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Hierarchical joint classification models for multi-resolution, multi-temporal and multi-sensor remote sensing images. Application to natural disasters

Hierarchical joint classification models for multi-resolution, multi-temporal and multi-sensor remote sensing images. Application to natural disasters

7.1 Markov mesh random eld As discussed in Chapter 4 probabilistic causal image models have been thoroughly studied since the early 90's through hierarchical MRFs on quad-trees. These models rely on a causality concept captured by the factorization of the prior distribution in terms of causal transition probabilities. In practice, this structure tends to generate "blocky" eects in the nal classication map. Several techniques have been intro- duced in Section 4.3.2 that could mitigate such undesired eects. However, in these methods, the practical advantages of the tree structure (i.e., causality) are then par- tially (e.g., [Chardin and Pérez, 1999]) or completely lost (e.g., [Kato et al., 1996], because of the spatial interactions introduced in the respective models). In this chap- ter, the quad-tree approach is extended and a novel contextual multi-scale technique is proposed in order to classify multi-resolution remote sensing data that incor- porate spatial contextual information and mitigate possible blocky artifacts while keeping the causality of the hierarchical model. Here, the focus is on the fusion of multi-resolution and spatial-contextual information for the supervised classication of single-date imagery. For this purpose, let us mention another important class of random elds. Markov mesh random elds (MMRFs), or causal Markov Ran- dom Fields that are also known as Unilateral MRFs (UMRFs) were rst recalled in [Abend et al., 1965, Besag, 1972, Pickard, 1980]. As indicated in Chapter 3 , im- ages are usually modeled on a nite rectangular lattice with each site s ∈ S being associated with one or more random variables.
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Agro-hydrology and multi temporal high resolution remote sensing: toward an explicit spatial processes calibration

Agro-hydrology and multi temporal high resolution remote sensing: toward an explicit spatial processes calibration

Abstract. The growing availability of high-resolution satellite image series offers new opportunities in agro- hydrological research and modeling. We investigated the possibilities offered for improving crop-growth dynamic simulation with the distributed agro-hydrological model: topography-based nitrogen transfer and transformation (TNT2). We used a leaf area index (LAI) map series de- rived from 105 Formosat-2 (F2) images covering the period 2006–2010. The TNT2 model (Beaujouan et al., 2002), cal- ibrated against discharge and in-stream nitrate fluxes for the period 1985–2001, was tested on the 2005–2010 data set (cli- mate, land use, agricultural practices, and discharge and ni- trate fluxes at the outlet). Data from the first year (2005) were used to initialize the hydrological model. A priori agricul- tural practices obtained from an extensive field survey, such as seeding date, crop cultivar, and amount of fertilizer, were used as input variables. Continuous values of LAI as a func- tion of cumulative daily temperature were obtained at the crop-field level by fitting a double logistic equation against discrete satellite-derived LAI. Model predictions of LAI dy- namics using the a priori input parameters displayed tempo- ral shifts from those observed LAI profiles that are irregularly
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Agro-hydrology and multi-temporal high-resolution remote sensing: toward an explicit spatial processes calibration

Agro-hydrology and multi-temporal high-resolution remote sensing: toward an explicit spatial processes calibration

Abstract. The growing availability of high-resolution satellite image series offers new opportunities in agro- hydrological research and modeling. We investigated the possibilities offered for improving crop-growth dynamic simulation with the distributed agro-hydrological model: topography-based nitrogen transfer and transformation (TNT2). We used a leaf area index (LAI) map series de- rived from 105 Formosat-2 (F2) images covering the period 2006–2010. The TNT2 model (Beaujouan et al., 2002), cal- ibrated against discharge and in-stream nitrate fluxes for the period 1985–2001, was tested on the 2005–2010 data set (cli- mate, land use, agricultural practices, and discharge and ni- trate fluxes at the outlet). Data from the first year (2005) were used to initialize the hydrological model. A priori agricul- tural practices obtained from an extensive field survey, such as seeding date, crop cultivar, and amount of fertilizer, were used as input variables. Continuous values of LAI as a func- tion of cumulative daily temperature were obtained at the crop-field level by fitting a double logistic equation against discrete satellite-derived LAI. Model predictions of LAI dy- namics using the a priori input parameters displayed tempo- ral shifts from those observed LAI profiles that are irregularly
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Impact of the Via Baltica on the Urbanisation in Lithuania by Multi-level Remote Sensing Analysis

