LEADER 00872nam0-22002891i-450- 001 990003851700403321 005 20080111103215.0 035 $a000385170 035 $aFED01000385170 035 $a(Aleph)000385170FED01 035 $a000385170 100 $a20030910d--------km-y0itay50------ba 101 0 $aita 102 $aIT 200 1 $aSaggi su l'accumulazione di capitale nei modelli di equilibrio generale$fEnrico Zaghini. 210 $aRoma$cEdizioni dell'Ateneo$d1967. 215 $a113 p.$d24 cm 610 0 $aEquilibrio economico$aModelli matematici 676 $aF/4 700 1$aZaghini,$bEnrico$0121081 801 0$aIT$bUNINA$gRICA$2UNIMARC 901 $aBK 912 $a990003851700403321 952 $aF/4 ZAG$b042232$fSES 959 $aSES 996 $aSaggi su l'accumulazione di capitale nei modelli di equilibrio generale$9515456 997 $aUNINA LEADER 06768nam 2201837z- 450 001 9910585941603321 005 20220812 035 $a(CKB)5600000000483066 035 $a(oapen)https://directory.doabooks.org/handle/20.500.12854/91137 035 $a(oapen)doab91137 035 $a(EXLCZ)995600000000483066 100 $a20202208d2022 |y 0 101 0 $aeng 135 $aurmn|---annan 181 $ctxt$2rdacontent 182 $cc$2rdamedia 183 $acr$2rdacarrier 200 00$aHyperspectral Imaging and Applications 210 $aBasel$cMDPI - Multidisciplinary Digital Publishing Institute$d2022 215 $a1 online resource (632 p.) 311 08$a3-03921-522-1 311 08$a3-03921-523-X 330 $aDue to advent of sensor technology, hyperspectral imaging has become an emerging technology in remote sensing. Many problems, which cannot be resolved by multispectral imaging, can now be solved by hyperspectral imaging. The aim of this Special Issue "Hyperspectral Imaging and Applications" is to publish new ideas and technologies to facilitate the utility of hyperspectral imaging in data exploitation and to further explore its potential in different applications. This Special Issue has accepted and published 25 papers in various areas, which can be organized into 7 categories with the number of papers published in every category included in its open parenthesis. 1. Data Unmixing (2 papers)2. Spectral variability (2 papers)3. Target Detection (3 papers)4. Hyperspectral Image Classification (6 papers)5. Band Selection (2 papers)6. Data Fusion (2 papers)7. Applications (8 papers) Under every category each paper is briefly summarized by a short description so that readers can quickly grab its content to find what they are interested in. 606 $aHistory of engineering & technology$2bicssc 606 $aTechnology: general issues$2bicssc 610 $a90° yaw imaging 610 $aadaptive window 610 $aAfrica 610 $aagroforestry 610 $aAHS 610 $aairborne laser scanning 610 $aalgebraic multigrid methods 610 $aanomaly detection 610 $aAVIRIS 610 $aband expansion process (BEP) 610 $aband grouping 610 $aband selection 610 $aband selection (BS) 610 $aband subset selection (BSS) 610 $abiodiversity 610 $aclass imbalance 610 $aclassification 610 $acomposite kernel 610 $aconstrained energy minimization 610 $aconstrained energy minimization (CEM) 610 $acorrelation band expansion process (CBEP) 610 $adata fusion 610 $adata integration 610 $adata unmixing 610 $adata-guided constraints 610 $adeep belief networks 610 $adeep learning 610 $adeep pipelined background statistics 610 $aDunhuang site 610 $aendmember extraction 610 $aensemble learning 610 $aevenness 610 $afire severity 610 $aGram-Schmidt orthogonalization 610 $agraph 610 $ahashing ensemble 610 $ahierarchical feature 610 $ahigh-level synthesis 610 $aHyMap 610 $ahyperspectral 610 $ahyperspectral classification 610 $ahyperspectral compression 610 $ahyperspectral detection 610 $ahyperspectral image 610 $ahyperspectral image (HSI) 610 $ahyperspectral image classification 610 $ahyperspectral imagery 610 $ahyperspectral images (HSIs) 610 $ahyperspectral imaging 610 $ahyperspectral pansharpening 610 $ahyperspectral unmixing 610 $aimage enhancement 610 $aimage fusion 610 $aimaging spectroscopy 610 $ain situ measurements 610 $aintrinsic image decomposition 610 $airradiance-based method 610 $aiterative algorithm 610 $aiterative CEM (ICEM) 610 $aKSVD 610 $alabel propagation 610 $alinearly constrained minimum variance (LCMV) 610 $alocal abundance 610 $alocal summation RX detector (LS-RXD) 610 $alossy compression 610 $amachine learning 610 $amineral mapping 610 $aminimum noise fraction 610 $amultiscale spatial information 610 $amultiscale union regions adaptive sparse representation (MURASR) 610 $anonlinear band expansion (NBE) 610 $anonnegative matrix factorization 610 $anuclear norm 610 $aon-board compression 610 $aoptical spectral region 610 $aorthogonal projections 610 $aOtsu's method 610 $apanchromatic 610 $apanchromatic image 610 $aparallel processing 610 $apeatland 610 $aprogressive sample processing (PSP) 610 $aprototype space 610 $araw material 610 $areal-time processing 610 $arecursive anomaly detection 610 $areflectance-based method 610 $aremote sensing 610 $arolling guidance filtering (RGF) 610 $arotation forest 610 $asemi-supervised learning 610 $asemi-supervised local discriminant analysis 610 $asequential LCMV-BSS (SQ LCMV-BSS) 610 $asliding window 610 $asparse coding 610 $asparse unmixing 610 $asparseness 610 $aspectral mixture analysis 610 $aspectral variability 610 $aspectral-spatial classification 610 $asprout detection 610 $astructure tensor 610 $asuccessive LCMV-BSS (SC LCMV-BSS) 610 $asuperpixel 610 $aSVM 610 $atarget detection 610 $aterrestrial hyperspectral imaging 610 $atexture feature enhancement 610 $athermal infrared spectral region 610 $atree species 610 $atree-based ensemble 610 $avegetation type 610 $avicarious calibration 610 $avineyard 610 $awater stress 610 $aweighted fusion 610 $aweighted least squares filter 615 7$aHistory of engineering & technology 615 7$aTechnology: general issues 700 $aChang$b Chein-I$4edt$0763028 702 $aSong$b Meiping$4edt 702 $aZhang$b Junping$4edt 702 $aWu$b Chao-Cheng$4edt 702 $aChang$b Chein-I$4oth 702 $aSong$b Meiping$4oth 702 $aZhang$b Junping$4oth 702 $aWu$b Chao-Cheng$4oth 906 $aBOOK 912 $a9910585941603321 996 $aHyperspectral Imaging and Applications$93035981 997 $aUNINA