- 标题
- 摘要
- 关键词
- 实验方案
- 产品
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Effect of Open Soil Surface Patterns on Soil Detectability Based on Optical Remote Sensing Data
摘要： Arable soils are subjected to the altering influence of agricultural and natural processes determining surface feedback patterns therefore affecting their ability to reflect light. However, remote soil mapping and monitoring usually ignore information on surface state at the time of data acquisition. Conducted research demonstrates the contribution of surface feedback dynamics to soil reflectance and its relationship with soil properties. Analysis of variance showed that the destruction surface patterns accounts for 71% of spectral variation. The effect of surface smoothing on the relationships between soil reflectance and its properties varies. In the case of organic matter and medium and coarse sand particles, correlation decreases with the removement of surface structure. For particles of fine sand and coarse silt, grinding changes spectral areas of high correlation. Partial least squares regression models also demonstrated variations in complexity, R2cv and RMSEPcv. Field dynamics of surface feedback patterns of arable soils causes 22–46% of soil spectral variations depending on the growing season and soil type. The directions and areas of spectral changes seem to be soil-specific. Therefore, surface feedback patterns should be considered when modelling soil properties on the basis of optical remote sensing data to ensure reliable and reproducible results.
关键词： digital soil mapping,remote sensing,spectral reflectance,surface feedback
更新于2025-09-23 15:21:21
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[IEEE IGARSS 2018 - 2018 IEEE International Geoscience and Remote Sensing Symposium - Valencia (2018.7.22-2018.7.27)] IGARSS 2018 - 2018 IEEE International Geoscience and Remote Sensing Symposium - Oil-Palm Tree Detection in Aerial Images Combining Deep Learning Classifiers
摘要： Palm oil is the largest vegetable oil in the world in terms of produced volume, and 75% of global production is used for food and cooking purposes. Sustainable management of the producing areas calls for the frequent assessment of field conditions. In this paper, we investigate an automatic algorithm based on deep learning that is capable to build an inventory of individual oil-palm trees using aerial color images collected by unmanned aerial vehicles. The idea consists of combining the outputs of two independent convolutional neural networks, trained on partially distinct subsets of samples and different spatial scales to capture coarse and fine details of image patches. The estimated posterior probabilities are combined by simple averaging as to improve detection accuracy and estimate the confidence for each individual detection. Non-maxima suppression removes weak detections. Experiments at three commercial oil-palm tree plantations sites aged two, four, and 16 years in Northern Brazil revealed overall detection accuracies in the range 91.2–98.8% using orthomosaics of decimeter spatial resolution. The proposed approach can be a useful component of a forest monitoring system based on remote sensing.
关键词： convolutional neural networks,classification,Tree counting,remote sensing,forest inventory
更新于2025-09-23 15:21:21
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[IEEE IGARSS 2018 - 2018 IEEE International Geoscience and Remote Sensing Symposium - Valencia, Spain (2018.7.22-2018.7.27)] IGARSS 2018 - 2018 IEEE International Geoscience and Remote Sensing Symposium - Deep Semantic Hashing Retrieval of Remotec Sensing Images
摘要： Due to the rapid evolution of satellite systems, traditional nearest neighbor image retrieval methods used in large-scale image retrieval usually cause "curse of dimensionality" that leads to boosting feature storage and slow retrieval speed. The hashing method, which aims at mapping the high-dimensional data to compact binary hash codes in Hamming space and quickly calculates the Hamming distance by bit operation and XOR operation, can effectively achieve search and retrieval with remaining similarity for big data. In this paper, we propose a novel image retrieval method based on deep hashing learning, called deep semantic hashing(DSH), attempting to mining the semantic information of remote sensing(RS) images. Experiments carried out on an archive of RS images point out that DSH outperforms other methods to achieve the state-of-the-art performance in image retrieval applications.
