- 标题
- 摘要
- 关键词
- 实验方案
- 产品
-
Analysis and design of a hybrid optical fiber refractometer for large dynamic range measurements
摘要： In this paper, we report the outcomes of the 2015 data fusion contest organized by the Image Analysis and Data Fusion Technical Committee (IADF TC) of the IEEE Geoscience and Remote Sensing Society. As for previous years, the IADF TC organized a data fusion contest aiming at fostering new ideas and solutions for multisource studies. The 2015 edition of the contest proposed a multiresolution and multisensorial challenge involving extremely high resolution RGB images (with a ground sample distance of 5 cm) and a 3-D light detection and ranging point cloud (with a point cloud density of approximatively 65 pts/m2 ). The competition was framed in two parallel tracks, considering 2-D and 3-D products, respectively. In this Part B, we report the results obtained by the winners of the 3-D contest, which explored challenging tasks of road extraction and ISO containers identi?cation, respectively. The 2-D part of the contest and a detailed presentation of the dataset are discussed in Part A.
关键词： light detection and ranging (LiDAR),very high resolution (VHR) data,object identi?cation,multiresolution-data fusion,multisource-data fusion,multimodal-data fusion,Image analysis and data fusion (IADF),road detection
更新于2025-09-16 10:30:52
-
Spatial–Spectral Feature Fusion Coupled with Multi-Scale Segmentation Voting Decision for Detecting Land Cover Change with VHR Remote Sensing Images
摘要： In this article, a novel approach for land cover change detection (LCCD) using very high resolution (VHR) remote sensing images based on spatial–spectral feature fusion and multi-scale segmentation voting decision is proposed. Unlike other traditional methods that have used a single feature without post-processing on a raw detection map, the proposed approach uses spatial–spectral features and post-processing strategies to improve detecting accuracies and performance. Our proposed approach involved two stages. First, we explored the spatial features of the VHR remote sensing image to complement the insu?ciency of the spectral feature, and then fused the spatial–spectral features with di?erent strategies. Next, the Manhattan distance between the corresponding spatial–spectral feature vectors of the bi-temporal images was employed to measure the change magnitude between the bi-temporal images and generate a change magnitude image (CMI). Second, the use of the Otsu binary threshold algorithm was proposed to divide the CMI into a binary change detection map (BCDM) and a multi-scale segmentation voting decision algorithm to fuse the initial BCDMs as the ?nal change detection map was proposed. Experiments were carried out on three pairs of bi-temporal remote sensing images with VHR remote sensing images. The results were compared with those of the state-of-the-art methods including four popular contextual-based LCCD methods and three post-processing LCCD methods. Experimental comparisons demonstrated that the proposed approach had an advantage over other state-of-the-art techniques in terms of detection accuracies and performance.
关键词： very high resolution,spatial–spectral features,bi-temporal remote sensing images,land cover change detection,multi-scale segmentation
更新于2025-09-11 14:15:04
-
A Long-Term Historical Aerosol Optical Depth Data Record (1982-2011) Over China From AVHRR
摘要： A long-term historical aerosol optical depth (AOD) data (15–45° N; 75–135° E) with 0.1 spatial resolution has been produced from Advanced Very High Resolution Radiometer (AVHRR) Pathfinder Atmospheres—Extended level-2B data. The spatial distribution pattern shows that high AOD values are found in central and eastern China over the entire period with AODs larger in summer and spring than in autumn and winter. As the high-quality products from AERONET were absent for this period over mainland China, AOD data obtained using the broadband extinction method from solar radiation stations have been used to verify the quality of the AVHRR AOD data set over China. The intercomparison results show that the interannual variation of AOD has been well captured in the variation curve of the AOD monthly mean and the variation trend is also consistent over the whole period. The correlation coefficient of the monthly mean is mostly larger than 0.55, the agreement index is larger than 0.57, and the relative error is less than 21%. Both AVHRR and visibility data sets show high values in regions with rapid economic development. Using Moderate Resolution Imaging Spectroradiometer AOD data as references, it is found that AVHRR AOD from this paper has better accuracy in general than that from Deep Blue (DB) algorithm over China, especially over eastern and southern China, while DB provides more coverage especially over bright surface such as northwest China. This long-term historic AOD data set can be used together with other AOD data sets to study the climate and environmental changes, especially in the 1980s and 1990s.
