing pressure to the deployment of satellite platforms with strict power consumption and computing power constraints. Spiking neural network (SNN), where the signal is encoded as a spike train instead of analog values, significantly reduces energy consumption compared to traditional artificial neural network. This paper proposes an all-input parallel deployment approach that minimizes or eliminates interactions with off-chip memory, thereby accelerating inference speed and reducing power consumption. We deployed LeNet ultra-low latency SNN on the Xilinx VC709 Board. Experimental results demonstrate that the proposed accelerator achieves a processing speed of 1240 frames per second, an accuracy rate of 98.14%, and a power consumption of only 0.745 Watts, making it well-suited for the low power and real-time requirements of remote sensing tasks.

关键词： Power demand Accuracy Satellites Spiking neural networks Transformers Real-time systems Resource management remote sensing Monitoring Meteorology

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HyperDiff: Masked Diffusion Model with High-efficient Transformer for Hyperspectral image Cross-Scene Classification

HyperDiff: Masked Diffusion Model with High-efficient Transf...

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

作者： Zhang, Pei Wang, Dong Wu, Chanyue Yang, Jing Kang, Lei Bai, Zongwen Li, Ying Shen, Qiang School of Computer Science Northwestern Polytechnical University Xi'an China Shaanxi Key Laboratory of Intelligent Processing for Big Energy Data Yan'an University Yan'an China School of Automation and Software Engineering Shanxi University Taiyuan China Qingdao Topscomm Communication Co. Ltd. Qingdao China Department of Computer Science Aberystwyth University Aberystwyth United Kingdom

ISBN: (纸本)9798350368741

Hyperspectral image (HSI) cross-scene classification is a challenging task in remote sensing, particularly when real-time processing of Target Domain (TD) HSI is required, and data cannot be reused for training. While deep learning methods have shown promising results, the generalization ability of HSI representations remains limited, mainly due to class label imbalance. This paper introduces a dual-stage learning framework based on transfer learning to enhance classification accuracy in the TD. The framework includes a self-supervised learning stage and a supervised fine-tuning stage. The self-supervised stage focuses on learning robust representations by leveraging inherent structures within HSI data, while the fine-tuning stage uses training labels to extract semantic information. A masked diffusion model predicts masked tokens from unmasked ones, capturing both high-level structures and fine details in HSI data. An efficient spatiospectral Transformer, which removes self-attention from the decoder, is proposed to enhance the self-supervised process. This design allows mask tokens to obtain information from visible tokens without interacting with each other, reducing sequence length and computational costs. By decoding each mask token conditionally independently, only a subset of masked tokens is processed. Extensive experiments on two public HSI datasets demonstrate that the proposed method outperforms state-of-the-art techniques. © 2025 IEEE.

关键词： Cross-scene classification Diffusion model Hyperspectral image Transformer

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