Impact of the Via Baltica on the Urbanisation in Lithuania by Multi-level Remote Sensing Analysis

2.2. Recognition and extraction of urban areas Landsat 5 TM /7 ETM+ images Fig. 1. Chain of image processing with multi-spectral Landsat series data (Gadal 2012) The generation of the urban land covers’ change modelling with Landsat data series is based on the merging of two methodological image processing approaches based on different classifications methods: maximum likelihood classifier for the automatic image processing and the interactive supervised classifier for the supervised classification. The integration of all the spectral bands for generating land cover maps with the maximum likelihood classifier takes all the biophysical characteristics of the landscape, improving the accuracy of the recognition of objects (buildings, roads, forests, etc.) and generation of maps. The urban objects are characterised by a large panel of spectral resolution because of the different urban landscape materials. Land cover maps created are reintroduced in the image processing chain for increasing the level of detection and decreasing the error rate of miss- classification. The accuracy of the recognition of urban areas is improved on the land cover maps created with maximum likelihood classifier by the application of oriented object approach based on the visual interactive recognition of urban objects: digitalisation of the recognised urban areas, integration of the detected objects in the classifier for the detection, extraction and automatic mapping of the urban areas recognised.
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Riga-Klaipeda Road Corridor Impacts on the Urbanization and Spatial Structures by Multi-Level Sensors Remote Sensing

Riga-Klaipeda Road Corridor Impacts on the Urbanization and Spatial Structures by Multi-Level Sensors Remote Sensing

10 2.3. Image remote sensing acquisition 2. 3. 1. DMSP series The interest of the DMSP data is to cover all the region of interest (Lithuanian and Latvian territories) at the regional level; but also, by the level of data acquisition regularities, to analyze and modeling the urban change and the urban growth during the last 12 years. The spatial resolution of 1km² permits to recognize major urban areas of the Baltic region. The detection of the urban areas is made by the spectral measure in the visible near infrared sensor by night.
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Remote Sensing of Mountain Permafrost Landscape by Multi-Fusion Data Modeling. Example of Verkhoyansk Ridge (Russia)

Remote Sensing of Mountain Permafrost Landscape by Multi-Fusion Data Modeling. Example of Verkhoyansk Ridge (Russia)

2.2.2. Landform classification by ASTER GDEM Compilation of ASTER GDEM data scenes (30m resolution) generates the mesorelief types obtained by the automatic landform classification using Jenness algorithm [14] based on Topographic Position Index (TPI). The TPI algorithm compares the values of each cell inside the DEM with the average of a specific neighborhood around the cell. Landform classification made using TPI is performed with 5x5 window neighborhood analysis at 250m (Fig.2 a) and at 500m (Fig. 2 b) radius (small and large scales) (Fig. 2, a, b) calculating slope degrees and altitudes. Five types of landform related to mesorelief characteristics are recognized (Table 1).
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Denoising Very High Resolution Optical Remote Sensing Images: Application and Optimization of Non Local Bayes method

Denoising Very High Resolution Optical Remote Sensing Images: Application and Optimization of Non Local Bayes method

2) Architectures: Two architectures are used: (A) desktop platform with Intel i7-6700 HQ (2.6-3.5GHz), 8 GB RAM and Ubuntu 16.04 / Gdal 2.1.1 / Fftw 3.3.4 / GCC 4.8.2; (B) High Performance Computing (HPC) platform based on Lenovo NX360m5 with Intel Xeon E5-2650 v4 (2.2-2.9GHz), 128 GB RAM and CentOS 7.2 / Gdal 2.1.1 / Fftw 3.3.4 / GCC 4.8.2 and managed with PBS pro v13. Architecture (A) is only shown for comparison purposes with literature results since HPC (B) is specific to massive RSI production. We have restricted these two architectures to a single-core execution in order to evaluate the computation complexity and not the scalability. Note that NLB is multi-thread ready, the original image can be easily split before denoising due to NLB local search of similar patches.
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Comparison between Thermal Airborne Remote Sensing, Multi-depth Electrical Resistivity Profiling and Soil Mapping

