Leakage erosion is one of the most harmful driving factors causing river embankment breaches, particularly in flood season. However, manual patrol is the main way to find river embankment leakage ...presently, which badly hinders disaster prevention. To realize the efficient detection and automatic identification of embankment leakage, for the first time the strategy of UAV carried passive infrared thermography combined with transfer learning is introduced herein as an innovative approach to ensure embankment safety. Especially, the problem of embankment leakage identification is transformed into image classification. The main research objects in this study are slope leakage and piping, two of the most dangerous causes of embankment failure. To obtain sufficient images for model training, an open-air simulation platform which can simulate the slope leakage and piping under the actual service conditions of river embankment is established. A total of more than 500 infrared thermography experiments are conducted on the leakage simulation platform and then an infrared image database containing more than 10,000 images which contain various thermal anomaly areas generated by 6 classes of embankment leakage is established. Using these images and AlexNet-based transfer learning method, an image classification model with excellent performance is trained. This model has a classification accuracy of 94.90%, a small leakage missed rate of 0.64%, and a small false alarm rate of 2.65% on the test set. Moreover, before model deployment, visualization techniques such as t-SNE and Grad-CAM are adopted to provide interior insight of the model to ensure that the objects of concern on which the model makes its classification decisions are reasonable. Finally, field tests demonstrated strong feasibility of UAV carried infrared thermography combined with this well-trained model, and revels that the proposed leakage detection and recognition approach has good applicability and generalization.
The real-time monitoring of wheat phenology variations among different varieties and their adaptive responses to environmental conditions is essential for advancing breeding efforts and improving ...cultivation management. Many remote sensing efforts have been made to relieve the challenges of key phenophase detection. However, existing solutions are not accurate enough to discriminate adjacent phenophases with subtle organ changes, and they are not real-time, such as the vegetation index curve-based methods relying on entire growth stage data after the experiment was finished. Furthermore, it is key to improving the efficiency, scalability, and availability of phenological studies. This study proposes a two-stage deep learning framework called PhenoNet for the accurate, efficient, and real-time classification of key wheat phenophases. PhenoNet comprises a lightweight encoder module (PhenoViT) and a long short-term memory (LSTM) module. The performance of PhenoNet was assessed using a well-labeled, multi-variety, and large-volume dataset (WheatPheno). The results show that PhenoNet achieved an overall accuracy (OA) of 0.945, kappa coefficients (Kappa) of 0.928, and F1-score (F1) of 0.941. Additionally, the network parameters (Params), number of operations measured by multiply-adds (MAdds), and graphics processing unit memory required for classification (Memory) were 0.889 million (M), 0.093 Giga times (G), and 8.0 Megabytes (MB), respectively. PhenoNet outperformed eleven state-of-the-art deep learning networks, achieving an average improvement of 3.7% in OA, 5.1% in Kappa, and 4.1% in F1, while reducing average Params, MAdds, and Memory by 78.4%, 85.0%, and 75.1%, respectively. The feature visualization and ablation analysis explained that PhenoNet mainly benefited from using time-series information and lightweight modules. Furthermore, PhenoNet can be effectively transferred across years, achieving a high OA of 0.981 using a two-stage transfer learning strategy. Furthermore, an extensible web platform that integrates WheatPheno and PhenoNet and ensures that the work done in this study is accessible, interoperable, and reusable has been developed (https://phenonet.org/).
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•Proposed the STLCF to modify the unrealistic land cover change.•The STLCF is based on the spatio-temporal transition probability.•The STLCF significantly improved the spatio-temporal ...consistency of annual land cover maps.
