1,103 research outputs found

    Toward reduction of artifacts in fused images

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    Most fusion satellite image methodologies at pixel-level introduce false spatial details, i.e.artifacts, in the resulting fusedimages. In many cases, these artifacts appears because image fusion methods do not consider the differences in roughness or textural characteristics between different land covers. They only consider the digital values associated with single pixels. This effect increases as the spatial resolution image increases. To minimize this problem, we propose a new paradigm based on local measurements of the fractal dimension (FD). Fractal dimension maps (FDMs) are generated for each of the source images (panchromatic and each band of the multi-spectral images) with the box-counting algorithm and by applying a windowing process. The average of source image FDMs, previously indexed between 0 and 1, has been used for discrimination of different land covers present in satellite images. This paradigm has been applied through the fusion methodology based on the discrete wavelet transform (DWT), using the à trous algorithm (WAT). Two different scenes registered by optical sensors on board FORMOSAT-2 and IKONOS satellites were used to study the behaviour of the proposed methodology. The implementation of this approach, using the WAT method, allows adapting the fusion process to the roughness and shape of the regions present in the image to be fused. This improves the quality of the fusedimages and their classification results when compared with the original WAT metho

    Integration of Panchromatic and Multispectral Images by Local Fractal Dimension

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    The fusion image strategies are a good solution to obtain a synthetic image with high spatial and spectral characteristics simultaneously. Some of them are based on the Wavelet Transform, computed by means of the à trous algorithm (AWT). Most of them do not differentiated between spectral bands. In this sense, a new approach that weights differently the spatial information integrated from the high resolution image in each of the fused image spectral bands by the optimization of the trade off between the spatial and spectral quality of the fused images, was proposed. The main problems of this approach are that a unique weighting factor for the whole spectral band is computed, and the need of indices, that separately measure the spectral and spatial quality of the fused images. In this work, a new strategy that tries to avoid the problems above mentioned is introduced. For that, it is proposed to determine a local weighting factor for each panchromatic pixel by means the fractal map, using the box-counting algorithm. Panchromatic and multispectral Quickbird images have been used to show the performances of this new methodology. The local quality of the final fused images has been evaluated by means of local quality maps of Q index. It has been proved that the proposed fusion strategy preserve the high frequency information of the panchromatic image in areas with a high detail, while in homogeneous areas the low frequency information of the multispectral image are conserved

    The Data Big Bang and the Expanding Digital Universe: High-Dimensional, Complex and Massive Data Sets in an Inflationary Epoch

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    Recent and forthcoming advances in instrumentation, and giant new surveys, are creating astronomical data sets that are not amenable to the methods of analysis familiar to astronomers. Traditional methods are often inadequate not merely because of the size in bytes of the data sets, but also because of the complexity of modern data sets. Mathematical limitations of familiar algorithms and techniques in dealing with such data sets create a critical need for new paradigms for the representation, analysis and scientific visualization (as opposed to illustrative visualization) of heterogeneous, multiresolution data across application domains. Some of the problems presented by the new data sets have been addressed by other disciplines such as applied mathematics, statistics and machine learning and have been utilized by other sciences such as space-based geosciences. Unfortunately, valuable results pertaining to these problems are mostly to be found only in publications outside of astronomy. Here we offer brief overviews of a number of concepts, techniques and developments, some "old" and some new. These are generally unknown to most of the astronomical community, but are vital to the analysis and visualization of complex datasets and images. In order for astronomers to take advantage of the richness and complexity of the new era of data, and to be able to identify, adopt, and apply new solutions, the astronomical community needs a certain degree of awareness and understanding of the new concepts. One of the goals of this paper is to help bridge the gap between applied mathematics, artificial intelligence and computer science on the one side and astronomy on the other.Comment: 24 pages, 8 Figures, 1 Table. Accepted for publication: "Advances in Astronomy, special issue "Robotic Astronomy

    A Review of Image Super Resolution using Deep Learning

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    The image processing methods collectively known as super-resolution have proven useful in creating high-quality images from a group of low-resolution photographic images. Single image super resolution (SISR) has been applied in a variety of fields. The paper offers an in-depth analysis of a few current picture super resolution works created in various domains. In order to comprehend the most current developments in the development of Image super resolution systems, these recent publications have been examined with particular emphasis paid to the domain for which these systems have been designed, image enhancement used or not, among other factors. To improve the accuracy of the image super resolution, a different deep learning techniques might be explored. In fact, greater research into the image super resolution in medical imaging is possible to improve the data's suitability for future analysis. In light of this, it can be said that there is a lot of scope for research in the field of medical imaging

    Complex land cover classifications and physical properties retrieval of tropical forests using multi-source remote sensing