Comparison between Thermal Airborne Remote Sensing, Multi-depth Electrical Resistivity Profiling and Soil Mapping

Introduction Soil physical characteristics, soil thickness, pebble density and their spatial variability are among the main parameters determining the agronomic potentiality of cultivated fields that need to be recorded at high resolution, metric or decametric, using non-destructive methods. To that aim a wide panel of remote sensing and ground based methods has been developed: remote sensing enable the estimation of soil parameters over large areas but are often limited to the estimation of the soil surface parameters, while ground based methods enable to derive soil parameters over the whole soil thickness, but they hardly can be run on large areas. The present study focuses on the assessment of one of them: the thermal airborne remote sensing that can both investigate over the whole soil thickness, and cover large areas. In one case example located in the Beauce agricultural region the surface temperature variations are compared with a series of reference data stored in a GIS: observations on soil thickness and soil content in gravel and stones achieved by auger drilling that allowed identifying haplic calcisols and calcaric cambisols, and electrical resistivity mapped over three different depths of investigation.
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Remote Sensing and Cropping Practices: A Review

Remote Sensing and Cropping Practices: A Review

The local scale is the most documented, as methods can be tested in a more controlled environment with less variability in irrigation practices, with using a considerable amount of ground data and expertise, and uses costly high or very high-resolution imagery without a necessary operational objective. Historically, the first methods principally used photointerpretation, with Landsat 1 imagery alone [ 98 , 99 ], or combined with field boundary information [ 100 ]. These methods are maybe the most accurate, but are the least efficient in terms of time and human resources. Consequently, automated classification approaches were developed, including unsupervised classification [ 101 ], single or multi-stage supervised classification [ 102 ], decision tree [ 103 ], and supervised learning models such as Random Forest or Support Vector Machines [ 104 , 105 ]. Furthermore, Li et al. [ 106 ] showed that the object-based image analysis (OBIA) approach applied to Landsat, which allowed the use of information about the geometry and topology of the fields, was useful for discriminating irrigated fields. All these methods exploited the spectral difference, in terms of reflectance, spectral indices, or other derived features, between irrigated areas and other land cover types. Among the spectral indices, the NDVI was commonly used because of its differential spectral response in presence of either irrigated or rainfed crops. The analysis of multiple satellite acquisitions over a growing season was shown to be more efficient, as it reflects the differences in phenological evolution between crops [ 107 ]. However, when a peak was observed within a known given small time period in an irrigation season, one image acquired at the right time may suffice to identify irrigated areas [ 92 ]. In tropical regions where optical imagery is affected by cloud coverage, studies are based on the use of SAR data. For example, Choudhury et al. [ 108 ] used Radarsat-1 time series to discriminate rice crop water regimes (shallow, intermediate and deep water rice) with very high accuracy (98.8%) based on the sensitivity of SAR backscatter to crop geometry and water combined with a knowledge-based classifier.
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Multilevel fusion for classification of very high resolution remote sensing images

Multilevel fusion for classification of very high resolution remote sensing images

Figure 1.10: The region-based fusion 1.13 Conclusion Traditionally, remote sensing of environment has depended highly on the vegetation indices driven by multi-spectral data from remote sensing satellites. However, all the vegetation indices designed for different applications remain susceptible to various climatic conditions and they remain vulnerable even to the change of data source, i.e. the sensors. the remote sensing experts often regard Multi-spectral data on a pixel as insufficient for land-cover classification, and emphasise on using the spatial context in which some spectral pixel value occurs; in other words, the texture. On the other hand, the RS satellites now provide the RS imagery that is both, affordable and high- resolution, making the identification of land-covers from remote sensing images easier than ever before. In the high-resolution imagery, every region covering a single land-cover type now comprises several image pixels, providing fine details and thereby making it possible to use texture information for identification of the land-covers apposed to the use of spectral information alone in the past. Therefore, the next chapter is dedicated to texture feature extraction and comparison of different texture features.
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Automatic Vehicle Trajectory Extraction by Aerial Remote Sensing

