- 标题
- 摘要
- 关键词
- 实验方案
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Extended attribute profiles on GPU applied to hyperspectral image classification
摘要： Extended pro?les are an important technique for modelling the spatial information of hyperspectral images at different levels of detail. They are used extensively as a pre-processing stage, especially in classi?cation schemes. In particular, attribute pro?les, based on the application of morphological attribute ?lters to the connected components of the image, have been shown to provide very good results. In this paper we present a parallel implementation of the attribute pro?les in CUDA for multispectral and hyperspectral imagery considering the attributes area and standard deviation. The pro?le computation is based on the max-tree approach but without building the tree itself. Instead, a matrix-based data structure is used along with a recursive ?ooding (component merging) and ?lter process. Additionally, a previous feature extraction stage based on wavelets is applied to the hyperspectral image in order to extract the most valuable spectral information, reducing the size of the resulting pro?le. This scheme ef?ciently exploits the thousands of available threads on the GPU, obtaining a considerable reduction in execution time as compared to the OpenMP CPU implementation.
关键词： Remote sensing,Attribute pro?les,GPU,Real-time,Hyperspectral,Supervised classi?cation
更新于2025-09-23 15:22:29
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Wheat Height Estimation Using LiDAR in Comparison to Ultrasonic Sensor and UAS
摘要： As one of the key crop traits, plant height is traditionally evaluated manually, which can be slow, laborious and prone to error. Rapid development of remote and proximal sensing technologies in recent years allows plant height to be estimated in more objective and efficient fashions, while research regarding direct comparisons between different height measurement methods seems to be lagging. In this study, a ground-based multi-sensor phenotyping system equipped with ultrasonic sensors and light detection and ranging (LiDAR) was developed. Canopy heights of 100 wheat plots were estimated five times during a season by the ground phenotyping system and an unmanned aircraft system (UAS), and the results were compared to manual measurements. Overall, LiDAR provided the best results, with a root-mean-square error (RMSE) of 0.05 m and an R2 of 0.97. UAS obtained reasonable results with an RMSE of 0.09 m and an R2 of 0.91. Ultrasonic sensors did not perform well due to our static measurement style. In conclusion, we suggest LiDAR and UAS are reliable alternative methods for wheat height evaluation.
关键词： remote sensing,plant breeding,crop,proximal sensing,phenotyping
更新于2025-09-23 15:21:21
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Feature-Level Fusion of Landsat 8 Data and SAR Texture Images for Urban Land Cover Classification
摘要： Each of the urban land cover types has unique thermal pattern. Therefore, thermal remote sensing can be used over urban areas for indicating temperature differences and comparing the relationships between urban surface temperatures and land cover types. On the other hand, synthetic-aperture radar (SAR) sensors are playing an increasingly important role in land cover classi?cation due to their ability to operate day and night through cloud cover, and capturing the structure and dielectric properties of the earth surface materials. In this research, a feature-level fusion of SAR image and all bands (optical and thermal) of Landsat 8 data is proposed in order to modify the accuracy of urban land cover classi?cation. In the proposed object-based image analysis algorithm, segmented regions of both Landsat 8 and SAR images are utilized for performing knowledge-based classi?cation based on the land surface temperatures, spectral relationships between thermal and optical bands, and SAR texture features measured in the gray-level co-occurrence matrix space. The evaluated results showed the improvements of about 2.48 and 0.06 for overall accuracy and kappa after performing feature-level fusion on Landsat 8 and SAR data.
关键词： Thermal remote sensing,SAR data,Object-based image analysis,Textural features,Feature-level fusion
更新于2025-09-23 15:21:21
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Multiscale Visual Attention Networks for Object Detection in VHR Remote Sensing Images
摘要： Object detection plays an active role in remote sensing applications. Recently, deep convolutional neural network models have been applied to automatically extract features, generate region proposals, and predict corresponding object class. However, these models face new challenges in VHR remote sensing images due to the orientation and scale variations and the cluttered background. In this letter, we propose an end-to-end multiscale visual attention networks (MS-VANs) method. We use skip-connected encoder–decoder model to extract multiscale features from a full-size image. For feature maps in each scale, we learn a visual attention network, which is followed by a classification branch and a regression branch, so as to highlight the features from object region and suppress the cluttered background. We train the MS-VANs model by a hybrid loss function which is a weighted sum of attention loss, classification loss, and regression loss. Experiments on a combined data set consisting of Dataset for Object Detection in Aerial Images and NWPU VHR-10 show that the proposed method outperforms several state-of-the-art approaches.
