Hyperspectral Remote Sensing has the potential to be used as an effective coral monitoring system from either space or airborne sensors. The problems to be addressed in hyperspectral imagery of coastal waters are related to the medium, which presents high scattering and absorption, and the object to be detected. The object to be detected, in this case coral reefs or different types of ocean floor, has a weak signal as a consequence of its interaction with the medium. The retrieval of information about these targets requires the development of mathematical models and processing tools in the area of inversion, image reconstruction and detection. This paper presents the development of algorithms that does not use labeled samples to detect coral reefs under coastal shallow waters. Synthetic data was generated to simulate data gathered using a high resolution imaging spectrometer (hyperspectral) sensor. A semi-analytic model that simplifies the radiative transfer equation was used to quantify the interaction between the object of interest, the medium and the sensor. Tikhonov method of regularization was used as a starting point in order to arrive at an inverse formulation that incorporates a priori information about the target. This expression will be used in an inversion process on a pixel by pixel basis to estimate the ocean floor signal. The a priori information is in the form of previously measured spectral signatures of objects of interest, such as sand, corals, and sea grass.
Hyperspectral Remote Sensing has the potential to be used as an effective coral monitoring system from space. The problems to be addressed in hyperspectral imagery of coastal waters are related to the medium, clutter, and the object to be detected. In coastal waters the variability due to the interaction between the coast and the sea can bring significant disparity in the optical properties of the water column and the sea bottom. In terms of the medium, there is high scattering and absorption. Related to clutter we have the ocean floor, dissolved salt and gases, and dissolved organic matter. The object to be detected, in this case the coral reefs, has a weak signal, 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.
The retrieval of information about an object beneath some medium with high scattering and absorption properties requires the development of mathematical models and processing tools in the area of inversion, image reconstruction and detection. This paper presents the development of algorithms for retrieving information and its application to the recognition and classification of coral reefs under water with particles that provide high absorption and scattering. The data was gathered using a high resolution imaging spectrometer (hyperspectral) sensor. 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. Tikhonov method of regularization was used in the inversion process to estimate the bottom albedo, ρ, of the ocean floor using a priori information. The a priori information is in the form of measured spectral signatures of objects of interest, such as sand, corals, and sea grass.
Hyperspectral imaging has been shown to be useful for producing qualitative maps of the concentration of hemoglobin in skin, and quantitative maps of its oxygen saturation. These maps provide information about the health of the skin, which may be useful in medical diagnosis and in planning intervention for skin lesions. Wavelengths have usually been determined on a somewhat intuitive basis using spectra based on analytical models. In the present work, we demonstrate a means of selecting wavelengths from a data cube of experimental data.
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