In hyperspectral imaging, the quality of the collected spectral signatures can be degraded by blurring due to the channel weighting function of the imaging spectrometer. In this work, we are investigating reconstruction
techniques to enhance salient features and remove degradation effects in measured spectra to assist in
subsequent machine analysis. Here preliminary work is presented showing spectral restoration using simulated
data and real data of the AVIRIS NW Indian Pines hyperspectral image using different restoration
algorithms. The restored AVIRIS image was classified and the classification accuracy was used to assess the
usefulness of the restoration process. All the methods gave comparable results with the Jansson method
giving slightly higher classification accuracy.
A fundamental challenge to Remote Sensing is mapping the ocean floor in coastal shallow waters where variability, due to the interaction between the coast and the sea, can bring significant disparity in the
optical properties of the water column. The objects to be detected, coral reefs, sands and submerged aquatic vegetation, have weak signals, with temporal and spatial variation. In real scenarios the absorption and backscattering coefficients have spatial variation due to different sources of variability (river discharge, different depths of shallow waters, water currents) and temporal fluctuations. This paper presents the development of algorithms for retrieving information and its application to the recognition, classification
and mapping of objects under coastal shallow waters. A mathematical model that simplifies the radiative transfer equation was used to quantify the interaction between the object of interest, the medium and the sensor. The retrieval of information requires the development of mathematical models and processing tools in the area of inversion, image reconstruction and detection. The algorithms developed were applied to one set of remotely sensed data: a high resolution HYPERION hyperspectral imagery. An inverse problem arises as this spectral data is used for mapping the ocean shallow waters floor. Tikhonov method of regularization was used in the inversion process to estimate the bottom albedo of the ocean floor using <i>a priori</i> information in the form of stored spectral signatures, previously measured, of objects of interest, such as sand, corals, and sea grass.
This paper addresses the problem of estimating the dimension of a hyperspectral image. Spanning and intrinsic dimension concepts are studied as ways to determine the number of degrees of freedom needed to represent a Hyperspectral Image. Algorithms for the estimation of spanning and intrinsic dimension are reviewed and applied to hyperspectral images. Estimators are evaluated and compared using simulated and AVIRIS data. The final objective of this work is to develop an algorithm to determine the number of bands to select in a band subset selection algorithm.