Introduction to Imaging Spectroscopy

Transcription

1 Introduction to Imaging Spectroscopy part 2 Remote Sensing (GRS-20306)

2 Outline Part 1 Definition History Why spectroscopy works! Measurement methods Non-imaging Imaging Applications Part 2 Analytical Methods SAM SUM Exercise Cuprite

3 Reflectance [scaled from 0-1] Its all in the mix! Pixel by definition mixed Identification: pixel size vs. feature of interest Spatial distribution lost Endmember Kaolinite Dolomite Hematite Hematite Absorption Feature 0.2 Kaolinite Absorption Feature Kaolinite Absorption Feature Wavelength [nm] 1800 Dolomite Absorption Feature

4 What causes spectral mixing A variety of factors interact to produce the signal received by the imaging spectrometer: A very thin volume of material interacts with incident sunlight. All the materials present in this volume contribute to the total reflected signal. Spatial mixing of materials in the area represented by a single pixel results in spectrally mixed reflected signals. Variable illumination due to topography (shade) and actual shadow in the area represented by the pixel further modify the reflected signal, basically mixing with a black endmember. The imaging spectrometer integrates the reflected light from each pixel.

5 Analytical Mapping Methods in Imaging Spectroscopy Maximum Likelihood Classification (MLC) Spectral Angle Mapper (SAM) hods.pdf Spectral Unmixing (SUM) ectral_analysis.pdf Spectral Feature Fitting (SFF) Mixture-Tuned Matched Filtering (MTMF) RS software: ERDAS-IMAGINE and IDL-ENVI (and R)

6 Endmember selection Training areas (MLC) vs. endmembers Selection of endmembers: Spectral libraries Field radiometry Image spectra Field information Automated methods: PPI

7 Spectral Angle Mapper: principle automated method for comparing image spectra to selected endmember spectra assumes that data have been reduced to apparent reflectance (true reflectance multiplied by some unknown gain factor, controlled by topography and shadows) determines similarity between image spectrum and reference spectrum by calculating the spectral angle in n-d space (see next) SAM calculates angle map (in radians) per endmember: a new data cube is prepared for nr of selected bands Gray-level thresholding is typically used to empirically determine areas that most closely match the reference spectrum while retaining spatial coherence.

8 Spectral Angle Mapper: method Consider a reference spectrum and an pixel spectrum from two-band data. The two different materials are represented in a 2D scatter plot by a point for each given illumination, or as a line (vector) for all possible illuminations. t: pixel spectrum r: reference spectrum nb: number of bands

9 Spectral Angle Mapper: example Using hyperspectral plant signatures for CO 2 leak detection (Male et al., 2010)

10 Spectral UnMixing: principle The linear mixing model assumes no interaction between materials. If each photon only sees one material, these signals add (a linear process). Multiple scattering involving several materials can be thought of as cascaded multiplications (a non-linear process). The spatial scale of the mixing and the physical distribution of the materials govern the degree of non-linearity. Large-scale aerial mixing is very linear. Small-scale intimate mixtures are slightly non-linear. In most cases, the non-linear mixing is a second-order effect. Many surface materials mix in non-linear fashions, but approximations of linear unmixing techniques appear to work well in many circumstances (Boardman and Kruse, 1994). Using linear methods to estimate material abundance is not as accurate as using non-linear techniques, but to the first order, they adequately represent conditions at the surface.

11 Spectral UnMixing: method A single pixel with three materials A, B and C IFOV of pixel Material A B C Fraction A Each endmember has a unique spectrum B C The mixed spectrum is just a weighted average mix=0.25*a+0.25*b+0.5*c

12 Spectral UnMixing: method

13 Mathematics of Linear Unmixing R R i = reflectance of the mixed spectrum of a pixel in image band i j = fraction of end-member j Re ij = reflectance of the end-member spectrum j in band i i = the residual error n = number of end-members Constraining assumptions: i n j 1 f j Re ij n j 1 i f 1 and 0 f j 1 j

14 Cuprite, Nevada Cuprite, Nevada (USA) is one of the most frequently used test-site for remote sensing instrument validation Cuprite is mineral of the class Oxides and Hydroxides and its chemical formula is Copper Oxide (Cu 2 O). Cuprite is a major ore of Copper and is still actively mined

15 Cuprite A Real World Example Mineral deposits. Provide resources for modern society Possible sources of life Possible sources of acidic water Cuprite, Nevada is an ancient hydrothermal alteration system (like Yellowstone)

16 Cuprite Mapping

17 Cuprite 3D View

18 Landsat TM (Cuprite) Landsat TM band 5 at 1650 nm

19 GER 63 channel data (Cuprite) GERIS band 43 at nm

20 AVIRIS 1995 (Cuprite) AVIRIS band 194 at nm

21 Cuprite - Spectral Unmixing Alunite Calcite Kaolinite Silica Zeolite RMS image Geologic map from unmixing

22 Mars: Mineral Mapping with OMEGA Regional map of Syrtis Major region showing regions enriched in olivine, High Calcium Pyroxene (HCP) and Low Calcium Pyroxene (LCP). Results draped over MOLA shaded relief (Mustard et al., Science, 2005)

23 Fingerprint The spectrum of each material produces a fingerprint which allows it to be identified Tetracorder identifies multiple materials, including effects of mixtures, grain size, and coatings.

24 Spectral endmembers of heathland habitats Source: Mücher, Kooistra, et al., 2012 Ecological Indicators

25 SUM of heathland habitats

26 Summary Mixed pixels: homogeneity at RS pixel level is a rare phenomenon at the Earth surface SAM and SUM use high spectral dimension of IS to map surface components Identification of relative concentrations for mixed pixels including spatial distribution Proper endmember selection is crucial for accurate results