William D. Philpot

Derivative Analysis of Hyperspectral Data

Abstract With the goal of applying derivative spectral analysis to analyze high-resolution, spectrally continuous remote sensing data, several smoothing and derivative computation algorithms have been reviewed and modified to develop a set of cross-platform spectral analysis tools. Emphasis was placed on exploring different smoothing and derivative algorithms to extract spectral details from spectral data sets. A modular program was created to perform interactive derivative analysis. This module calculated derivatives using either a convolution (Savitzky–Golay) or finite divided difference approximation algorithm. Spectra were smoothed using one of the three built-in smoothing algorithms (Savitzky–Golay smoothing, Kawata–Minami smoothing, and mean-filter smoothing) prior to the derivative computation procedures. Laboratory spectral data were used to test the performance of the implemented derivative analysis module. An algorithm for detecting the absorption band positions was executed on synthetic spectra and a soybean fluorescence spectrum to demonstrate the usage of the implemented modules in extracting spectral features. Issues related to smoothing and spectral deviation caused by the smoothing or derivative computation algorithms were also observed and are discussed. A scaling effect, resulting from the migration of band separations when using the finite divided difference approximation derivative algorithm, can be used to enhance spectral features at the scale of specified sampling interval and remove noise or features smaller than the sampling interval.

Bathymetric mapping with passive multispectral imagery

Bathymetric mapping will be most straightforward where water quality and atmospheric conditions are invariant over the scene. Under these conditions, both depth and an effective attenuation coefficient of the water over several different bottom types may be retrieved from passive, multispectral imagery. As scenes become more complex-with changing water type and variable atmospheric conditions-it is probable that a strictly spectral analysis will no longer be sufficient to extract depth from multispectral imagery. In these cases an independent source of information will be required. The most likely sources for such information are spatial and temporal variations in image data.

Yellowness index: An application of spectral second derivatives to estimate chlorosis of leaves in stressed vegetation

This paper introduces yellowness index (YI) as a measure for chlorosis of leaves in stressed plants. YI provides a measure of the change in shape of the reflectance spectra between the maximum near...

The derivative ratio algorithm: avoiding atmospheric effects in remote sensing

The author describes the development of the derivative ratio algorithm based on derivatives of a simple radiative transfer equation. The limiting conditions of the algorithm are derived and demonstrated using examples of reflectance spectra of turbid water and an ash leaf. For these example targets, the algorithm indicates that some spectral features do survive the trip through the atmosphere and are recognizable using ratios of the spectral derivatives. The most detectable spectral features tended to be those that spanned bandwidths substantially larger than the minimum bandwidth tested (10 nm). >

A derivative-aided hyperspectral image analysis system for land-cover classification

The large number of spectral bands in hyperspectral data seriously complicates their use for classification. Selection of a useful subset of bands or derived features (spectral ratios, differences, derivatives) is always desirable, strongly affects the accuracy of the classification, and is often a practical necessity to keep the processing speed and memory requirements under control. This paper examines one possible procedure for selecting spectral derivatives to improve supervised classification of hyperspectral images. The procedure is designed to identify derivative features that are more effective at separating target classes and then add them to a base subset of features for classification. The goal is to create the smallest set of features that will result in the best classification result. A key issue in this process is the interplay of the number of features and the size of the training data sets since classification accuracy declines if the dimensionality of the feature space is too large relative to the number of training samples.

Radiative transfer in stratified waters: a single-scattering approximation for irradiance.

The singly scattered irradiance (SSI) model is an approximate radiative transfer model designed to describe optically shallow stratified waters. It is intended to be a tool to aid interpretation of remote (satellite or aircraft) observations of water color. The SSI model was derived in analogy to single-scattering radiance models. As it is based on irradiance (rather than radiance), multiple-scattering events are included implicitly, yielding a model which is expressed in terms of readily measurable parameters and which preserves mathematical simplicity without sacrificing many important subtleties of the radiative transfer process. The success of the SSI model relies on the underwater radiance distribution being nearly independent of sun angle, cloud cover and depth, and the resulting quasi-inherent property of the irradiance attenuation coefficient, irradiance reflectance, and distribution functions. A derivation of the model is presented, along with evidence for the qualitative and quantitative correctness of the model.

Spectral texture pattern matching: a classifier for digital imagery

Because of the difficulty of specifying general criteria for texture features, automated image analysis in the field of remote sensing has been largely restricted to the spectral domain. An algorithm that integrates spectral and textural information in the classification process is presented. The procedure is capable of classifying a region of arbitrary shape and size and operates effectively near class boundaries. Except for the requirement of user-defined training data, the algorithm can be completely automated. For all accuracy measures tested, the classification accuracy of the spectral texture pattern matching algorithm was higher for most classes than that of the maximum-likelihood classifier. Furthermore, errors with the spectral/textural algorithm are largely confined to omission, which gives a high degree of confidence to the classified pixels. >

Environmental effects on laser-induced fluorescence spectra of natural waters

Laser fluorosensing can be used to monitor dissolved organic carbon (DOC), but analysis of the data can be hindered by several environmental phenomena. These phenomena include attenuation of the laser beam and differential attenuation of the fluorescence by the water column, variability in the molecular weight composition of the DOC, and temperature, pH, and metal ion effects on DOC fluorescence. These factors are discussed in terms of their effect on laboratory and remote field data analysis. Experimental results are provided. Analysis of fluorosensor data of DOC may be improved by compensating for the environmental factors. An improved methodology is discussed, and a suggestion is made for indirect monitoring of pH and metal ion concentration.

Characterizing temporal vegetation dynamics of land use in regional scale of Java Island, Indonesia

Improving the understanding of land use and land cover is a major research challenge for the human-environmental sciences and is essential for many aspects of global environmental research. Considering seasonal vegetation dynamics or phenological dynamics in multi-year series leads to a broader view of land use and land cover. This study is based on the hypothesis that a pixel representing a complex but consistent land use has a typical, distinct and repeated temporal pattern of vegetation index inter-annually, which can be used as characteristic signatures for land use classification. Considering the seasonal events and climatic variability in Indonesia, we characterized the temporal vegetation dynamics of long-term land use by using multi-temporal Moderate Resolution Imaging Spectroradiometer (MODIS) enhanced vegetation index 16-day composite data from 2001 to 2007, and then generated a land use map using those characteristics. Accuracy assessment of the results showed the need to evaluate such methods for land use types that do not have a consistent yearly pattern. On the other hand, the identification of the intensive agriculture lands, such as paddy rice and upland, was satisfactory. Although the mixed pixel issue is quite problematic when using MODIS data, the results indicate that MODIS data offer great promise for characterizing seasonal as well as multi-year variation at large scales. Indeed, the methodology proposed in this research distinguished among many specific land use classes based on temporal land cover information properties. Characterization of temporal vegetation dynamics patterns would provide sufficient, significant and useful information regarding the patterns of land use; consequently it should be possible to consider the actual, subtle nature of inter-annual land use change as well as overall land use.

Estimating Atmospheric Transmission and Surface Reflectance From a Glint-Contaminated Spectral Image

Sun glint obscures the radiance originating from within the water, but it also mirrors the solar radiance after transmission through the atmosphere. The difference between a glint-contaminated pixel and a nearby nonglint pixel from a spectral image prior to atmospheric correction yields a direct estimate of the spectral transmission of solar radiance which can then be used both to confirm atmospheric models and to retrieve an estimate of remote sensing reflectance