关键词： image retrieval,semantic mining,Remote sensing,deep learning,hashing methods
更新于2025-09-23 15:21:21
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[IEEE IGARSS 2018 - 2018 IEEE International Geoscience and Remote Sensing Symposium - Valencia, Spain (2018.7.22-2018.7.27)] IGARSS 2018 - 2018 IEEE International Geoscience and Remote Sensing Symposium - Effects of Nonuniform Vertical Profiles of Suspended Particles on Remote Sensing Reflectance of Turbid Water
摘要： The in situ data and forward radiative transfer model were applied to simulate the nonuniform vertical profiles of suspended particles in turbid Poyang Lake. The sensitivity of remote sensing reflectance (Rrs) associated with nonuniform water column showed correlation with suspended particulate matter (SPM), wavelength and water depth. Different nonuniform vertical profiles could cause more than 108% overestimation or 60% underestimation of Rrs at most. The uncertainties in Rrs decreased with the increase of water depth. The sensitive wavelength moved to longer wavelength and the maximum influence water depth let up, along with the increase in concentration of SPM in surface water. A dimensionless parameter made up of beam attenuation coefficient of surface water, water depth and SPM vertical distribution, was established to quantitatively describe the effects of vertically nonuniform water column on Rrs.
关键词： vertically nonuniform water column,water optical properties,remote sensing reflectance
更新于2025-09-23 15:21:21
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[IEEE IGARSS 2018 - 2018 IEEE International Geoscience and Remote Sensing Symposium - Valencia, Spain (2018.7.22-2018.7.27)] IGARSS 2018 - 2018 IEEE International Geoscience and Remote Sensing Symposium - Automated Analysis of Remotely Sensed Images Using the Unicore Workflow Management System
摘要： The progress of remote sensing technologies leads to increased supply of high-resolution image data. However, solutions for processing large volumes of data are lagging behind: desktop computers cannot cope anymore with the requirements of macro-scale remote sensing applications; therefore, parallel methods running in High-Performance Computing (HPC) environments are essential. Managing an HPC processing pipeline is non-trivial for a scientist, especially when the computing environment is heterogeneous and the set of tasks has complex dependencies. This paper proposes an end-to-end scientific workflow approach based on the UNICORE workflow management system for automating the full chain of Support Vector Machine (SVM)-based classification of remotely sensed images. The high-level nature of UNICORE workflows allows to deal with heterogeneity of HPC computing environments and offers powerful workflow operations such as needed for parameter sweeps. As a result, the remote sensing workflow of SVM-based classification becomes re-usable across different computing environments, thus increasing usability and reducing efforts for a scientist.
关键词： High-Performance Computing (HPC),Remote Sensing,Scientific Workflows,UNICORE,Support Vector Machine (SVM)
更新于2025-09-23 15:21:21
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Land Use Change Detection Using Remote Sensing Technology
摘要： Background: Change detection is useful in many applications related to land use and land cover (LULC) changes, such as shifting cultivation and landscape changes, land degradation and desertification. Remotes sensing technology has been used for the detection of the change in land use land cover in upper Rib watershed. The main objective of this study was to detect the land use change using remote sensing for sustainable land use planning in Upper Rib watershed. Methodology: The two satellite images for the year 2007 and 2018 were downloaded and used for detecting the land cover changes. Maximum likelihood classification was used in ERDAS Imagine tool for classifying the images. Ground truth points were collected and used for verification of image classification. Results: The accuracy of image classification were checked using the Ground truth points and the has showed an overall accuracy of 84% and a kappa coefficient of 0.8 which indicates the method of classification and the images used were very good. During this study period an agricultural land has showed an increasing trend by 13.78%, while grassland had decreased by 15.97% due to an increase of interest to cropland area. Conclusion: In Upper Rib watershed, there has been a significant land use change which was due to an increase in population with a high interest to croplands which resulted in an increase of agricultural land by 13.78% over 11 years period.
关键词： Land use change,Upper Rib watershed,Remote sensing,ERDAS Imagine
更新于2025-09-23 15:21:21
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Télédétection satellitaire des surfaces enneigées et englacées
摘要： This article presents an overview of recent advances in remote sensing applied to the study of snow and glacierized areas, in which the French scientific community has been involved. Whatever the type of satellite data, optical, radar, lidar or gravimetric, these works on seasonal or perennial snow cover, mountain glaciers, ice caps, sea ice, and lake or river ice, aim at documenting both the physical characteristics of these objects and their spatial and temporal variability at local, regional or global scales.