关键词： object detection,VHR remote sensing image,visual attention,Multiscale feature
更新于2025-09-23 15:21:21
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Aerosol Retrieval in the Autumn and Winter From the Red and 2.12 μm Bands of MODIS
摘要： In the autumn and winter, aerosol is the important atmospheric pollutant over the Beijing–Tianjin–Hebei region. For monitoring aerosol in the autumn and winter, the lack of vegetation and the aging of MODIS sensor are two problems that needed to be solved. In this paper, after analyzing the characteristics of aerosol radiance in the red and shortwave infrared (2.12 μm) bands of MODIS, we develop a new algorithm for terrestrial aerosol with the assumption that the re?ectance ratio between the red and 2.12 μm bands is invariant. With MODIS data over the Beijing–Tianjin–Hebei region from September 2016 to February 2017, the algorithm is applied to aerosol retrieval. The retrieved aerosol optical depth images show that our algorithm can retrieve aerosol over sparse vegetation, and the validation with the AERONET/PHOTONS Beijing site shows that the correlation is greater than 0.9% and 77% of the retrievals fall within the expected error. An error analysis shows that a 2% error in the proportion of the soot component can lead to 15% retrieval error, and over more than 60% of the surface area, the error from the changes in the ratio between the red and 2.12 μm bands can lead to retrieved errors less than 0.1.
关键词： infrared measurements,remote sensing,Aerosol,atmospheric measurements
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 - Multi-Attribute Super-Tensor Model for Remote Sensing Image Classification with High Spatial Resolution
摘要： With the development of remote sensors, it is much easier to acquire large amount of remote sensing images (RSIs) with very high spatial resolution, which has made the spatial characteristics play an important role in classification task. Many work of spatial-spectral classification have been done and achieved good results, especially superpixel-based methods. However, these methods didn’t take each superpixel as an entirety, which had ignored the relationship between spatial and spectral signature. It is well known that RSI can be treated as a third-order data cube, thus it can also be represented by a third-order tensor. This paper proposed a Multi-Attribute Superpixel Tensor (MAST) model to address the aforementioned problem. Experiments conducted on two real RSIs and compared with several well-known methods demonstrate the effectiveness of the proposed model.
关键词： remote sensing images,superpixel,spatial-spectral classification,EMAP,tensor
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 - Evaluation of the Vegetation Optical Depth Index on Monitoring Fire Risk in the Mediterranean Region
摘要： Monitoring live fuel moisture content (LFMC) in Mediterranean area is of great importance for fire risk assessment. LFMC has extensively been estimated based on optical remote sensing data. But the latter can be affected by atmospheric effects. As a complementary data source, microwave data can be used as they are relatively insensitive to atmospheric effects. Yet further evaluations are needed to investigate the potential of microwave observations to monitor LFMC. In this study, we assess the capability of long-term microwave vegetation optical depth (VOD) to capture the temporal variability of in situ measured LFMC in 14 Mediterranean shrub species in southern France during 1996-2014. Microwave-derived VOD at X band (VODX-15) displayed a high sensitivity to LFMC with correlation coefficients of 0.56. Similar evaluations were made using four optical indices computed from the Moderate Resolution Imaging Spectrometer (MODIS) data including normalized difference vegetation index (NDVI), soil adjusted vegetation index (SAVI), visible atmospheric resistant index (VARI), normalized difference water index (NDWI). The comparisons showed that VARI performs better than VODX-15 and other optical indices with highest median of correlation coefficients of 0.65. Overall, this study shows that passive microwave-derived VOD, are efficient proxies for LFMC of Mediterranean shrub species and could be used along with optical indices to evaluate fire risks in the Mediterranean region.