Annual analyses of land cover dynamics in urban areas provide a thorough understanding of the urbanization effects on environment and valuable information for the improvement of urban growth modeling. However, most current studies focus on major land cover changes, such as urbanization and vegetation loss. The most feasible way to evaluate the complex interactions among different land cover types is the post-classification change detection, but the temporal inconsistency in the time series of land cover maps impedes the high-frequency and long-term analyses. This study proposed a spatio-temporal land cover filter (STLCF) to remove the illogical land cover change events in the time series of land cover maps, and analyzed the annual land cover dynamics in urban areas. The knowledge of illogical land cover change events was ‘learned’ from the land cover maps through the spatio-temporal transition probability matrix, instead of experts’ knowledge. The illogical change was modified with the land cover of the maximum probability calculated from the naïve Bayesian equation. The STLCF was tested in Wuhan, a typical densely urbanized Chinese city. The annual land cover maps from 2000 to 2013 were derived from multi-date Landsat images using the Decision Tree (DT) classifier. Results showed that the STLCF improved the mean overall accuracy of annual change detection by about 6%. Additionally, the amount of land cover trajectories with unrealistically frequent changes was significantly decreased. During the study period, 7.86% of the pixels experienced one land cover change, and about 0.57% of the pixels experienced land cover changes more than once. The annual analyses demonstrated the non-linear increasing trend in urbanization as well as the corresponding trend in vegetation loss in the study area. We also found the conversion from built-up areas to vegetation near rivers and lakes and in the reserves and rural areas, mainly caused by the restoration of built-up areas to the park or green belt/wedges along rivers and new roads in the metropolitan areas, and to the cropland and woods in the rural areas. Results of this study showed the importance of the spatio-temporal consistency check with knowledge derived from land cover maps of the study area, which facilitates the annual analyses of major and subtle land cover dynamics in urban areas.
UAV BORNE LOW ALTITUDE PHOTOGRAMMETRY SYSTEM
The international archives of the photogrammetry, remote sensing and spatial information sciences/International archives of the photogrammetry, remote sensing and spatial information sciences,
07/2012
Journal Article
In this paper, a novel change detection approach is proposed for multitemporal synthetic aperture radar (SAR) images. The approach is based on two difference images, which are constructed through ...intensity and texture information, respectively. In the extraction of the texture differences, robust principal component analysis technique is used to separate irrelevant and noisy elements from Gabor responses. Then graph cuts are improved by a novel energy function based on multivariate generalized Gaussian model for more accurately fitting. The effectiveness of the proposed method is proved by the experiment results obtained on several real SAR images data sets.
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The interaction between a moving submarine and seawater generates characteristic wakes on the sea surface, enabling indirect detection of undersea objects via airborne remote sensing. ...Here, we demonstrate the feasibility of using visible-light polarization imaging to observe submarine wakes. The key links in the imagine chain are considered separately. These include the polarization patterns of skylight, the elevations and slopes of submarine wakes and sea waves, and the changes in the sea surface polarized bidirectional reflectance characteristics due to modulation of gravity–capillary waves by the wake’s velocity field. A complete model of the airborne optical polarization imaging process is constructed and images are simulated via ray tracing. All theories proposed are verified by a series of terrestrial observation experiments. The results show that both the sea surface roughness modulation by the wake’s velocity field and the sea surface slope formed by wake elevation play significant roles in the imaging process. The wake features in the Stokes vector linear polarization component (Q, U) images are effectively enhanced, and the environmental adverse effect on these images is smaller than that on the intensity images. The degree of linear polarization (DoLP) and angle of polarization (AoP) images exhibit acceptable contrast under certain zenith and azimuth angles. Thus, our analysis confirms that airborne optical polarization imaging has considerable potential for observing wakes and other small- and medium-scale ocean dynamic processes.
Surface water plays a vital role in natural environment and human development. The research of water extraction method using remote sensing image is a hot topic, which has been widely developed in ...water index, classification, subpixel, and other aspects. Compared with other methods, a water-index based method has the advantages of fast speed and convenience. The characteristics of surface water, such as wide coverage and instability, make the water index stand out in monitoring large area of surface water. However, land surface in water environment is complex, and the main factors that reduce water extraction accuracy are also different, such as shadow in urban areas and water leakage in unshaded areas. The current index is bound to weaken the information in the water body when suppressing shadows, and vice versa. To address these issues, contrast difference water index (CDWI) and shadow difference water index (SDWI) are proposed in this paper by improving the modified normalized difference water index (MNDWI). CDWI is used to enhance water information, which is suitable for areas without building shadows. SDWI is used to eliminate the shadow of buildings, which is suitable for urban areas. Moreover, background difference water index (BDWI) was proposed by combining the advantages of CDWI and SDWI through a background regularizer B, which is used to extract surface water under complex background. The regularizer B represents the similarity between local background features of the image and the reference urban area, which is used to locally weight SDWI and CDWI, so that BDWI can automatically enhance the water body in the shadowless area and eliminate the shadow of buildings. The water extraction results of BDWI, MNDWI, the tasseled cap wetness index (TCW), the automatic water extraction index (AWEInsh, AWEIsh), and the water index 2015 (WI2015) were used for comparison. Other methods tend to perform well only in built-up areas or non-built-up areas, while the BDWI can extract surface water under various backgrounds with high accuracy and stability. The overall accuracy produced by the BDWI was 91.58–97.57%, CDWI was 84.85–97.09%, SDWI was 81.63–94.40%, MNDWI was 80.19–95.64%, TCW was 82.33–95.98%, AWEIsh was 87.50–96.37%, AWEInsh was 80.59–98.78%, and WI2015 was 78.24–98.38%. Combining water index with image local information is helpful to improve the accuracy of water extraction in large and complex environment. Finally, surface water in Jiangsu Province, China was extracted through BDWI and the changes in 1985, 2000, and 2015 were analyzed.