关键词： Aerosol optical depth (AOD),Advanced Very High Resolution Radiometer (AVHRR),solar radiation,multiple regression method,Moderate Resolution Imaging Spectroradiometer (MODIS)
更新于2025-09-10 09:29:36
-
Attention-Mechanism-Containing Neural Networks for High-Resolution Remote Sensing Image Classification
摘要： A deep neural network is suitable for remote sensing image pixel-wise classi?cation because it effectively extracts features from the raw data. However, remote sensing images with higher spatial resolution exhibit smaller inter-class differences and greater intra-class differences; thus, feature extraction becomes more dif?cult. The attention mechanism, as a method that simulates the manner in which humans comprehend and perceive images, is useful for the quick and accurate acquisition of key features. In this study, we propose a novel neural network that incorporates two kinds of attention mechanisms in its mask and trunk branches; i.e., control gate (soft) and feedback attention mechanisms, respectively, based on the branches’ primary roles. Thus, a deep neural network can be equipped with an attention mechanism to perform pixel-wise classi?cation for very high-resolution remote sensing (VHRRS) images. The control gate attention mechanism in the mask branch is utilized to build pixel-wise masks for feature maps, to assign different priorities to different locations on different channels for feature extraction recalibration, to apply stress to the effective features, and to weaken the in?uence of other pro?tless features. The feedback attention mechanism in the trunk branch allows for the retrieval of high-level semantic features. Hence, additional aids are provided for lower layers to re-weight the focus and to re-update higher-level feature extraction in a target-oriented manner. These two attention mechanisms are fused to form a neural network module. By stacking various modules with different-scale mask branches, the network utilizes different attention-aware features under different local spatial structures. The proposed method is tested on the VHRRS images from the BJ-02, GF-02, Geoeye, and Quickbird satellites, and the in?uence of the network structure and the rationality of the network design are discussed. Compared with other state-of-the-art methods, our proposed method achieves competitive accuracy, thereby proving its effectiveness.
关键词： feedback attention mechanism,very high resolution,internal classi?er,multi-scale,remote sensing,control gate,attention,pixel-wise classi?cation
更新于2025-09-10 09:29:36
-
[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 - Fusion of Lidar, Hyperspectral and RGB Data for Urban Land Use and Land Cover Classification
摘要： In this paper, we present an ensemble-based classification approach for urban land use and land cover classification based on multispectral LiDAR, hyperspectral and very high resolution RGB data. The approach has been evaluated on the dataset provided for the IEEE GRSS 2018 Data Fusion Contest organized by the GRSS IADF technical committee and has been proven to have a high operational performance, being able to distinguish between different grass-, building- and street-types among other classes like water, railways and parking lots as well as other non-typical classes like cars, trains, stadium seats, etc.