关键词： vegetation optical depth,fire risk,microwave remote sensing,live fuel moisture content
更新于2025-09-23 15:21:21
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A High-Resolution 220-GHz Ultra-Wideband Fully Integrated ISAR Imaging System
摘要： In this paper, an ultra-wideband fully integrated imaging radar at sub-terahertz (sub-THz) frequencies is presented, which demonstrates a fine lateral resolution without using any focal lens/mirror. We have achieved a lateral resolution of 2 mm for an object at 23-cm distance as well as a range resolution of 2.7 mm. To achieve the decent range resolution, in a frequency modulation continuous wave radar configuration, a state-of-the-art chirp bandwidth (BW) of 62.4 GHz at a center frequency of 221.1 GHz is generated and efficiently radiated. We have presented a design technique for the optimal design of the passive embedding around the core transistor to maximize the tuning BW of the voltage controlled oscillator. At the receiver side, to maximize the intermediate frequency level, a subharmonic mixer is utilized, which is designed for the lowest conversion loss. Finally, to obtain the fine lateral resolution, we have implemented near-field beamforming algorithm based on the inverse synthetic aperture radar (ISAR) systems. The synthesized beamwidth is less than 0.5°; hence, high-resolution images are reconstructed. The system is fabricated in a 55-nm BiCMOS process. To the best of our knowledge, this is the first imaging radar at THz/sub-THz frequencies, which utilizes ISAR to achieve a high lateral resolution while the radar system is fully integrated.
关键词： ultra-wideband,Frequency modulation continuous wave (FMCW),remote sensing,THz,radar,sub-terahertz (sub-THz),oscillator,inverse synthetic aperture radar (ISAR),plane wave
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 - Cnn Based Renormalization Method for Ship Detection in Vhr Remote Sensing Images
摘要： Ship detection with very high resolution (VHR) remote sensing image has recently been an attractive topic due to rapid development of deep learning. Current researches on ship detection are generally confronted with a big challenge that existing methods failed to get high quality of object proposal with good intersection-over-union (IOU) before detection. In this paper, a Convolutional Neural Network (CNN) based renormalization method is proposed to improve the quality of object proposal. First, CNN is used to predict shape information of candidate ships’ which are involved with rotation, location and scale in patches. Then, a renormalization net is designed to adjust the candidate ships in patches by correcting the shape information and renormalizing it to uniform patch. In this way, good candidate objects in patches could be generated and will be helpful with improving following ship detection. The proposed renormalization net was tested on a Google-Earth handcraft dataset. The experimental result demonstrates the proposed renormalization net greatly improve the ship detection with both of good detection accuracy and high IOU.
关键词： Ship detection,CNN,renormalization,remote sensing
更新于2025-09-23 15:21:21
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Uncertainty budgets of major ozone absorption cross sections used in UV remote sensing applications
摘要： Detailed uncertainty budgets of three major ultraviolet (UV) ozone absorption cross-section datasets that are used in remote sensing application are provided and discussed. The datasets are Bass–Paur (BP), Brion–Daumont–Malicet (BDM), and the more recent Serdyuchenko–Gorshelev (SG). For most remote sensing application the temperature dependence of the Huggins ozone band is described by a quadratic polynomial in temperature (Bass–Paur parameterization) by applying a regression to the cross-section data measured at selected atmospherically relevant temperatures. For traceability of atmospheric ozone measurements, uncertainties from the laboratory measurements as well as from the temperature parameterization of the ozone cross-section data are needed as input for detailed uncertainty calculation of atmospheric ozone measurements. In this paper the uncertainty budgets of the three major ozone cross-section datasets are summarized from the original literature. The quadratic temperature dependence of the cross-section datasets is investigated. Combined uncertainty budgets is provided for all datasets based upon Monte Carlo simulation that includes uncertainties from the laboratory measurements as well as uncertainties from the temperature parameterization. Between 300 and 330 nm both BDM and SG have an overall uncertainty of 1.5 %, while BP has a somewhat larger uncertainty of 2.1 %. At temperatures below about 215 K, uncertainties in the BDM data increase more strongly than the others due to the lack of very low temperature laboratory measurements (lowest temperature of BDM available is 218 K).
关键词： uncertainty budgets,Monte Carlo simulation,temperature dependence,UV remote sensing,ozone absorption cross sections
更新于2025-09-23 15:21:21