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Hyperspectral remote sensing image classification is one of the most challenging tasks. In our previous work, motivated by the similarity between the structures of DNA and hyperspectral remote sensing images, a DNA matching mechanism was used to transform the hyperspectral remote sensing image into a DNA cube for classification. However, the above DNA encoding strategy lacks the process of encoding...
Climate change and anthropogenic pressures are expected to reduce global freshwater availability and to exacerbate water crises in the near futures. To manage water crisis, the most common path of the past century has been a “hard-path”, based on centralized structural actions, strongly affecting the environment. This work aims at developing novel adaptive water management control strategies, in a...
High-resolution flood mapping is an essential step in the monitoring and prevention of inundation hazard, both to gain insight into the processes involved in the generation of flooding events, and from the practical point of view of the precise assessment of inundated areas, useful e.g. in the case of post-event recovery and insurance indemnity assessments. Synthetic Aperture Radar (SAR) data present...
This paper presents a methodology aimed at enabling local government personnel and decision makers to easily process satellite-derived precipitation data for the assessment of extreme precipitation hazard and to integrate them with geospatial reference datasets for the production of timely and meaningful flood risk information, considering also the assessment of exposed infrastructure, population...
This paper shows how remote sensing derived information about the human and physical exposure can be used to support disaster alert systems. The human and physical exposures are calculated from LandScan and GHSL datasets, respectively. In this approach, a multiscale alert system relies on Web services that offer information about the amount of population and infrastructure affected by a given hazard...
Soil moisture (SM) is a key variable in describing land surface characteristics. However, most passive microwave sensed soil moisture products are spatially and temporally discontinuous. In this study, a recurrent autoregressive neural network was investigated for its capability to reconstruct time-series soil moisture. The train dataset was collected from the observations of AMSR2 and SMOS, along...
With the perspective of discriminating some regions particularly suited or unsuited to sea surface salinity retrieval studies by satellite data, the horizontal and vertical Sea surface salinity variability for different time scale is calculated in South China Sea area from 2005 to 2014. It gives a variability with less than 0.1psu in most areas in space resolution of 1°×1° for short time scales. But...
The paper researches on the relationship between image quality and the performance of interest point detection. In this paper, we use the image quality metrics and interest point repeatability as the measures of image quality and the performance of interest point detection respectively. Considering the differences of image's scene and quality degradation factor, nine images covering three kinds of...
This paper proposes a method for automated extraction of street trees in a typical urban environment from 3D point cloud data acquired by the mobile laser scanning system. First, the algorithm utilizes the voxel-based method to remove the ground points from the scene. Second, the Euclidean distance clustering is adopted to cluster points into individual objects. The eigenvalues of neighborhood covariance...
This study evaluates the potential of High Resolution Spotlight TerraSAR-X image for forest type discrimination. Emphasis is put on textural analysis accessible with high resolution radar data. Textural attributes are extracted from GLCM matrices, wavelet, and Fourier Transform (i.e. FOTO method). Their contribution for classification is assessed by their performance through the SVM algorithm.
Feature learning algorithms aim to provide a compact and discriminative representation of complex datasets in order to increase the speed and accuracy of clustering or classification. In this paper, we propose a novel interactive feature learning approach which is mainly based on 3D interactive data visualization and Non-negative Matrix Factorization (NMF). Here, the data is visualized in a 3D interface...
Ground-based leaf area and leaf direction measurements are crucial for remote sensing validation of Leaf Area Index (LAI) and Leaf Angle Distribution (LAD) products. The acquisition of field data is a time-consuming and labor-intensive manual operation. Terrestrial LiDAR (light detection and ranging) has potential to characterize and rebuild the three dimensional structure of vegetation. A method...
This paper presents a novel approach for extracting street lighting poles directly from MLS point clouds. The approach includes four stages: 1) elevation filtering to remove ground points, 2) Euclidean distance clustering to cluster points, 3) voxel-based normalized cut (Ncut) segmentation to separate overlapping objects, and 4) statistical analysis of geometric properties to extract 3D street lighting...
Building detection is a challenging issue that requires efficient solutions in many operational contexts. Hierarchical image representation through tree structures is known for its compliance with such requirements for fast methods in remote sensing. In this paper, we address the building detection problem using an underlying hierarchical image model, and rely solely on height information coming from...
This paper presents a novel method for traffic sign detection and visibility evaluation from mobile Light Detection and Ranging (LiDAR) point clouds and the corresponding images. Our algorithm involves two steps. Firstly, a detection algorithm based on high retro-reflectivity of the traffic sign from the MLS point clouds is designed for sign detection in complicated road scenes. To solve the spatial...
With the development of synthetic aperture radar (SAR) technology in recent years, we have to face the huge amount of data. So, the fast image processing technology seems to be really important in the domain of SAR. Based on the situation above, a new method to accelerate SAR image processing is proposed in this paper. The proposed method employs SIMD (Single Instruction Multiple Data) instructions...
Nowadays Spatial Data Infrastructures are the best practice to publish huge amount of spatial data on the Web in an interoperable and distributed way. Nevertheless, this operation requires a significant effort, expertise and motivation to data providers. In this paper, we propose an original approach to support geo-data providers by automating the workflows for publishing geo-data and relative web...
Multipaths are one of the major source of error for positioning applications using Global Navigation Satellite Systems (GNSS). During the last decade, several studies demonstrated the potential of the inversion of Signal to Noise Ratio (SNR) due to multipaths for estimating environmental parameters such as sea level, soil moisture, and snow depth variations, vegetation growth and biomass quantification...
Joint statistical properties of range-resolved sea backscatter at different polarizations are investigated using large Monte Carlo ensembles of numerically generated data. The simulations are based on the first-principles boundary integral equation technique in the two-dimensional (2-D) space and produce noise-free sets of backscatter corresponding to well-defined wind conditions. This study focuses...
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