Multi-Spectral Enhancement Techniques: Spectral Operations, PCM and Spectral Management (Especially for GATE-Geospatial 2022)

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Spectral remote sensing involves the collection, processing and interpretation of electromagnetic energy that is reflected or emitted from features on the Earth՚s surface.

Visualizing Remotely Sensed Data: True Color and False Color

Spectral Remote Sensing

  • The fundamental premise is that different materials reflect and emit energy at different wavelengths and can be discerned accordingly. In mineral exploration, spectral imagery can be used for a variety of applications that include:
    • Logistical planning
    • Indicator mapping
    • Geomorphological mapping
    • Lithological mapping
    • Mineral/alteration mapping
    • Structural mapping
  • Spectral imagery can be particularly effective where the type of mineralization being sought is associated with geological characteristics that are spectrally distinct, such as a specific host rock or alteration type.
Illustration 2 for Spectral_Remote_Sensing
  • Some of the major deposit types that can be identified using spectral imagery include porphyry copper-gold, epithermal gold, orogenic gold and volcanogenic massive sulphides.
  • Using spectral imagery in mineral exploration is associated with numerous advantages that include:
    • Being able to consider large areas cost-effectively
    • Providing an elevated vantage to identify features less visible at ground level
    • Being able to identify features not visible to the human eye
    • Using imagery that is often readily available “off-the-shelf”
    • Being able to acquire some types of imagery for free
    • Using a discrete and confidential means of exploration
    • Not requiring permitting
    • Having a very low environmental impact
  • Like other exploration methods, spectral remote sensing is associated with some limitations.
  • Fundamentally, it can only detect what is exposed at the surface and, consequently, anything obscuring the features of interest (for example, snow/ice, vegetation, shadow, urbanization, etc.) can hinder its effectiveness.
  • Remote sensing has provided valuable insights into agronomic management over the past 40 yr.
  • The e contributions of individuals to remote sensing methods have lead to understanding of how leaf reflectance and leaf emittance changes in response to leaf thickness, species, canopy shape, leaf age, nutrient status, and water status.
  • Leaf chlorophyll and the preferential absorption at different wavelengths provides the basis for utilizing reflectance with either broad-band radiometers typical of current satellite platforms or hyper spectral sensors that measure reflectance at narrow wavebands.
  • Understanding of leaf reflectance has led to various vegetative indices for crop canopies to quantify various agronomic parameters, e. g. , leaf area, crop cover, biomass, crop type, nutrient status, and yield.
  • Emittance from crop canopies is a measure of leaf temperature and infrared thermometers have fostered crop stress indices currently used to quantify water requirements.
  • These tools are being developed as we learn how to use the information provided in reflectance and emittance measurements with a range of sensors.
  • Remote sensing continues to evolve as a valuable agronomic tool that provides information to scientists, consultants, and producers about the status of their crops.

Spectral Image Arithmetic Operations

The operations of addition, subtraction, multiplication and division are performed on two or more co-registered images of the same geographical area. These techniques are applied to images from separate spectral bands from single multi-spectral dataset or they may be individual bands from image data sets that have been collected at different dates. More complicated algebra is sometimes encountered in the derivation of sea-surface temperature from multi-spectral thermal infrared data (so-called split-window and multichannel to the addition of images are generally carried out to give a dynamic range of the image that equals the input images.

  • Band Subtraction Operation on images is sometimes carried out to co-register scenes of the same area acquired at different times for change detection.
  • Multiplication of images normally involves the use of a single ‘real’ image and binary image made up of ones and zeros.
  • Band Ratioing or image division is one of the most common transforms applied to image data. Image rating serves to highlight subtle variations in the spectral responses of various surface covers. By rationing the data from two different spectral bands, the resultant image enhances variations in the slopes of the spectral reflectance curves between the two different spectral ranges that may otherwise be masked by the pixel brightness variations in each of the bands. The following example illustrates the concept of spectral ratios. Healthy vegetation reflects strongly in the near-infrared portion of the spectrum while absorbing strongly in the visible red. Other surface types, such as soil and water, show near equal reflectances in both the near-infrared and red portions. Thus, a ratio image of Band 7 (Near-Infrared - 0.8 to 1.1 mm) divided by Band 5 (Red - 0.6 to 0.7 mm) would result in ratios much greater than 1.0 for vegetation, and ratios around 1.0 for soil and water. Thus, the discrimination of vegetation from other surface cover types is significantly enhanced. Also, we may be better able to identify areas of unhealthy or stressed vegetation, which show low near-infrared reflectance, as the ratios would be lower than for healthy green vegetation.
    • Another benefit of spectral ratioing is that, because we are looking at relative values (i.e.. . ratios) instead of absolute brightness values, variations in scene illumination as a result of topographic effects are reduced. Thus, although the absolute reflectances for forest covered slopes may vary depending on their orientation relative to the sun՚s illumination, the ratio of their reflectances between the two bands should always be very similar. More complex ratios involving the sums of and differences between spectral bands for various sensors have been developed for monitoring vegetation conditions. One widely used image transform is the Normalized Difference Vegetation Index (NDVI) which has been used to monitor vegetation conditions on continental and global scales.
    • The NDVI is calculated from these individual measurements as follows: NDVI = (NIR - RED) / (NIR + RED)
    • Where RED and NIR stand for the spectral reflectance measurements acquired in the red and near-infrared regions, respectively. These spectral reflectances are themselves ratios of the reflected over the incoming radiation in each spectral band individually; hence they take on values between 0.0 and 1.0. By design, the NDVI itself thus varies between -1.0 and + 1.0

Principal Component Analysis

  • Spectrally adjacent bands in a multi-spectral remotely sensed image are often highly correlated. Multiband visible/near-infrared images of vegetated areas will show negative correlations between the near-infrared and visible red bands and positive correlations among the visible bands because the spectral characteristics of vegetation are such that as the vigour or greenness of the vegetation increases the red reflectance diminishes and the near-infrared reflectance increases. Thus the presence of correlations among the bands of E multi-spectral image implies that there is redundancy in the data and Principal Component Analysis aims at removing this redundancy.
PC Transformation

PC Transformation — the variance along PC Band 1 is larger than the variance within either of the original data bands

  • Principal Components Analysis (RCA) is related to another statistical technique called factor analysis and can be used to transform a set of image bands such that the new bands (called principal components) are uncorrelated with one another and are ordered in terms of the amount of image variation they explain. The components are thus a statistical abstraction of the variability inherent in the original band set.
  • To transform the original data onto the new principal component axes, transformation coefficients (Eigenvalues and Eigenvectors) are obtained that are further applied in a linear fashion to the original pixel values. This linear transformation is derived from the covariance matrix of the original data set. These transformation coefficients describe the lengths and directions of the principal axes. Such transformations are generally applied either as an enhancement operation or prior to the classification of data. In the context of PCA, information means variance or scatter about the mean. Multi-spectral data generally have a dimensionality that is less than the number of spectral bands. The purpose of PCA is to define the dimensionality and to fix the coefficients that specify the set of axes, which point in the directions of greatest variability. The bands of PCA are often more interpretable than the source data.
Transformation of the Original Data

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