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    The work presented in this thesis mainly focuses on two subjects related to the application of remote sensing data: (1) for land cover classification combining optical sensor, texture features generated from spectral information and synthetic aperture radar (SAR) features, and (2) to develop a non-destructive approach for above ground biomass (AGB) and forest attributes estimation employing multi-source remote sensing data (i.e. optical data, SAR backscatter) combined with in-situ data. Information provided by reliable land cover map is useful for management of forest resources to support sustainable forest management, whereas the generation of the non-destructive approach to model forest biophysical properties (e.g. AGB and stem volume) is required to assess the forest resources more efficiently and cost-effective, and coupled with remote sensing data the model can be applied over large forest areas. This work considers study sites over tropical rain forest landscape in Indonesia characterized by different successional stages and complex vegetation structure including tropical peatland forests. The thesis begins with a brief introduction and the state of the art explaining recent trends on monitoring and modeling of forest resources using remote sensing data and approach. The research works on the integration of spectral information and texture features for forest cover mapping is presented subsequently, followed by development of a non-destructive approach for AGB and forest parameters predictions and modeling. Ultimately, this work evaluates the potential of mosaic SAR data for AGB modeling and the fusion of optical and SAR data for peatlands discrimination. The results show that the inclusion of geostatistics texture features improved the classification accuracy of optical Landsat ETM data. Moreover, the fusion of SAR and optical data enhanced the peatlands discrimination over tropical peat swamp forest. For forest stand parameters modeling, neural networks method resulted in lower error estimate than standard multi-linear regression technique, and the combination of non-destructive measurement (i.e. stem number) and remote sensing data improved the model accuracy. The up scaling of stem volume and biomass estimates using Kriging method and bi-temporal ETM image also provide favorable estimate results upon comparison with the land cover map.Die in dieser Dissertation präsentierten Ergebnisse konzentrieren sich hauptsächlich auf zwei Themen mit Bezug zur angewandten Fernerkundung: 1) Der Klassifizierung von Oberflächenbedeckung basierend auf der Verknüpfung von optischen Sensoren, Textureigenschaften erzeugt durch Spektraldaten und Synthetic-Aperture-Radar (SAR) features und 2) die Entwicklung eines nichtdestruktiven Verfahrens zur Bestimmung oberirdischer Biomasse (AGB) und weiterer Waldeigenschaften mittels multi-source Fernerkundungsdaten (optische Daten, SAR Rückstreuung) sowie in-situ Daten. Eine zuverlässige Karte der Landbedeckung dient der Unterstützung von nachhaltigem Waldmanagement, während eine nichtdestruktive Herangehensweise zur Modellierung von biophysikalischen Waldeigenschaften (z.B. AGB und Stammvolumen) für eine effiziente und kostengünstige Beurteilung der Waldressourcen notwendig ist. Durch die Kopplung mit Fernerkundungsdaten kann das Modell auf große Waldflächen übertragen werden. Die vorliegende Arbeit berücksichtigt Untersuchungsgebiete im tropischen Regenwald Indonesiens, welche durch verschiedene Regenerations- und Sukzessionsstadien sowie komplexe Vegetationsstrukturen, inklusive tropischer Torfwälder, gekennzeichnet sind. Am Anfang der Arbeit werden in einer kurzen Einleitung der Stand der Forschung und die neuesten Forschungstrends in der Überwachung und Modellierung von Waldressourcen mithilfe von Fernerkundungsdaten dargestellt. Anschließend werden die Forschungsergebnisse der Kombination von Spektraleigenschaften und Textureigenschaften zur Waldbedeckungskartierung erläutert. Desweiteren folgen Ergebnisse zur Entwicklung eines nichtdestruktiven Ansatzes zur Vorhersage und Modellierung von AGB und Waldeigenschaften, zur Auswertung von Mosaik- SAR Daten für die Modellierung von AGB, sowie zur Fusion optischer mit SAR Daten für die Identifizierung von Torfwäldern. Die Ergebnisse zeigen, dass die Einbeziehung von geostatistischen Textureigenschaften die Genauigkeit der Klassifikation von optischen Landsat ETM Daten gesteigert hat. Desweiteren führte die Fusion von SAR und optischen Daten zu einer Verbesserung der Unterscheidung zwischen Torfwäldern und tropischen Sumpfwäldern. Bei der Modellierung der Waldparameter führte die Neural-Network-Methode zu niedrigeren Fehlerschätzungen als die multiple Regressions. Die Kombination von nichtdestruktiven Messungen (z.B. Stammzahl) und Fernerkundungsdaten führte zu einer Steigerung der Modellgenauigkeit. Die Hochskalierung des Stammvolumens und Schätzungen der Biomasse mithilfe von Kriging und bi-temporalen ETM Daten lieferten positive Schätzergebnisse im Vergleich zur Landbedeckungskarte

    An object-based approach for mapping forest structural types based on low-density LiDAR and multispectral imagery

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    [EN] Mapping forest structure variables provides important information for the estimation of forest biomass, carbon stocks, pasture suitability or for wildfire risk prevention and control. The optimization of the prediction models of these variables requires an adequate stratification of the forest landscape in order to create specific models for each structural type or strata. This paper aims to propose and validate the use of an object-oriented classification methodology based on low-density LiDAR data (0.5 m−2) available at national level, WorldView-2 and Sentinel-2 multispectral imagery to categorize Mediterranean forests in generic structural types. After preprocessing the data sets, the area was segmented using a multiresolution algorithm, features describing 3D vertical structure were extracted from LiDAR data and spectral and texture features from satellite images. Objects were classified after feature selection in the following structural classes: grasslands, shrubs, forest (without shrubs), mixed forest (trees and shrubs) and dense young forest. Four classification algorithms (C4.5 decision trees, random forest, k-nearest neighbour and support vector machine) were evaluated using cross-validation techniques. The results show that the integration of low-density LiDAR and multispectral imagery provide a set of complementary features that improve the results (90.75% overall accuracy), and the object-oriented classification techniques are efficient for stratification of Mediterranean forest areas in structural- and fuel-related categories. Further work will be focused on the creation and validation of a different prediction model adapted to the various strata.This work was supported by the Spanish Ministerio de Economia y Competitividad and FEDER under [grant number CGL2013-46387-C2-1-R]; Fondo de Garantia Juvenil under [contract number PEJ-2014-A-45358].Ruiz Fernández, LÁ.; Recio Recio, JA.; Crespo-Peremarch, P.; Sapena, M. (2018). An object-based approach for mapping forest structural types based on low-density LiDAR and multispectral imagery. Geocarto International. 33(5):443-457. https://doi.org/10.1080/10106049.2016.1265595S44345733
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