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

共 3572 条记录，以下是81-90 订阅

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Comparative analysis of multipositional remote sensing techniques

Comparative analysis of multipositional remote sensing techn...

引用

conference on image processing, signal processing, and Synthetic Aperture Radar for remote sensing

作者： Yegorova, IA Yegorov, AD Voeikov MGO Res Ctr Atmospher Remote Sensing St Petersburg 194021 Russia

New integral multipositional remote sensing techniques have been developed to obtain the optical parameters of the atmosphere with more adequate accuracy. The results of multipositional techniques development and erro... 详细信息

ISBN: (纸本)0819426490

关键词： remote sensing multipositional systems integral techniques backscattering extinction

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HIERARCHICAL MULTINOMIAL LATENT MODEL WITH G⁰ DISTRIBUTION FOR remote sensing image SEMANTIC SEGMENTATION 5

HIERARCHICAL MULTINOMIAL LATENT MODEL WITH <i>G<SUP>0</SUP><...

引用

5th IEEE Global conference on signal and Information processing (GlobalSIP)

作者： Duan, Yiping Tao, Xiaoming Han, Chaoyi Lu, Jianhua Tsinghua Univ Dept Elect Engn Tsinghua Natl Lab Informat Sci & Technol State Key Lab Microwave & Digital Commun Beijing Peoples R China

ISBN: (纸本)9781509059904

Considering the scattering statistics and multi scale characteristics of the remote sensing images, this paper presents a hierarchical multinomial latent model with G(0) distribution (HML-G(0)) for remote sensing image semantic segmentation. In the proposed approach, hierarchical multinomial latent model is proposed to capture the multi scale information of the remote sensing images. Moreover, the flexibility of G(0) distribution is plugged into the hierarchical multinomial latent model for the segmentation of various types of land covers. Then, the developed Bayesian inference on the quadtree is incorporated in our approach, and the semantic segmentation map is achieved by bottom-up and top-down probability computation. Experimental results demonstrate that our proposed hierarchical scheme produces the semantic segmentation maps, and the exhibiting performance improvements in terms of labeling consistency and the detail preservation.

关键词： remote sensing images semantic segmentation hierarchical multinomial latent model G(0) distribution Bayesian inference

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A Multi-DSP Based remote sensing image Compression System

A Multi-DSP Based Remote Sensing Image Compression System

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2006 8th International conference on signal processing

作者： Wang Qian Zeng Yifang Ge Bao-shan Li Bo School of Computer Science and Technology BeiHang University Research Institute of Information Technology Beijing

ISBN: (纸本)0780397371

remote sensing image data is huge and increasing unceasingly,which challenges real-time processing. In accordance with this feature,the paper presents a kind of hardware architechure solution based on 4-DSP blocks,designs and realizes a high performance remote sensing image compression model *** paper gives out a compression algorithm which takes advantage of the correlation between near neighhbour,far neighbour and father-children coefficients to *** algorithm is better than SPIHT and JPEG2000,and is transported into the model machine *** last,the paper gives out the experiment results and the improving directions.

关键词： DSP image A Multi-DSP Based remote sensing image Compression System

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Domain Generalized Object Detection for remote sensing images 31

Domain Generalized Object Detection for Remote Sensing Image...

引用

31st IEEE conference on signal processing and Communications Applications (SIU)

作者： Durakli, Efkan Aptoula, Erchan Gebze Tech Univ Dept Comp Engn Kocaeli Turkiye Sabanci Univ Fac Engn & Nat Sci Istanbul Turkiye

ISBN: (纸本)9798350343557

Building roof type detection from remotely sensed images is a crucial task for many remote sensing applications, including urban planning and disaster management. In recent years, deep learning-based object detection approaches have demonstrated outstanding performance in this field. However, most of these approaches assume that the training and testing data are sampled from the same distribution. When there are differences between the distributions of training and test data, known as domain shift, the performance significantly degrades. In this paper, we proposed a domain generalization method to address domain shift at the instance and image level for roof type detection from remote sensing images. Furthermore, we evaluated our proposed method with IEEE Data Fusion Contest 2023 dataset. The proposed approach is the first of its kind in terms of domain generalization for remote sensing object detection.

关键词： domain generalization object detection remote sensing

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A Study on the Impact of the Spatial and Spectral Resolution on Plant Species Richness in Mediterranean Regions Using Optical remote sensing Data 29

A Study on the Impact of the Spatial and Spectral Resolution...