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Current filtering methods of airborne LiDAR bathymetry (ALB) point clouds cannot identify negative anomalies or avoid over-filtering of the data. To overcome these problems, we ...propose a bidirectional cloth simulation filtering (BCSF) method and verify it using captured data. First, a transfer iterative trend surface is established to eliminate the negative anomalies and realize the continuous expression of the seafloor topography. The terrain complexities of the seafloor points are calculated using four extracted feature factors: slope, standard deviation of depth, Gaussian curvature, and roughness. We then calculate the sub-regional terrain complexity and the adaptive distance threshold and obtain user-defined parameters. Finally, sub-regional filtering is carried out, and a filtered surface is established to solve the over-filtering problem of convex and concave seafloor topographies based on the BCSF correction model. To evaluate the performance of the proposed method, the BCSF method was applied to ALB data captured around Yuanzhi Island in the South China Sea. The experimental results show that the BCSF method effectively filters out non-seafloor points and fully preserves the seafloor microtopography to realize the integrity of the seafloor topography. The proposed BCSF method outperforms the cloth simulation filtering method in terms of the elimination rate, which decreases from 38.78% to 2.52% and from 29.52% to 0.70% in the whole study area and local study area, respectively. Consequently, the BCSF method that combines forward filtering with inverse filtering exhibits complementary advantages, avoids over-filtering, and demonstrates strong adaptability and robustness for ALB data.
Automated and efficient algorithms to perform segmentation of terrestrial LiDAR data is critical for exploitation of 3D point clouds, where the ultimate goal is CAD modeling of the segmented data. In ...this work, a novel segmentation technique is proposed, starting with octree decomposition to recursively divide the scene into octants or voxels, followed by a novel split and merge framework that uses graph theory and a series of connectivity analyses to intelligently merge components into larger connected components. The connectivity analysis, based on a combination of proximity, orientation, and curvature connectivity criteria, is designed for the segmentation of pipes, vessels, and walls from terrestrial LiDAR data of piping systems at industrial sites, such as oil refineries, chemical plants, and steel mills. The proposed segmentation method is exercised on two terrestrial LiDAR datasets of a steel mill and a chemical plant, demonstrating its ability to correctly reassemble and segregate features of interest.
The level of detail (LOD) concept for 3D building models, which indicates the degree of closeness between a model and its real-world counterpart, is deeply rooted among the stakeholders in the field ...of urban research and 3D geoinformation. However, with the increasing use and demand of a wide range of applications, the LOD definition standardized by the City Geography Markup Language (CityGML) standard appears to be generic, potentially resulting in redundancy and inflexibility. To address this issue, we reconsider the LOD concept from an application point of view and suggest a new context-aware heterogeneous LOD modeling paradigm for 3D building models tailored to specific applications. The new proposal challenges the original homogeneous generic modeling logic and instead adopts a bottom-up approach, putting the focus on the building components rather than on the building itself, resulting in models that may lead to a better fitness for use. In this paper, we first specify a number of discrete LODs for building component models, called CLODs, and then assemble them to derive the LODs of building models suited for particular applications, diminishing redundancy and being tailored for a specific application. To obtain the appropriate LOD specification, we introduce five essential evaluation criteria and a series of semantic and geometrically assembled constraints on the CLODs. We implement two experiments, outdoor component selection and indoor furniture simulation, and conclude that the proposed application-driven LOD definition is more suited in the context of particular applications.
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