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

共 3565 条记录，以下是121-130 订阅

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FPGA-Based High-Speed remote sensing Satellite image Data Transmission System 10th

FPGA-Based High-Speed Remote Sensing Satellite Image Data Tr...

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10th International conference on signal and Information processing, Network and Computers, ICSINC 2022

作者： He, Yifeng Sun, Peng Liu, Qian Zhang, Hao Zhao, Dayang Xi’an Institute of Space Radio Technology Xi’an710100 China

ISBN: (纸本)9789811999673

At present, with the increasing number of remote sensing satellite systems established in China, a large amount of remote sensing satellite image data has been obtained. Based on FPGA, this paper studies the transmission of high-speed remote sensing satellite image data, studies the image characteristics, image preprocessing methods and image detection principles of remote sensing satellite images, and analyzes various methods of image preprocessing. For different targets and different image backgrounds, the specific processing algorithms are also different. Only by flexibly using the basic theory of image processing can a good image processing effect be achieved. Among them, the fast connected region labeling algorithm designed and implemented can realize the merging of equivalent pairs while scanning. The results show that the system can reliably implement the algorithm in this paper, complete the processing of image data, and meet the requirements of the project. © 2023, The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.

关键词： Field programmable gate arrays (FPGA)

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Cross-Band Fusion for Multispectral Scene Classification 2

Cross-Band Fusion for Multispectral Scene Classification

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

作者： Li, Jin Liu, Yu Jiang, Zhizhuo Wang, Xueqian Zheng, Bolun Tsinghua University Shenzhen International Graduate School Shenzhen China Tsinghua University Department of Electronic Engineering Beijing China Hangzhou Dianzi University School of Automation Hangzhou China

ISBN: (纸本)9798331515669

With the daily acquisition of vast amounts of remote sensing images by satellites, effectively utilizing these images for Earth observation has become a significant research focus. Multispectral image scene classification plays a fundamental role in advancing the understanding of remote sensing imagery. Existing methods for multispectral image scene classification do not sufficiently explore the interrelationships between different bands, which limits the performance of models in this task. Unlike these, we propose a cross-band fusion method for multispectral scene classification. Specifically, we employ two appropriate visual backbones to extract band-level visual features from different bands, followed by a cross-band fusion module to fuse the features from various bands adaptively. This approach fully explores the complementary information between different bands, achieving enhanced accuracy in multispectral scene classification. Extensive experiments on a commonly used dataset BigEarthNet validate that our proposed cross-band fusion method outperforms state-of-the-art (SOTA) methods. © 2024 IEEE.

关键词： image classification

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Dense object detection for remote sensing images based on multi-scale partitioning and super-resolution optimization 24

Dense object detection for remote sensing images based on mu...

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3rd International conference on signal processing, Computer Networks and Communications, SPCNC 2024

作者： Li, Lu Zhu, Xia Ni, Shaofeng Gao, Fan State Grid Location based Service Co. Ltd. Beijing China State Grid Information & Telecommunication Group Co. Ltd. Beijing China State Key Laboratory of Virtual Reality Technology and Systems Beihang University Beijing China

ISBN: (纸本)9798400710834

Detecting dense objects in remote sensing image is a critical yet challenging task due to variations in target scale, image resolution, and complex backgrounds. Traditional detection methods often struggle with these dense regions, particularly for small or overlapping objects. To overcome these limitations, this study introduces an innovative approach that combines multi-scale partitioning with super-resolution optimization to improve the detection of densely packed objects. By applying K-Means clustering to identify regions of interest (ROI) and using a local super-resolution network to enhance these areas, we improve the clarity and detectability of small-scale objects. Additionally, we implement a global-local feature extraction architecture, where global features are captured using Transformer-based attention, while local features focus on fine-grained object details. Non-Maximum Suppression (NMS) is employed to fuse multi-scale features and eliminate redundant detections. Experimental results on the DOTA and HRSC2016 datasets demonstrate the effectiveness of our approach, achieving superior performance over state-of-the-art methods in detecting small and densely packed targets. This method shows significant promise for remote sensing applications requiring high-precision target detection. © 2024 Copyright held by the owner/author(s).

关键词： Proximity sensors

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MMAQ: A MULTI-MODAL SELF-SUPERVISED APPROACH FOR ESTIMATING AIR QUALITY FROM remote sensing DATA 31

MMAQ: A MULTI-MODAL SELF-SUPERVISED APPROACH FOR ESTIMATING ...

