高光谱遥感物理基础

高光谱遥感物理基础、处理方法与应用,1. 高光谱遥感的概念 2. 高光谱遥感物理基础 3.高光谱成像光谱仪概览 4. 高光谱图象预处理 5. 高光谱遥感处理方法与应用,1. 高光谱遥感的概念,Quantitative measurements of the spectral characteristics of materials using a remote sensing system having greater than 60 spectral bands with a spectral resolution less than 10 nm producing a continuous portion of the light spectrum which defines the chemical composition of the material through its spectral signature.,What is Hyperspectral Sensing?,Ultraspectral,Broadband,Hyperspectral,Multispectral,Spectral Sensing,2. 高光谱遥感的物理基础,电磁波的波粒二象性：电磁波在传播过程中，主要表现为波动性；在与物质相互作用时，主要表现为粒子性波动性：电磁波以波动的形式在空间传播-波动性 粒子性：电磁波由密集的光子微粒组成，电磁辐射实质是光子微粒的有规律的运动。电磁波的粒子性，使得电磁辐射的能量具有统计性,物质的内部状态和电磁能量的关系：,Photons traveling through the Earths atmosphere strike the surface and are either absorbed, transmitted, scattered and/or reflectedVarious materials absorb photons over specific wavelength intervals resulting in absorption features in reflectance spectraThe location and shape of these unique absorption features provide information on the chemical composition of materials,Scientific Principles,E = hf E = E2 E1 = Energy of photon in joules (J). f = Frequency of the photon in hertz. h = Plancks constant = 6.625 1034 joule-seconds Wavelength = c/f = hc/E A light wave that is emitted with a single quantum of energy E = hf is called a “photon”,What is a Photon?,REFLECTED,ABSORBED,TRANSMITTED (AND REFRACTED),EMITTED,SCATTERED,Electromagnetic Energy,Hyperspectral Reflectance Measurements,Hyperspectral Sensing Concept,After Elachi, JPL,Hyperspectral Sensing Concept (Cont.),Courtesy of JPL,USGS,Multispectral Imaging,N-Dimensional Space - For Use in Pattern Analysis,Spectral Signatures - Physical Basis for Response,Data Space Representations,UV,BLUE,RED,NIR,SWIR,MWIR,LWIR,GREEN,What you see is not what you get!,Reflected and Emitted Energy,Human Eye,00101010101010101010100000011111110000000000101000010101000100101000 00101010101010101010010101010101010101001010101010010101010101010101 10101010101010101010101010101010100100101010100101010101010101010100 10101010101010101010010101010101011001011001010101010101010100101001 01010101001010101010011110101010010000000010101010101001101010101010 00101010101011010101010010101010101010101010101010101010101010101011 01010100100101010100101010101010101010101010101010101010101001010101 01010110010110010101010101010101001010010101010100101010101001111010 10100100000000101010101010011010101010100101010101010101001100101011 01010101010010101010101010010101010101010100101010101010101010101010 10101001010101010101010101010010101010101010101010101010101010101010 01010010101010101011110000111101010001010010000100010101000100101000 00111011001010001001111111110000010101010101010101010000001111111001 00000000101000010101000100101000001010101010101010100101010101010101 01001010101010010101010101010101010101010101010101010101010101010010 00101010100101010101010101010101010101010101010101001010101010101100 10110010101010101010101001010010101010100101010101001111010101001000 00000010101010101001101010101010010101010101010100110010101010101010 10010101010101010010101010101010100101010101010101010101010101010011 01010101010101010101001010101010101010101100011011001010101100110010 01101010111001000110101001111000101000101010010011000010010111101001,Hyperspectral Imagery Data Before Processing,So What Does Hyperspectral Imagery Data Look Like?,Black Body Radiation of the earth (300K),Solar Radiance Back-Scattered from Earths Surface,VIS,3u,10u,1u,1 mm,500,a,0.3u,VIS,Laser Sensors,SWIR,Radiometers & Imaging Systems,Photography,Passive microwave Radiometers,Radars,b,Human Eye,MIR,FIR,MW,(mm),um,500,50,100,5,1,10,Blocked,500,20,300,5,3,10,2,1.0,0.5,1.5,0.3,0.4u,0.7u,Energy,Transmission %,0,100,NIR,Wavelength,Remote Sensing Electromagnetic Spectrum,Electromagnetic waves between the “spectral windows” highlighted above are severely attenuated (either absorbed, scattered, or both) by the Earths atmosphere.,Spectral Windows,Visible Near Infrared (VNIR) 0.4 1.5 microns,3. 高光谱成像光谱仪器概览,Image Acquisition Modes Whiskbroom Imagers Pushbroom Imagers Staring Imagers Spectral Selection Modes Dispersion Element (grating, prism) Filter-Based Systems Interference Filters Acoustical-Optical Filters Liquid Crystal Tunable Filters (LCTF) Interferometer-Based Systems Michelson Interferometer Fourier Transform Interferometer System Other (e.g., Multi-order etalons),Classification of Sensors,Image Acquisition Modes, 1983 AIS, 10m pixels, 128 bands (0.8-2.4um) - retired1986 GER 63, 10m pixels, 63 bands (0.43-2.5um) 1987 AVIRIS, 20m pixels, 224 bands (0.40-2.45um)1989 CASI, 10m pixels, 288 bands (0.4-0.9um)1993 AISA, 286 bands (0.43-0.9 um)1994 TRWIS III, 242 bands (0.45-2.5 m)1995 HYDICE, 210 bands (0.4-2.5 um)1996 HyperCam, 256 bands (0.45-1.05 m)1997 PROBE-1, 128 bands (0.43-2.5um)1998 HyMap, 126 bands (0.4-2.5 um)1999 AURORA, 512 bands (0.4-0.9 um),Airborne Hyperspectral Systems,AISA Hyperspectral System,Australian Resource Information and Environment Satellite (ARIES) Naval EarthMap Observer (NEMO) Coastal Ocean Imaging Spectrometer (COIS)- Now considered a terminated program. Orbview 4 (Warfighter 1) Launched: 21 September 2001 (Failed to Orbit) NASA EO-1 Hyperion (Built by TRW) Launched: 21 November 2000 AFRL MightySat II.1 (Sindri) - Fourier Transform Hyperspectral Imager (FTHSI): Launched: 19 July 2000Compact High Resolution Imaging Spectrometer (CHRIS) Launched aboard ESAs Proba satellite on 22 October 2001,Spaceborne Hyperspectral Systems,Reflectance spectrum of a live oak from Ft. Hood TexasSignature extracted from HYDICE imagery using ENVI software,Image Measurements,Laboratory Measurements,Sample field vegetation spectral measurement,Field Measurements,4. 