Spectral Unmixing Applied to Desert Soils for the - Naval ...

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A spectral library was then built and the spectra for the various levels of disturbance were used as inputs for the mixture analysis and mapping of the AVIRIS imagery. Figure 19. This figure illustrates the original sample radiance data (red) and white reference spectrum (black) collected with the ASD spectrometer (left) compared to the spectrum calculated using spectral math (right). The blue reflectance spectrum (right), represents the red spectrum (left) divided by the black spectrum (left) (solar spectrum removed). 3. Spectral Mixture Analysis The mixture-tuned matched filter (MTMF) approach was used in this research. The MTMF process combines older methods of Matched Filtering (MF), used for target signal detection and signal processing, with newer methods and algorithms that account for mixing of reflectance that occurs within a pixel (Figure 20) (Boardman and Kruse, 2011). The previous MF method had a high false alarm rate and exhibited difficulty distinguishing between similar yet rare targets in the image space because of mixing that 40
occurs on a sub-pixel level (Boardman and Kruse, 2011). The MTMF method has proven successful at leveraging mixing between a point of interest and the background to eliminate such problems as false alarms during the analysis process (Boardman and Kruse, 2011). Through the combination of spectral unmixing, MF, and convex geometry, MTMF excels at accurately mapping subtle sub-pixel targets with sufficiently reduced false alarms (Boardman and Kruse, 2011). Figure 20. This figure from Boardman and Kruse (2011) shows how mixing in a picture element (pixel) occurs based on 2 (left) and 3 (right) endmember concepts. The 3 endmember example shows how this occurs both spatially and spectrally within the pixel. The MTMF method is broken into the following steps: pre-processing and data preparation, MF abundance estimation, and mixture tuning (MT) to reject any false positives (Boardman and Kruse, 2011). Before MTMF can be conducted, it makes sense to first derive endmembers from the data using the hourglass method illustrated in Figure 21 (below) (Boardman and Kruse, 2011). For the purposes of this research, endmembers have been derived both from the data using the hourglass approach described in (Boardman and Kruse, 2011), as well as via the use of a spectral library created using the ASD collected spectra. Imagery derived endmembers come from the AVIRIS flight lines 41
Page 1 and 2:
NAVAL POSTGRADUATE SCHOOL MONTEREY,
Page 3 and 4:
REPORT DOCUMENTATION PAGE Form Appr
Page 5 and 6:
Approved for public release; distri
Page 7 and 8:
ABSTRACT Desert areas cover approxi
Page 9 and 10: TABLE OF CONTENTS I. INTRODUCTION..
Page 11 and 12: LIST OF FIGURES Figure 1. The above
Page 13 and 14: spectrum by atmospheric effects. Re
Page 15 and 16: emoved function showing an absorpti
Page 17 and 18: LIST OF TABLES Table 1. This table
Page 19 and 20: LIST OF ACRONYMS AND ABBREVIATIONS
Page 21 and 22: I. INTRODUCTION A study published b
Page 23 and 24: II. THE PHYSICS BEHIND REMOTE SENSI
Page 25 and 26: sensitive a given sensor is to diff
Page 27 and 28: Figure 3. From Green et al. (1998),
Page 29 and 30: analyzing imagery spectra, it is mo
Page 31 and 32: After data have been converted to r
Page 33 and 34: Collins et al. (1997) was able to s
Page 35 and 36: These purposes include, but are not
Page 37 and 38: III. DESERT ECOSYSTEM CHARACTERISTI
Page 39 and 40: sagebrush of Utah, Montana, and the
Page 41 and 42: in desert regions include argids, o
Page 43 and 44: 2. Biological Soil Crusts (BSCs) Bi
Page 45 and 46: 2004), especially in cases where ma
Page 47 and 48: IV. STUDY SITES The focus area of t
Page 50 and 51: Figure 13. This figure illustrates
Page 52 and 53: Following the uplift that occurred
Page 54 and 55: the Mazourka Canyon OHV park betwee
Page 56 and 57: wavelengths being analyzed to obtai
Page 58 and 59: 2. Field Spectroscopy An Analytical
Page 62 and 63: after atmospherically correcting th
Page 64 and 65: where: is the mean corrected and no
Page 66 and 67: also be seen in Figure 23. The leve
Page 68 and 69: Figure 24. This figure is a compari
Page 70 and 71: Figure 25. This figure shows ASD co
Page 72 and 73: Looking at Figure 25 it is apparent
Page 74 and 75: A. IMAGERY DERIVED ENDMEMBERS The i
Page 76 and 77: Figure 28. The above shows some of
Page 78 and 79: spectrometer, reflectance values we
Page 80 and 81: such an inference can be made (Ben-
Page 82 and 83: While this is lower than the hoped
Page 84 and 85: While the lower value would initial
Page 86 and 87: Figure 33. This figure shows the ad
Page 88 and 89: Inset C of Figure 35 is the same da
Page 90 and 91: However, the presences of BSCs are
Page 92 and 93: A B C Figure 37. Inset A shows the
Page 94 and 95: A B C Figure 38. Inset A shows a co
Page 96 and 97: differences in the studies by other
Page 98 and 99: small concentrations making them un
Page 100 and 101: area making it possible to tell wha
Page 102 and 103: Clark, R. N., Swayze, G. A., Livo,
Page 104 and 105: Kruse, F. A., Boardman, J. W., and
Page 106 and 107: Sharp, R. P., and Glazner, A. F., (
Page 108: INITIAL DISTRIBUTION LIST 1. Defens
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