12916

11814

كارشناسي ارشد

بيابان زدايي

اصفهان: دانشگاه صنعتي اصفهان، دانشكده منابع طبيعي

1396

يازده، 93ص.: مصور، جدول، نمودار

ص.ع. به فارسي و انگليسي

رضا جعفري

حسين بشري، سعيد پورمنافي

1396/07/29

كتابنامه

منابع طبيعي

مهندسي منابع طبيعي

ID11814

4 1 حذف باندهاي نويز دار 35 4 2 تصحيح اتمسفري با استفاده از مدل 55 FLAASH 4 3 تبديل 52 MNF 4 4 شاخص خلوص پيكسل 56 4 5 تعيين كالسهاي طيفي در فضاي n بعدي 66 4 6 اجراي الگوريتم آناليز تركيب طيفي 26 4 7 نمونه برداري 57 4 1 اعتبارسنجي 67 4 1 1 صحت كليو ضريب كاپا 67 4 2 استخراج شاخص هاي گياهي 77 فصل پنجم نتيجهگيري و پيشنهادات 11 5 2 پيشنهادات 31 منابع 51 چكيده انگليسي 22 شش

23 The Potential of Hyperion Hyperspectral Data In Endmember Extraction Case Study Isfahan County Laleh Davoudi l davoudi@na iut ac ir June 7 2017 Department of Natural Resources Isfahan University of Technology Isfahan 84156 83111 IranDegree M Sc Language Persian Reza Jafari reza jafari@cc iut ac irAbstract In recent years hyperspectral sensors have been developed with the ability to image hundreds ofbands and are used in remote sensing Hyperspectral data with the ability of spectral differentiationhas the ability of recognition of arid land plants with low percentage of vegetation Hyperion is ahyperspectral sensor which is a powerful tool for the identification of land coverage and has 240bands on 400 nm to 2500 nm wavelength with the band width of 10 nm Hyperion image is anunprocessed picture which is influenced by atmospheric effects and has noisy bands The aim ofthis study is inspection the potential of hyperspectral data level 1 Hyperion sensor in identifyingendmembers of an entire Hyperion hyperspectral data For this purpose subpixel SMA method isused for spectral differentiation of hyperspectral data and finally identifying endmembers In thisresearch first bad pixels were omitted from hyperspectral image of Hyperion Noisy anduncalibrated bands including bands 1 7 58 76 121 127 129 132 165 181 183 185 187 191 205 207 210 218 223 and 225 242 were omitted from the image and atmospheric correction ofthe image was done using FLAASH model in ENVI The results showed specters of the imagewere improved after the atmospheric correction and absorbed bands of water were identified Afterthe atmospheric correction to extract the endmembers MNF PPI multispectral space and SMAtransformation methods were used Spectral mixture analysis SMA is the partial coverage ofdifferent type of land coverage for example green plant vegetation litter soil and shadow that issimulated in a picture pixel Finally best bands for vegetation index were extracted using bandratio of hyperspectral Hyperion image and NDVI The results of implementation of the mentionedmethods on hyperspectral Hyperion image showed most data exists in the primary MNF imagesand in least data exists in the final images PPI implementation with the threshold of 50000 led toseparation of pure pixels of the image In multi dimensional space pixels were classified and 7endmembers were extracted using SMA and at the end the results were inspected and verified Extracted endmembers of the Hyperion image were compatible with the land data for more than 70percent which indicates the high potential of hyperspectral Hyperion data in the identification ofcoverage of arid land Generally the results show that at the areas with low vegetation coverage the subpixel SMA method is efficient and can differentiate between rangeland vegetation and baresoil The results of the inspection of Hyperion band ratio showed some band ratios are suitablemeans for the assessment of vegetation coverage and can be implemented in areas with lowvegetation coverage successfully Key Words Hyperxpectral Endmember SMA MNF PPI

رضا جعفري

حسين بشري، سعيد پورمنافي