引用

2024 International conference on image processing

作者： Angelis, G. F. Emvoliadis, A. Drosou, A. Tzovaras, D. Ctr Res & Technol Hellas Informat & Technol Inst Thessaloniki Greece Aristotle Univ Thessaloniki Dept Elect & Comp Engn Thessaloniki Greece

ISBN: (纸本)9798350349405;9798350349399

Air quality intensifies climate change through global pollution, particularly affecting lower and middle-income countries that lack local ground pollutant monitoring networks. While atmospheric pollutant satellite measurements offer a broad view, their coarse spatial resolution limits detailed air pollution insights, resulting in unmonitored regions and information gaps. Furthermore, satellites generate extensive data that is often challenging to directly correlate with ground air pollution stations. To overcome this, we propose a multi-modal self-supervised approach that learns from diverse satellite sources for air pollution monitoring. More specifically, aside from incorporating a combination of multi-spectral and spectral modalities (Sentinel-2 & 5P products), we also leverage tabular land cover data. Their integration into multi-modal self-supervised learning is highlighted, employing a novel augmentation scheme that results in more resilient embeddings. The proposed approach integrates a self-supervised redundancy reduction loss in a multi-modal fashion, capturing both inter-modal and intra-modal correspondences. Furthermore, an adaptive loss weighting mechanism is introduced to effectively combine different multi-modal losses. Our approach's efficacy is showcased in the air pollution prediction task, exhibiting a noteworthy improvement of up to 17% compared to existing methods. Furthermore, in our experiments, the applicability of our approach in other environmental tasks is also exhibited.

关键词： multi-modal self-supervised remote sensing air pollution

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PECR:PARAMETER-EFFICIENT TRANSFER LEARNING WITH CROSS-MODAL REPRESENTATION LEARNING FOR remote sensing VISUAL QUESTION ANSWERING 49

PECR:PARAMETER-EFFICIENT TRANSFER LEARNING WITH CROSS-MODAL ...

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49th IEEE International conference on Acoustics, Speech, and signal processing (ICASSP)

作者： Li, Pengfei He, Jinlong Liu, Gang Zhong, Shenjun Harbin Engn Univ Coll Comp Sci & Technol Harbin Peoples R China Monash Univ Monash Biomed Imaging Melbourne Vic Australia

ISBN: (纸本)9798350344868;9798350344851

remote sensing (RS) visual question answering (VQA) aims to provide accurate answers to questions related to RS images. Transformer-based models have gradually become popular to solve RS VQA tasks. Due to the ever-growing model size, full-parameter training of the model becomes prohibitively costly. Moreover, most current RS VQA methods primarily focus on improving unimodal image encoding, paying little attention to cross-modal interactions between visual and textual features. In this paper, we propose the Parameter-Efficient transfer learning with Cross-modal Representation learning model (PECR) for RS VQA tasks. Specifically, we introduce adapter-based parameter-efficient transfer learning techniques into the visual encoder and initialize them with pre-trained weights on large-scale RS images. Furthermore, we utilize a cross attention mechanism in the cross-modal fusion module to merge context representations of images and text, facilitating cross-modal representation learning. Experimental results demonstrate that our approach outperforms previous state-of-the-art methods on both RSVQA-LR and RSVQA-HR datasets. Additionally, we also validate that employing the adapter strategy for local parameter training can yield performance results comparable to full parameter training, significantly reducing the model training cost.

关键词： RS VQA Parameter-efficient transfer learning Transformer Cross-modal representation learning

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Efficient On-board remote sensing Scene Classification Using FPGA With Ternary Weight 2

Efficient On-board Remote Sensing Scene Classification Using...

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

作者： Qi, Guijie Qiao, Tingting Yu, Jingchi Xie, Yizhuang Beijing Institute of Technology National Key Laboratory of Science and Technology on Space-Born Intelligent Information Processing Beijing China

ISBN: (纸本)9798331515669

remote sensing Scene Classification (RSSC) is essential for applications such as environmental monitoring and catastrophe management, which often have stringent time constraints requiring real-time processing. On-board processing significantly enhances real-time performance but is challenged by limited computational resources and strict power requirements. To address these challenges, this study proposes an FPGA-based accelerator designed for Ternary Weight Networks (TWNs), which use ternary values {+1, 0, -1} for weights. By adopting TWNs, the multiplication operations in convolution are eliminated, resulting in a significant reduction in computing and power needs. Experimental results show that TWNs significantly reduce network parameters while retaining classification accuracy comparable to Full-precision Weight Networks (FPWNs). The FPGA-based accelerator achieves an energy-efficiency ratio of 434.11 GOP/W, outperforming most existing CNN accelerators, hence meeting the requirements for on-board RSSC. © 2024 IEEE.

关键词： image thinning

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Change Detection for remote sensing images based on Semantic Prototypes and Contrastive Learning 30

Change Detection for Remote Sensing Images based on Semantic...

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30th IEEE International conference on image processing (ICIP)

作者： Zhao, Guiqin Wang, Weiqiang Univ Chinese Acad Sci Sch Comp Sci & Technol Beijing 100049 Peoples R China

ISBN: (纸本)9781728198354

Deep networks have achieved remarkable success in addressing the problem of change detection for bi-temporal remote sensing images. While most networks focus on learning discriminating features using diverse backbone network architectures, few of them prioritize improving the performance of classification networks. In this paper, we propose a method called SPCL-BIT, which enhances the BIT method [1] by replacing its Softmax classifier with a more discriminative classifier based on semantic prototypes and contrastive learning. In the proposed classifier, we introduce two semantic prototypes to represent the change class and the unchanged class. A semantic encoder is then employed to model the relationship between these two prototypes. To obtain effective prototype representations with good inter-class separability and intra-class cohesiveness, we adopt a contrastive loss. The learned classifier, named SPCL-CD, based on semantic prototypes and contrastive learning, can be integrated with the backbone networks of most existing change detection models to enhance their performance. Comprehensive experimental results demonstrate the effectiveness of the SPCL-BIT network, and the proposed classification network, SPCL-CD, shows varying degrees of improvement when combined with the backbones of state-of-the-art networks in change detection.

