LEADER 01574cam a2200301 a 4500 001 991002586189707536 008 121022s20122014it aaa b 001 0 ita d 020 $a9788882432973 035 $ab14198952-39ule_inst 040 $aDip.to Beni Culturali$bita 100 1 $aBella, Tancredi,$d1981-$0480076 245 10$aS. Andrea a Piazza Armerina, priorato dell'Ordine del Santo Sepolcro :$bvicende costruttive, cicli pittorici e spazio liturgico /$cTancredi Bella ; presentaz. di Maria Antonietta Crippa 246 3 $aSant'Andrea a Piazza Armerina, priorato dell'Ordine del Santo Sepolcro 260 $aCaltanissetta :$bEdizioni Lussografica,$c2012 300 $a462 p., [10] c. di tav. :$bill. ;$c24 cm. 490 1 $aRicerche : collana del Centro diocesano per la formazione permanente di Piazza Armerina ;$v6 504 $aContiene bibliografia: pp. [325]-372 610 20$aS. Andrea (Chiesa : Piazza Armerina, Italia) 650 4$aArchitettura sacra$zItalia$zPiazza Armerina 651 4$aPiazza Armerina (Italia)$xArchitettura 710 2 $aCentro diocesano per la formazione permanente di Piazza Armerina 830 0$aRicerche (Centro diocesano per la formazione permanente di Piazza Armerina) ;$v6 907 $a.b14198952$b14-04-15$c15-09-14 912 $a991002586189707536 945 $aLE001 AR II 168 8°$g1$i2001000183692$lle001$og$pE28.00$q-$rl$s- $t0$u0$v0$w0$x0$y.i15660059$z03-03-15 996 $aS. Andrea a Piazza Armerina, priorato dell'Ordine del Santo Sepolcro$9258371 997 $aUNISALENTO 998 $ale001$b15-09-14$cm$da $e-$fita$git $h0$i0 LEADER 01443oam 2200409Ia 450 001 9910699659603321 005 20230902161606.0 035 $a(CKB)5470000002404339 035 $a(OCoLC)554987997 035 $a(EXLCZ)995470000002404339 100 $a20100315d2009 ua 0 101 0 $aeng 135 $aurmn||||||||| 181 $ctxt$2rdacontent 182 $cc$2rdamedia 183 $acr$2rdacarrier 200 10$aMid-Permian Phosphoria Sea in Nevada and the upwelling model$b[electronic resource] /$fby Keith B. Ketner 210 1$aReston, Va. :$cU.S. Dept. of the Interior, U.S. Geological Survey,$d2009. 215 $a1 online resource (v, 21 p). $ccolor illustrations, map 225 1 $aProfessional paper ;$v1764 300 $aTitle from title screen (viewed on March 15, 2010). 320 $aIncludes bibliographical references (pages 17-21). 410 0$aU.S. Geological Survey professional paper ;$v1764. 606 $aGeology, Stratigraphic$yPermian 606 $aFormations (Geology)$zNevada 607 $aPhosphoria Formation 615 0$aGeology, Stratigraphic 615 0$aFormations (Geology) 700 $aKetner$b Keith B.$f1921-2019.$01389779 712 02$aGeological Survey (U.S.) 801 0$bEJB 801 1$bEJB 801 2$bGPO 906 $aBOOK 912 $a9910699659603321 996 $aMid-Permian Phosphoria Sea in Nevada and the upwelling model$93502667 997 $aUNINA LEADER 05581nam 2201381z- 450 001 9910557383103321 005 20220111 035 $a(CKB)5400000000042068 035 $a(oapen)https://directory.doabooks.org/handle/20.500.12854/77042 035 $a(oapen)doab77042 035 $a(EXLCZ)995400000000042068 100 $a20202201d2021 |y 0 101 0 $aeng 135 $aurmn|---annan 181 $ctxt$2rdacontent 182 $cc$2rdamedia 183 $acr$2rdacarrier 200 00$aRemote Sensing Data Compression 210 $aBasel, Switzerland$cMDPI - Multidisciplinary Digital Publishing Institute$d2021 215 $a1 online resource (366 p.) 311 08$a3-0365-2303-0 311 08$a3-0365-2304-9 330 $aA huge amount of data is acquired nowadays by different remote sensing systems installed on satellites, aircrafts, and UAV. The acquired data then have to be transferred to image processing centres, stored and/or delivered to customers. In restricted scenarios, data compression is strongly desired or necessary. A wide diversity of coding methods can be used, depending on the requirements and their priority. In addition, the types