关键词： multispectral LiDAR,very high-resolution RGB,hyperspectral imaging,land use classification
更新于2025-09-10 09:29:36
-
A New Method for Region-Based Majority Voting CNNs for Very High Resolution Image Classification
摘要： Conventional geographic object-based image analysis (GEOBIA) land cover classification methods by using very high resolution images are hardly applicable due to their complex ground truth and manually selected features, while convolutional neural networks (CNNs) with many hidden layers provide the possibility of extracting deep features from very high resolution images. Compared with pixel-based CNNs, superpixel-based CNN classification, carrying on the idea of GEOBIA, is more efficient. However, superpixel-based CNNs are still problematic in terms of their processing units and accuracies. Firstly, the limitations of salt and pepper errors and low boundary adherence caused by superpixel segmentation still exist; secondly, this method uses the central point of the superpixel as the classification benchmark in identifying the category of the superpixel, which does not allow classification accuracy to be ensured. To solve such problems, this paper proposes a region-based majority voting CNN which combines the idea of GEOBIA and the deep learning technique. Firstly, training data was manually labeled and trained; secondly, images were segmented under multiresolution and the segmented regions were taken as basic processing units; then, point voters were generated within each segmented region and the perceptive fields of points voters were put into the multi-scale CNN to determine their categories. Eventually, the final category of each region was determined in the region majority voting system. The experiments and analyses indicate the following: 1. region-based majority voting CNNs can fully utilize their exclusive nature to extract abstract deep features from images; 2. compared with the pixel-based CNN and superpixel-based CNN, the region-based majority voting CNN is not only efficient but also capable of keeping better segmentation accuracy and boundary fit; 3. to a certain extent, region-based majority voting CNNs reduce the impact of the scale effect upon large objects; and 4. multi-scales containing small scales are more applicable for very high resolution image classification than the single scale.
关键词： remote sensing,region-based classification,very high resolution image,CNN,GEOBIA
更新于2025-09-09 09:28:46
-
Mathematical Modeling and Accuracy Testing of WorldView-2 Level-1B Stereo Pairs without Ground Control Points
摘要： With very high resolution satellite (VHRS) imagery of 0.5 m, WorldView-2 (WV02) satellite images have been widely used in the field of surveying and mapping. However, for the specific WV02 satellite image geometric orientation model, there is a lack of detailed research and explanation. This paper elaborates the construction process of the WV02 satellite rigorous sensor model (RSM), which considers the velocity aberration, the optical path delay and the atmospheric refraction. We create a new physical inverse model based on a double-iterative method. Through this inverse method, we establish the virtual control grid in the object space to calculate the rational function model (RFM) coefficients. In the RFM coefficient calculation process, we apply the correcting characteristic value method (CCVM) and least squares (LS) method to compare the two experiments’ accuracies. We apply two stereo pairs of WV02 Level 1B products in Qinghai, China to verify the algorithm and test image positioning accuracy. Under the no-control conditions, the monolithic horizontal mean square error (RMSE) of the rational polynomial coefficient (RPC) is 3.8 m. This result is 13.7% higher than the original RPC positioning accuracy provided by commercial vendors. The stereo pair horizontal positioning accuracy of both the physical and RPC models is 5.0 m circular error 90% (CE90). This result is in accordance with the WV02 satellite images nominal positioning accuracy. This paper provides a new method to improve the positioning accuracy of the WV02 satellite image RPC model without GCPs.
关键词： WorldView-2,physical model,rational function model,very high-resolution satellites
更新于2025-09-09 09:28:46
-
Automated detection of individual clove trees for yield quantification in northeastern Madagascar based on multi-spectral satellite data
摘要： There is an increasing demand for clove products, mainly dried buds and essential oil on global markets. Consequently, the importance of clove trees as a provisioning service is increasing at the local level, particularly for smallholders cultivating clove trees as cash crops. Due to limited availability of data on local production, using remote sensing-based methods to quantify today's clove production is of key interest. We estimated the clove bud yield in a study site in northeastern Madagascar by detecting individual clove trees and determining relevant production systems, including pasture and clove, clove plantation and agroforestry systems. We implemented an individual tree detection method based on two machine learning approaches. Specifically, we proposed using a circular Hough transform (CHT) for the automated detection of individual clove trees. Subsequently, we implemented a tree species classification method using a random forests (RF) classifier based on a set of features extracted for relevant trees in the above production systems. Finally, we classified and mapped different production systems. Based on the number of detected clove trees growing in a clove production system, we estimated the production system-dependent clove bud yield. Our results show that 97.9% of all reference clove trees were detected using a CHT. Classifying clove and non-clove trees resulted in a producer accuracy of 70.7% and a user accuracy of 59.2% for clove trees. The classification of the clove production systems resulted in an overall accuracy of 77.9%. By averaging different clove tree yield estimates obtained from the literature, we estimated an average total yield of approximately 575 tons/year for our 25,600 ha study area. With this approach, we demonstrate a first step towards large-scale clove bud yield estimation using remote sensing data and methodologies.
