| 000 | 05371naaaa2201153uu 4500 | ||
|---|---|---|---|
| 001 | https://directory.doabooks.org/handle/20.500.12854/62022 | ||
| 005 | 20220714175837.0 | ||
| 020 | _abooks978-3-03921-757-1 | ||
| 020 | _a9783039217571 | ||
| 020 | _a9783039217564 | ||
| 024 | 7 |
_a10.3390/books978-3-03921-757-1 _cdoi |
|
| 041 | 0 | _aEnglish | |
| 042 | _adc | ||
| 100 | 1 |
_aMarcello, Javier _4auth _91595791 |
|
| 700 | 1 |
_aEugenio, Francisco _4auth _91595792 |
|
| 245 | 1 | 0 | _aVery High Resolution (VHR) Satellite Imagery: Processing and Applications |
| 260 |
_bMDPI - Multidisciplinary Digital Publishing Institute _c2019 |
||
| 300 | _a1 electronic resource (262 p.) | ||
| 506 | 0 |
_aOpen Access _2star _fUnrestricted online access |
|
| 520 | _aRecently, growing interest in the use of remote sensing imagery has appeared to provide synoptic maps of water quality parameters in coastal and inner water ecosystems;, monitoring of complex land ecosystems for biodiversity conservation; precision agriculture for the management of soils, crops, and pests; urban planning; disaster monitoring, etc. However, for these maps to achieve their full potential, it is important to engage in periodic monitoring and analysis of multi-temporal changes. In this context, very high resolution (VHR) satellite-based optical, infrared, and radar imaging instruments provide reliable information to implement spatially-based conservation actions. Moreover, they enable observations of parameters of our environment at greater broader spatial and finer temporal scales than those allowed through field observation alone. In this sense, recent very high resolution satellite technologies and image processing algorithms present the opportunity to develop quantitative techniques that have the potential to improve upon traditional techniques in terms of cost, mapping fidelity, and objectivity. Typical applications include multi-temporal classification, recognition and tracking of specific patterns, multisensor data fusion, analysis of land/marine ecosystem processes and environment monitoring, etc. This book aims to collect new developments, methodologies, and applications of very high resolution satellite data for remote sensing. The works selected provide to the research community the most recent advances on all aspects of VHR satellite remote sensing. | ||
| 540 |
_aCreative Commons _fhttps://creativecommons.org/licenses/by-nc-nd/4.0/ _2cc _4https://creativecommons.org/licenses/by-nc-nd/4.0/ |
||
| 546 | _aEnglish | ||
| 653 | _avery high-resolution Pléiades imagery | ||
| 653 | _asurface convergence | ||
| 653 | _adata augmentation | ||
| 653 | _aacquisition geometry | ||
| 653 | _aSVM classification | ||
| 653 | _aurban water mapping | ||
| 653 | _abeaver dam analogue | ||
| 653 | _aagriculture parcel segmentation | ||
| 653 | _amorphological building index | ||
| 653 | _aairborne hypespectral imagery | ||
| 653 | _asunglint correction | ||
| 653 | _awater index | ||
| 653 | _aover-segmentation index (OSI) | ||
| 653 | _aHigh-resolution satellite imagery | ||
| 653 | _amulti-resolution segmentation (MRS) | ||
| 653 | _aGaoFen-2 (GF-2) | ||
| 653 | _abenthic mapping | ||
| 653 | _ascene classification | ||
| 653 | _agreenhouse extraction | ||
| 653 | _aedge constraint | ||
| 653 | _aDeformable CNN | ||
| 653 | _abuilt-up areas extraction | ||
| 653 | _aultra-dense connection | ||
| 653 | _aseagrass | ||
| 653 | _abeaver mimicry | ||
| 653 | _aforested mountain | ||
| 653 | _anatural hazards | ||
| 653 | _aremote sensing | ||
| 653 | _adimensionality reduction techniques | ||
| 653 | _aroad extraction | ||
| 653 | _alandslide monitoring | ||
| 653 | _aSlumgullion landslide | ||
| 653 | _asynthetic aperture radar | ||
| 653 | _abuilding detection | ||
| 653 | _aWorldview-2 | ||
| 653 | _asaliency index | ||
| 653 | _aunder-segmentation index (USI) | ||
| 653 | _atexture analysis | ||
| 653 | _afast marching method | ||
| 653 | _avideo satellite | ||
| 653 | _aCNN | ||
| 653 | _acapsule | ||
| 653 | _asuper-resolution | ||
| 653 | _afeature distillation | ||
| 653 | _ashadow detection | ||
| 653 | _aPrimaryCaps | ||
| 653 | _asemiautomatic | ||
| 653 | _acompensation unit | ||
| 653 | _asuperpixels | ||
| 653 | _ariparian | ||
| 653 | _aQuickBird | ||
| 653 | _asubmesoscale | ||
| 653 | _alinear unmixing | ||
| 653 | _aaccuracy assessment | ||
| 653 | _acomposite error index (CEI) | ||
| 653 | _acyanobacteria | ||
| 653 | _alocal feature points | ||
| 653 | _aFaster R-CNN | ||
| 653 | _aoccluded object detection | ||
| 653 | _aerror index of total area (ETA) | ||
| 653 | _alarge displacements | ||
| 653 | _athreshold stability | ||
| 653 | _aremote sensing imagery | ||
| 653 | _awater column correction | ||
| 653 | _acanopy height model | ||
| 653 | _aspiral eddy | ||
| 653 | _asub-pixel offset tracking | ||
| 653 | _aconsensus | ||
| 653 | _astream restoration | ||
| 653 | _awestern Baltic Sea | ||
| 653 | _aWorldview | ||
| 653 | _avery high-resolution image | ||
| 653 | _aCapsNet | ||
| 653 | _aatmospheric correction | ||
| 856 | 4 | 0 |
_awww.oapen.org _uhttps://mdpi.com/books/pdfview/book/1776 _70 _zDOAB: download the publication |
| 856 | 4 | 0 |
_awww.oapen.org _uhttps://directory.doabooks.org/handle/20.500.12854/62022 _70 _zDOAB: description of the publication |
| 999 |
_c2998824 _d2998824 |
||