and properties of images differ a lot, thus, practical implementation aspects have to be taken into account. The Special Issue paper collection taken as basis of this book touches on all of the aforementioned items to some degree, giving the reader an opportunity to learn about recent developments and research directions in the field of image compression. In particular, lossless and near-lossless compression of multi- and hyperspectral images still remains current, since such images constitute data arrays that are of extremely large size with rich information that can be retrieved from them for various applications. Another important aspect is the impact of lossless compression on image classification and segmentation, where a reasonable compromise between the characteristics of compression and the final tasks of data processing has to be achieved. The problems of data transition from UAV-based acquisition platforms, as well as the use of FPGA and neural networks, have become very important. Finally, attempts to apply compressive sensing approaches in remote sensing image processing with positive outcomes are observed. We hope that readers will find our book useful and interesting 606 $aTechnology: general issues$2bicssc 610 $a3D-CALIC 610 $aCCSDS 610 $aCCSDS 123.0-B-2 610 $acompact data structure 610 $acomplexity 610 $acompressed sensing 610 $acompression impact 610 $acompressive sensing 610 $acomputational complexity 610 $acoupled dictionary 610 $acrop classification 610 $aDACs 610 $adata compression 610 $aEBCOT 610 $aElias codes 610 $aFAPEC 610 $aFPGA 610 $afully convolutional network 610 $aGPU 610 $agraph filterbanks 610 $agraph signal processing 610 $agroup convolution 610 $aHEVC 610 $ahigh bit-depth compression 610 $ahyperspectral 610 $ahyperspectral image 610 $ahyperspectral image coding 610 $ahyperspectral images 610 $ahyperspectral imaging 610 $ahyperspectral scenes 610 $aimage classification 610 $aimage quality 610 $ainteger-to-integer transforms 610 $aintra coding 610 $ainvertible projection 610 $aJPEG 2000 610 $aJPEG2000 610 $ak2-raster 610 $ak2-tree 610 $aLandsat-8 610 $alearned compression 610 $alossless compression 610 $alossy compression 610 $aM-CALIC 610 $amultispectral 610 $amultispectral image compression 610 $amultispectral satellite images 610 $anear-lossless hyperspectral image compression 610 $aneural networks 610 $aon board compression 610 $aon-board compression 610 $aon-board data compression 610 $aon-board processing 610 $aparallel computing 610 $apartitioned extraction 610 $aPCA 610 $aPForDelta 610 $aquadtree 610 $arate-distortion 610 $areal time 610 $areal-time compression 610 $areal-time performance 610 $areal-time transmission 610 $aremote sensing 610 $aremote sensing data compression 610 $aRice codes 610 $asemantic segmentation 610 $aSentinel-2 610 $aSimple16 610 $aSimple9 610 $asingular value 610 $aspectral image 610 $aspectral-spatial feature 610 $asynthetic aperture sonar 610 $atask-driven learning 610 $atensor decomposition 610 $atransform 610 $atransform coding 610 $aUAV 610 $aUAVs 610 $aunderwater sonar imaging 610 $avariational autoencoder 610 $avisual quality metrics 615 7$aTechnology: general issues 700 $aLukin$b Vladimir$4edt$01325419 702 $aVozel$b Benoit$4edt 702 $aSerra-Sagristà$b Joan$4edt 702 $aLukin$b Vladimir$4oth 702 $aVozel$b Benoit$4oth 702 $aSerra-Sagristà$b Joan$4oth 906 $aBOOK 912 $a9910557383103321 996 $aRemote Sensing Data Compression$93036875 997 $aUNINA