关键词： Random forest,Tree species classification,Very high-resolution satellite image,Pléiades satellite,LULC classification,Single tree detection,Circular Hough transform,Clove bud yield estimation
更新于2025-09-09 09:28:46
-
Impact of the Acquisition Geometry of Very High-Resolution Pléiades Imagery on the Accuracy of Canopy Height Models over Forested Alpine Regions
摘要： This work focuses on the accuracy estimation of canopy height models (CHMs) derived from image matching of Pléiades stereo imagery over forested mountain areas. To determine the height above ground and hence canopy height in forest areas, we use normalised digital surface models (nDSMs), computed as the differences between external high-resolution digital terrain models (DTMs) and digital surface models (DSMs) from Pléiades image matching. With the overall goal of testing the operational feasibility of Pléiades images for forest monitoring over mountain areas, two questions guide this work whose answers can help in identifying the optimal acquisition planning to derive CHMs. Specifically, we want to assess (1) the benefit of using tri-stereo images instead of stereo pairs, and (2) the impact of different viewing angles and topography. To answer the first question, we acquired new Pléiades data over a study site in Canton Ticino (Switzerland), and we compare the accuracies of CHMs from Pléiades tri-stereo and from each stereo pair combination. We perform the investigation on different viewing angles over a study area near Ljubljana (Slovenia), where three stereo pairs were acquired at one-day offsets. We focus the analyses on open stable and on tree covered areas. To evaluate the accuracy of Pléiades CHMs, we use CHMs from aerial image matching and airborne laser scanning as reference for the Ticino and Ljubljana study areas, respectively. For the two study areas, the statistics of the nDSMs in stable areas show median values close to the expected value of zero. The smallest standard deviation based on the median of absolute differences (σMAD) was 0.80 m for the forward-backward image pair in Ticino and 0.29 m in Ljubljana for the stereo images with the smallest absolute across-track angle (?5.3?). The differences between the highest accuracy Pléiades CHMs and their reference CHMs show a median of 0.02 m in Ticino with a σMAD of 1.90 m and in Ljubljana a median of 0.32 m with a σMAD of 3.79 m. The discrepancies between these results are most likely attributed to differences in forest structure, particularly tree height, density, and forest gaps. Furthermore, it should be taken into account that temporal vegetational changes between the Pléiades and reference data acquisitions introduce additional, spurious CHM differences. Overall, for narrow forward–backward angle of convergence (12?) and based on the used software and workflow to generate the nDSMs from Pléiades images, the results show that the differences between tri-stereo and stereo matching are rather small in terms of accuracy and completeness of the CHM/nDSMs. Therefore, a small angle of convergence does not constitute a major limiting factor. More relevant is the impact of a large across-track angle (19?), which considerably reduces the quality of Pléiades CHMs/nDSMs.
关键词： acquisition geometry,canopy height model,forested mountain,very high-resolution Pléiades imagery,accuracy assessment
更新于2025-09-04 15:30:14
-
[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 - Combining Deep and Shallow Neural Networks with Ad Hoc Detectors for the Classification of Complex Multi-Modal Urban Scenes
摘要： This article describes the work?ow of the classi?cation algorithm which ranked at 2nd place in the 2018 GRSS Data Fusion Contest. The objective of the contest was to provide a classi?cation map with 20 classes on a complex urban scenario. The available multi-modal data were acquired from hyperspectral, LiDAR and very high-resolution RGB sensors ?own on the same platform over the city of Houston, TX, USA. The classi?cation was obtained by merging deep convolutional and shallow fully-connected neural networks on a simpli?ed set of classes, complemented by a series of speci?c detectors and adhoc classi?ers.
关键词： hyperspectral,very high-resolution,data fusion,classi?cation,LiDAR
更新于2025-09-04 15:30:14