引用

conference on image and signal processing for remote sensing xxix

作者： Boakye, Ama Serwah Martinez, Margarita Huesca Paris, Claudia Univ Twente ITC Dept Nat Resources Enschede Netherlands

ISBN: (纸本)9781510666955;9781510666962

Plant Species Richness (PSR) is one of the most widely used metrics to estimate alpha diversity in ecology. Several approaches have been developed to estimate PSR with remote sensing (RS) data. Among them, the Spectral Diversity Hypothesis (SDH) approach can be successfully applied to airborne hyperspectral data. Although effective, these data are limited in space and time due to high aerial acquisition costs. Satellite multispectral data are continuously acquired on a global scale, but their spatial and spectral resolutions are not comparable to those of hyperspectral data. Although some studies compared different optical data for estimating PSR using SDH, the impact of the spatial and spectral resolutions on the assessment of this biodiversity indicator is not clear. Moreover, most of the studies focus on dense tropical forest areas or wetlands, while little has been done to test the SDH approach in open forests located in Mediterranean regions. For all these reasons, the present work aims to: (1) apply and interpret PSR estimated with the SDH approach in open Mediterranean forest, and (2) evaluate the impact of the spatial and spectral resolutions on PSR estimation using real and simulated RS data. The PSR was estimated applying the SDH approach on a 4m hyperspectral data (373 bands), 30m multispectral satellite data (7 bands), synthetic 16m and 30m hyperspectral data (373 bands), and synthetic 4m multispectral data (7 bands). Preliminary results carried out in the San Joaquin Experimental Range (SJER) indicate that: (1) there is a weak correlation between spectral and species diversity in the less dense forest areas (R-2 =-0.13 for the hyperspectral and R-2 =0.14 for the multispectral data), while revealing good correlation in the more dense forest areas (R-2 =0.68 for the hyperspectral and R-2 =0.65 for the multispectral data), (2) the number of identified spectral species is more influenced by the spectral resolution than the spatial one, and (3) high spatial

关键词： Biodiversity Spectral diversity Species richness Spectral Diversity Hypothesis (SDH) Mediterranean ecosystem hyperspectral data multispectral data

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TOTAL-VARIATION-REGULARIZED TENSOR RING COMPLETION FOR remote sensing image RECONSTRUCTION 44

TOTAL-VARIATION-REGULARIZED TENSOR RING COMPLETION FOR REMOT...

引用

44th IEEE International conference on Acoustics, Speech and signal processing (ICASSP)

作者： He, Wei Yuan, Longhao Yokoya, Naoto RIKEN Ctr Adv Intelligence Project AIP Wako Saitama Japan Saitama Inst Technol Grad Sch Engn Fukaya Saitama Japan

ISBN: (纸本)9781479981311

In recent studies, tensor ring (TR) decomposition has shown to be effective in data compression and representation. However, the existing TR-based completion methods only exploit the global low-rank property of the visual data. When applying them to remote sensing (RS) image processing, the spatial information in the RS image is ignored. In this paper, we introduce the TR decomposition to RS image processing and propose a tensor completion method for RS image reconstruction. We incorporate the total-variation regularization into the TR completion model to exploit the low-rank property and spatial continuity of the RS image simultaneously. The proposed algorithm is solved by the augmented Lagrange multiplier method and has shown the superior performance in hyperspectral image reconstruction and multi-temporal RS image cloud removal against the state-of-the-art algorithms.

关键词： remote sensing reconstruction tensor ring total-variation cloud removal

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Semantic structure tree with application to remote sensing image segmentation

Semantic structure tree with application to remote sensing i...

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conference on image and signal processing for remote sensing XVI

作者： Zhang, Xiangrong Pan, Xian Hou, Biao Jiao, Licheng Xidian Univ Inst Intelligent Informat Proc Minist Educ Key Lab Intelligent Percept & Image Understanding Xian 710071 Peoples R China

ISBN: (纸本)9780819483478

This paper presents a new method based on Semantic Structure Tree (SST) for remote sensing image segmentation, in which, the semantic image analysis is used to construct the SST of the image. The leaves of the SST represent the semantics of the image and serve as human semantic understanding of the image. The root of the tree is the whole image. The SST uses grammar rules to construct a hierarchy structure of the image and gives a complete high-level semantics contents description of the image. Experimental results show that the tree can give efficient description of the semantic content of the remote sensing image, and can be well used in remote sensing image segmentation.