高光谱遥感预处理 1)Data reduction to apparent surface reflectance 2)Geometric correction,1)Transformation from radiance to reflectance -corrects for solar illumination, atmospheric absorption and scattering effects,Atmospheric Compensation,Atmospheric Effects,Calibration methods from radiance to reflectance I-normalisation methods,Calibration methods from radiance to reflectance II-normalisation methods,List of several atmospheric correction algorithms,ATmospheric REMoval (ATREM)Hyperspectral (HATCH) Data Fast Line-of-sight Atmospheric Analysis of Spectral Hypercubes (FLAASH)Atmospheric CORrection Now (ACORN),2)Geometric correction,Spectral Analysis Manager (SPAM) JPLIntegrated Software for Imaging Spectrometers (ISIS) USGS FlagstaffHyperspectral Image Processing System (HIPS) U.S. Army TECSpectral Image Processing System (SIPS) University of Colorado, BoulderSPECtrum Processing Routines (SPECPR) USGS Denver Optical Real-time Adaptive Spectral Identification System (ORASIS) NRLDIMPLE RockWare, Inc.Imaging Spectrometer Data Analysis System (ISDAS) CCRS in CanadaPCI PCI Remote Sensing CorporationEnvironment for Visualizing Images (ENVI) Research Systems, Inc.Multispectral Image Data Analysis System (MultiSpec) Purdue UniversityHyperCube U. S. Army TECProVIEW Applied Coherent Technology, Inc.ERDAS IMAGINE Commercial packageOthers,Spectral Sensing Processing Systems,5. 高光谱遥感数据处理方法与应用,1)两种波谱降维方法: PCA MNF,Principal Component Analysis (PCA),Calculation of new transformed variables (components) by a coordinate rotation Components are uncorrelated and ordered by decreasing variance First component axis aligned in the direction of the highest percentage of the total variance in the data Component axes are mutually orthogonal Maximum SNR and largest percentage of total variance in the first component,Principal Component Analysis (PCA),Principal component transformation,Minimum Noise Fraction (MNF) Transformation,The minimum noise fraction (MNF) transformation is used to determine the inherent dimensionality of image data, to segregate noise in the data, and to reduce the computational requirements for subsequent processingThe MNF transformation consists essentially of two-cascaded Principal Components transformationsThe first transformation, based on an estimated noise covariance matrix, decorrelates and rescales the noise in the data. This first step results in transformed data in which the noise has unit variance and no band-to-band correlationsThe second step is a standard Principal Components transformation of the noise-whitened data.For further spectral processing, the inherent dimensionality of the data is determined by examination of the final eigenvalues and the associated imagesThe data space can be divided into two parts: one part associated with large eigenvalues and coherent eigenimages, and a complementary part with near-unity eigenvalues and noise-dominated images. By using only the coherent portions, the noise is separated from the data, thus improving spectral processing results.,Minimum Noise Fraction Transform,AVIRIS data collected in 1997 by NASA and EPA 224 contiguous bands ranging from 0.4um to 2.5um and 20mt spatial resolution Whiskbroom scanner 68 bands selected for from 224 Rest of bands are noise,MNF,AVIRIS data collected in 1997 by NASA and EPA 224 contiguous bands ranging from 0.4um to 2.5um and 20mt spatial resolution Whiskbroom scanner 68 bands selected for from 224 Rest of bands are noise,Part of three front MNF bands compositing false colourimage,2)波谱分析-选择参考波谱(reference spectra)或端元(endmembers),View data in the “spectral space”:data scatterplot,Endmembers,convex geometry, mixing concept,Select and identify endmembers -most extreme spectra,associated with pure elements,or”purest”pixels in the image,Pixel Purity Index,To find endmembers in the data Identifies pure spectra by assigning Purity Index Randomly project n dimensional scatter plot on randomly generated unit vector,PPI Image,N Dimensional Visualizer To Select endmembers interactively,2)波谱制图 -地物分类识别方法与制图 Spectral angle mapper Spectral unmixing Matched filtering ,Spectral Angle Mapper Classification,The Spectral Angle Mapper (SAM) is a physically-based spectral classification that uses the n-dimensional angle to match pixels to reference spectra The SAM algorithm determines the spectral similarity between two spectra by calculating the angle between the spectra, treating them as vectors in a space with dimensionality equal to the number of bands,Spectral Angle Mapper (SAM) Classification,The Spectral Angle Mapper (SAM) is a physically based spectral classification that uses the n-dimensional angle to