Automatic Vehicle Trajectory Extraction by Aerial Remote Sensing

3. System Configuration In the current study, a Cessna T210L Centurion II aircraft, with a gyro-stabilizing platform GSM3000 assuring the support of a Digicam-H/39 camera was used in the image collection. The choice of such method (instead of static observation or more advanced aircrafts) relied on its ability to collect partial trajectories over the entire length of the pilot study area and fulfil existing financial limitations. The Digicam, with a RGB sensor of 7216x5412 pixels and a 80mm Hasselblad lens allowed for a very high resolution image collection and was directly connected to a high precision positioning system through differential GPS for flight data collection. Photos were collected at an average rate of 0.5Hz, triggered by the fixed maximum image overlapping rate of 90%. The focal distance, shutter speed and aperture were fixed during the entire flight over the study site, the A44 motorway, a 5km urban motorway in the southern region of Porto, Portugal.
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Remote Sensing of Industrial Palm Groves in Cameroon

Remote Sensing of Industrial Palm Groves in Cameroon

Chemura, A, van Duren, I, van Leeuwen, L. M 2015, ‘Determination of the age of oil palm from crown projection area detected from WorldView-2 multispectral remote sensing data: The case of Ejisu-Juaben district, Ghana’., ISPRS Journal of Photogrammetry and Remote Sensing, n°100, PP.118-127. Chitroub , S 2007, ‘Annalyse des composantes indépendantes d'images multibandes: Faisabilité et perspectives’, Revue de télédétection, Vol.7, n°1-2,pp.3-4.

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Conditioning Stochastic Rainfall Replicates on Remote Sensing Data

Conditioning Stochastic Rainfall Replicates on Remote Sensing Data

Methods for simulating rainfall divide naturally into physics- based meteorological models and stochastic models. Physics- based models generate rainfall replicates by perturbing the initial and/or boundary conditions in primitive equations based on mass, momentum, and energy conservation [5]. The compu- tational demands of this approach make it impractical for most ensemble applications. The alternative is to use a stochastic model that reproduces the observed space-time structure of rainfall without simulating the physical processes responsible for this structure. Typical examples of the stochastic approach include multifractal models and scaling laws [6], [7], multi- plicative cascade models [8]–[10] which belong to a broader class of multiscale tree models [11], clustered point processes [12], and wavelet models [13]. Most stochastic models are unable to limit rainfall to specified spatial supports. Such a capability is required if rainfall data are to be conditioned on remote sensing data.
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Remote sensing and air quality in urban areas

Remote sensing and air quality in urban areas

18. SIFAKIS, N., SOULAKELLIS, N.A., PARONIS, D.K.: Quantitative Mapping of Air Pollution Density Using Earth Observations : a New Processing Method and Application to an Urban Area, Int. J. Remote Sensing, 19, 17, (1998) 3289-3300. 19. RETALIS, A., CARTALIS, C., ATHANASSIOU E.: Assessment of the distribution of aerosols in the area of Athens with the use of Landsat Thematic Mapper data, Int. J. Remote Sensing, 20, 5, (1999) 939-945.