关键词： Change detection semantic prototypes contrastive loss remote sensing

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Illigal irrigation Mapping in Oases Using Optical and radar Time-Series Data and Machine-Learning Classifiers 7

Illigal irrigation Mapping in Oases Using Optical and radar ...

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IEEE 7th International conference on Advanced Technologies, signal and image processing (ATSIP)

作者： Kassouk, Zeineb Akacha, Ferdaws Chebbi, Wafa Habaieb, Hamadi Saad, Atfa Chabaane, Zohra Lili Carthage Univ Natl Agron Inst Tunisia Lr1 7AGR01 Lr GREEN TEAM 43 Av Charles Nicolle Tunis 1082 Tunisia Deutsch Gesell Int Zusammenarbeit GIZ GmbH GIZ Tu Rue Le Grand Blvd Corniche Immeuble Grand Berges Du Lac Tunis 1053 Tunisia

ISBN: (纸本)9798350351491;9798350351484

The scarcity of water and the need for the irrigation of the oases leading to a significant overexploitation of the groundwater. The detection of irrigated areas based on illicit drilling is a well-resolved problem in Tunisia. The goal of thus paper is to explore the potential of optical and radar remote sensing data and machine learning in the identification and quantification of oases areas irrigated from illicit boreholes in the governorate of Kebili identification. We used a synergy of time-series optic Sentinel2(S2) and radar Sentinel1 (S1) data acquired from February to June 20022. The retrieved Oases land cover classes included three categories of stratums: 1 stratum (date Palm), 2 stratums (date palm and associated crops), 3 stratums (date palm, fruit trees and crops). In the first time, a hierarchical object-based classification approach was processed using the combined data derived from the normalized vegetation index (NDVI) and the 3 principal components (PCA1, 2 and 4) of the S2 time-series dataset. Three classifiers tested the random forest (RF), support vector machine (SVM) and k-nearest neighbor (KNN) classifiers, respectively using 40 samples of ground control data. The RF classifier performs better than SVM and KNN, with highest overall accuracy of 92 % and a Kappa coefficient of 0.87. Obtained The Rf result map were crossed to the soil moisture map derived from S1 data to obtain irrigated crops outside the legal irrigated areas. The result retained following this approach was compared to illegal drillings identified in the field survey and legal drilling and confirm more than 90% of illicit drillings. Furtherer more illegal extensions represent five times the area of the legal Public Irrigated Perimeters (PPI). The findings of this study might be useful for policy makers as well as planners.

关键词： Illegal irrigated area Oases Optic radar remote sensing Object oriented classification

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Cultivated land segmentation in RGB remote sensing images: nonuniform regularization with kernel space and graph cut

Cultivated land segmentation in RGB remote sensing images: n...

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2024 International conference on image, signal processing, and Pattern Recognition, ISPP 2024

作者： Wu, Wangsheng College of Computer and Information Science Chongqing Normal University Chongqing401331 China

ISBN: (纸本)9781510680425

To improve the application efficiency of RGB remote sensing images in agricultural land resource surveys, a cultivated land segmentation algorithm based on kernel space non-uniform regularization classification and improved graph cut was proposed. Firstly, extracting texture and color features of remote sensing images using Local Binary Pattern algorithm (LBP), Gabor filters, and RGB, HSV color space, respectively. Next, introducing a kernel method to map data from low-dimension to high-dimension, and construct a kernel space-based non-uniform regularization sparse representation model to classify and segment images in pixel level. Finally, an innovative graph cut algorithm is enhanced by incorporating a Gaussian distribution to redefine the penalty term for homogeneous regions and introducing a new color gradient measure to define the penalty term for boundaries. This approach effectively removes scatter and restricts the segmentation boundary. The average classification accuracy and average F1 score of the classifier proposed in this paper are about 2% and 3% higher than those of recent regularized subspace classifiers, respectively. Compared with the Graph cut algorithm, the proposed improved graph cut algorithm has an average mIoU improvement of about 9%. The average accuracy of the whole segmentation algorithm is 95.43%, and the average mIoU is 88.56%. Compared with the comparison algorithm, the proposed algorithm has higher segmentation accuracy, which proves that the proposed algorithm can adapt to the cultivated land segmentation scene of remote sensing images and is effective. © 2024 SPIE.

关键词： RGB color model

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

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

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

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

ISBN: (纸本)9798350368741

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. © 2025 IEEE.

关键词： contrastive language-image pre-training(CLIP) mask attention remote sensing change detection segment anything model(SAM) semantic information

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