关键词： glaciers,remote sensing,snow,ice,spatial variability,satellite data,temporal variability
更新于2025-09-23 15:21:21
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A novel approach for the extraction of cloud motion vectors using airglow imager measurements
摘要： The paper explores the possibility of implementing an advanced photogrammetric technique, generally employed for satellite measurements, on airglow imager, a ground-based remote sensing instrument primarily used for upper atmospheric studies, measurements of clouds for the extraction of cloud motion vectors (CMVs). The major steps involved in the algorithm remain the same, including image processing for better visualization of target elements and noise removal, identification of target cloud, setting a proper search window for target cloud tracking, estimation of cloud height, and employing 2-D cross-correlation to estimate the CMVs. Nevertheless, the implementation strategy at each step differs from that of satellite, mainly to suit airglow imager measurements. For instance, climatology of horizontal winds at the measured site has been used to fix the search window for target cloud tracking. The cloud height is estimated very accurately, as required by the algorithm, using simultaneous collocated lidar measurements. High-resolution, both in space and time (4 min), cloud imageries are employed to minimize the errors in retrieved CMVs. The derived winds are evaluated against MST radar-derived winds by considering it as a reference. A very good correspondence is seen between these two wind measurements, both showing similar wind variation. The agreement is also found to be good in both the zonal and meridional wind velocities with RMSEs < 2.4 m s?1. Finally, the strengths and limitations of the algorithm are discussed, with possible solutions, wherever required.
关键词： lidar measurements,photogrammetric technique,MST radar,cloud motion vectors,ground-based remote sensing,airglow imager
更新于2025-09-23 15:21:21
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Patches of polar mesospheric summer echoes characterized from radar imaging observations with MAARSY
摘要： A recent study has hypothesized that polar mesospheric summer echoes (PMSEs) might consist mainly of localized isotropic scattering. These results have been inferred from indirect measurements. Using radar imaging with the Middle Atmosphere Alomar Radar System (MAARSY), we observed horizontal structures that support our previous findings. We observe that small-scale irregularities, causing isotropic scattering, are organized in patches. We find that patches of PMSEs, as observed by the radar, are usually smaller than 1 km. These patches occur throughout the illuminated volume, supporting that PMSEs are caused by localized isotropic or inhomogeneous scattering. Furthermore, we show that imaging can be used to identify side lobe detections, which have a significant influence even for narrow beam observations. Improved spectra estimations are obtained by selecting the desired volume to study parameters such as spectral width and to estimate the derived energy dissipation rates. In addition, a combined wide beam experiment and radar imaging is used to resolve the radial velocity and spectral width at different volumes within the illuminated volume.
关键词： radio science (remote sensing),Meteorology and atmospheric dynamics (turbulence; instruments and techniques)
更新于2025-09-23 15:21:21
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Road Segmentation Based on Hybrid Convolutional Network for High-Resolution Visible Remote Sensing Image
摘要： Road segmentation plays an important role in many applications, such as intelligent transportation system and urban planning. Various road segmentation methods have been proposed for visible remote sensing images, especially the popular convolutional neural network-based methods. However, high-accuracy road segmentation from high-resolution visible remote sensing images is still a challenging problem due to complex background and multiscale roads in these images. To handle this problem, a hybrid convolutional network (HCN), fusing multiple subnetworks, is proposed in this letter. The HCN contains a fully convolutional network, a modi?ed U-Net, and a VGG subnetwork; these subnetworks obtain a coarse-grained, a medium-grained, and a ?ne-grained road segmentation map. Moreover, the HCN uses a shallow convolutional subnetwork to fuse these multigrained segmentation maps for ?nal road segmentation. Bene?tting from multigrained segmentation, our HCN shows impressing results in processing both multiscale roads and complex background. Four testing indicators, including pixel accuracy, mean accuracy, mean region intersection over union (IU), and frequency weighted IU, are computed to evaluate the proposed HCN on two testing data sets. Compared with ?ve state-of-the-art road segmentation methods, our HCN has higher segmentation accuracy than them.
关键词： high-resolution visible remote sensing image,Convolutional neural network (CNN),road segmentation
更新于2025-09-23 15:21:21