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Optimal Transport for Data Fusion in Remote Sensing

Optimal Transport for Data Fusion in Remote Sensing

3 MMRS/University of Zurich, Zurich, Switzerland 4 LETG/CNRS, Rennes, France ABSTRACT One of the main objective of data fusion is the integration of several acquisition of the same physical object, in order to build a new consistent representation that embeds all the in- formation from the different modalities. In this paper, we pro- pose the use of optimal transport theory as a powerful mean of establishing correspondences between the modalities. After reviewing important properties and computational aspects, we showcase its application to three remote sensing fusion prob- lems: domain adaptation, time series averaging and change detection in LIDAR data.
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Shoreline detection using optical remote sensing A review

Shoreline detection using optical remote sensing A review

It is obvious to remark that a high number of methods have been developed using Landsat data. This is due to the accessibility of these data, which are available from the USCG web site. In addition, Landsat images cover all the areas of the earth and allow diachronic studies over a long period. Another satellite whose data are also commonly used is SPOT, since it is one of the oldest satellites with a wide coverage. As for high-resolution satellites data, they are used in a fewer number of publications due to the high cost of these products.

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Mobile and remote inertial sensing with atom interferometers

Mobile and remote inertial sensing with atom interferometers

The baseline design consists of a matter wave-laser antenna, where two or more atom interferometers are coupled to a cavity-based optical interferometer through the radiation circulating in the resonator. The optical field stored in the cavity, which has ultra-low phase noise, will be used to coherently split, reflect and recombine the matter waves. The combined atom-laser system will monitor the motion of the cavity and the forces acting on the atoms at the same time, and with a broad frequency resolution. The laser interferometer performs best at frequencies above 10 Hz, while the atom interferometers provide sensitivity at low frequency (10 Hz and below) due to their intrinsic high accuracy. The system will operate in a gravity-gradiometer configuration, with two (or more) atom interferometers residing in the one-arm optical gravitational detector, which is coupled to a highly precise laser link. This allows the variations of optical path between the two ensembles to be measured with extreme precision. These variations can be induced by the space strain due to a passing GW, or by fluctuating gravitational forces. During the measurement, the atoms are in free-fall, hence coupled to environmental vibrations only through gravity. Together with the use of the same laser light to operate the two interferometers, this strongly mitigates the effect of vibrations. The effect of the laser phase noise on the matter wave interferometer can be reduced using (i) a pre-stabilization cavity on the probe light source, (ii) a second interferometer baseline, or (iii) by adopting new interrogation schemes as proposed in ref. [104].
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Optimization techniques for image registration applied to remote sensing

Optimization techniques for image registration applied to remote sensing

In this context, one can rely on LiDAR processing or high resolution pho- togrammetry to extract the pertinent information. Natural processes While natural hazards trigger sudden deformation of the landscape, many slow natural processes progressively alter planets topography. Motion of dunes A dune is a hill of loose sand built by wind or the flow of water. Dunes move, evolve, merge or divide due to eolian forces and the shape of the bed rock [157]. Dunes non-only exist on Earth but also on Venus, Mars and Titan. Using time series of DEMs of dune fields obtained with photogrammetry, researchers have recently been able to demonstrate that Mars is geologically active [155]. Furthermore, follow-up studies were even able to estimate climate cycles from the motion of dune fields [4].
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A Semantic Retrieval System for Remote Sensing Web Platforms

A Semantic Retrieval System for Remote Sensing Web Platforms

Nys, G.-A., Kasprzyk, J.-P., Hallot, P., & Billen, R. 2018. Towards an ontology for the structuring of remote sensing operations shared by different processing chains. ISPRS - International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences, XLII–4, pp. 483–490. https://doi.org/10.5194/isprs-archives-XLII-4-483-2018 Rebele, T., Suchanek, F., Hoffart, J., Biega, J., Kuzey, E., & Weikum, G. 2016. YAGO: A Multilingual Knowledge Base from Wikipedia, Wordnet, and Geonames. In P. Groth, E. Simperl, A. Gray, M. Sabou, M. Krötzsch, F. Lecue, … Y. Gil (Eds.), The Semantic Web – ISWC 2016 (Vol. 9982, pp. 177–185). https://doi.org/10.1007/978-3-319-46547-0_19
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Use of remote sensing imagery for geotraceability in agriculture

Use of remote sensing imagery for geotraceability in agriculture

standards (1 rts & 2 nd pillars…) Minimise risks Minimise risks (Healthy, Safety…) Food claim Food claim (labels, certifications…) Help farmers Help farmers (Farm management…).. Op[r]

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