关键词： image semantics image grammar semantic structure tree remote sensing image segmentation

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Monitoring of Renewable energy sources with remote sensing, Open data and field data in Bulgaria 29

Monitoring of Renewable energy sources with remote sensing, ...

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conference on image and signal processing for remote sensing xxix

作者： Spasova, Temenuzhka Dancheva, Adlin Avetisyan, Daniela Ivanova, Iva Popov, Iliyan Shirov, Boris Bulgarian Acad Sci SRTI BAS Space Res & Technol Inst Acad G Bonchev Strbl 1 Sofia 1113 Bulgaria Res Lab Met Air Syst OXYMET Sofia Bulgaria

ISBN: (纸本)9781510666955;9781510666962

Monitoring through satellite data, in situ (including spectrometer data, GPS, thermal camera), Open data, data from various devices and Unmanned aerial vehicles (UAV) in the selected anthropogenic sites is of extremely high ecological importance for tracking natural processes, the consequences of climate changes and the creation of a useful model for the analysis of spectral characteristics based on machine learning. The timeliness of the data and the spatial extent of the observed objects allow satellite information to be reliable in monitoring and making predictions about the risk and potential risk of natural disasters, rise of average air temperatures and anthropogenic pollution. The sites were pre-marked based on open data from non-governmental organizations (NGOs) and administration. Data from the Multispectral Instrument (MSI) of the Sentinel 2 platform and SAR of the European Space Agency's Copernicus program, spectrometer (380-780nm) and drone data were used. Landsat sensors and data from Sentinel 3 (EUMETSAT) were used to calculate the surface temperature of renewable energy sites such as photovoltaic parks. Data from different years were used in order to track the studied territories according to NUTS2. The result is the development of a useful hybrid model for spectral analysis and tracking of spatial dynamics and surface changes of objects of interest based on satellite and field surveys. Data from the ground mobile and autonomous weather station AWG (1), powered by an environmentally friendly magnesium-air battery was improved specifically for the project. Another important task is the creation of an energy atlas for the benefit of the Earth's Digital Twins. The data is part of an open data catalog of the NGO Eco Global Monitoring TA(2).

关键词： Climate change Digital Twins LST NUTS 2 Sentinel 3 Renewable energy SAR

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LAND USE AND LAND COVER CLASSIFICATION BASE ON image SALIENCY MAP COOPERATED CODING

LAND USE AND LAND COVER CLASSIFICATION BASE ON IMAGE SALIENC...

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IEEE International conference on image processing (ICIP)

作者： Zhang, Hui Zhang, Jinfang Xu, Fanjiang Chinese Acad Sci Inst Software Beijing 100864 Peoples R China

ISBN: (纸本)9781479983391

The land use and land cover classification is an important and hot research topic in remote sensing image processing. How to use information effectively in remote sensing data to categorize different land use and land cover scenes needs urgent attention. In this paper, we analysis the Bag-of-Word model based feature extracting method systematically, and propose the Saliency Map Cooperated (SMC) coding strategy according characters of remote sensing images. The proposed SMC takes into account both the primary objects and partial of large scale objects in remote sensing images, with little effecting on texture dominated images. Extensive experimental results show the efficiency of the proposed SMC coding strategy.

关键词： Land Use and Land Cover Classification remote sensing image Bag-of-Words Model Saliency Map Cooperated Coding

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A Survey on image Completion Techniques in remote sensing images 4

A Survey on Image Completion Techniques in Remote Sensing Im...

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4th International conference on signal processing, Communication and Networking (ICSCN)

作者： Lakshmanan, V. Gomathi, R. Univ Coll Engn Dindigul Dept ECE Dindigul Tamil Nadu India

ISBN: (纸本)9781509047406

Nowadays, reconstruction of satellite images is one of the important challenges in technology. Sometimes, remote sensing images have dead pixels or pixel missing gives poor visual quality. It is mainly due to the presence of clouds, fogs, or shadows since these images are acquired by sensors at different seasons. The other serious problems in remote sensing images are instrumentation error, registration error and losses of image data during transmission. To get a good visual quality images, degraded remote sensing images are processed by using the application of inpainting. The main objectives of this image inpainting approaches are to fill lost parts of images, delete unwanted objects, remove noise in images and to enhance images quality. image completion or image inpainting process is one in which the damaged portions are reconstructed or to fill the lost regions using data collected from surrounding areas in original image. So far, a number of inpainting algorithms are available. This paper gives a detailed survey of some inpainting methods which are suitable for remotely sensed images.

关键词： image Completion remote sensing images PDE Exemplar Wavelet DCT MRF

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