Arthur C. R. Gleason

Interpretation of hyperspectral remote-sensing imagery by spectrum matching and look-up tables

A spectrum-matching and look-up-table (LUT) methodology has been developed and evaluated to extract environmental information from remotely sensed hyperspectral imagery. The LUT methodology works as follows. First, a database of remote-sensing reflectance (Rrs) spectra corresponding to various water depths, bottom reflectance spectra, and water-column inherent optical properties (IOPs) is constructed using a special version of the HydroLight radiative transfer numerical model. Second, the measured Rrs spectrum for a particular image pixel is compared with each spectrum in the database, and the closest match to the image spectrum is found using a least-squares minimization. The environmental conditions in nature are then assumed to be the same as the input conditions that generated the closest matching HydroLight-generated database spectrum. The LUT methodology has been evaluated by application to an Ocean Portable Hyperspectral Imaging Low-Light Spectrometer image acquired near Lee Stocking Island, Bahamas, on 17 May 2000. The LUT-retrieved bottom depths were on average within 5% and 0.5 m of independently obtained acoustic depths. The LUT-retrieved bottom classification was in qualitative agreement with diver and video spot classification of bottom types, and the LUT-retrieved IOPs were consistent with IOPs measured at nearby times and locations.

Image-Based Coral Reef Classification and Thematic Mapping

This paper presents a novel image classification scheme for benthic coral reef images that can be applied to both single image and composite mosaic datasets. The proposed method can be configured to the characteristics (e.g., the size of the dataset, number of classes, resolution of the samples, color information availability, class types, etc.) of individual datasets. The proposed method uses completed local binary pattern (CLBP), grey level co-occurrence matrix (GLCM), Gabor filter response, and opponent angle and hue channel color histograms as feature descriptors. For classification, either k- nearest neighbor (KNN), neural network (NN), support vector machine (SVM) or probability density weighted mean distance (PDWMD) is used. The combination of features and classifiers that attains the best results is presented together with the guidelines for selection. The accuracy and efficiency of our proposed method are compared with other state-of-the-art techniques using three benthic and three texture datasets. The proposed method achieves the highest overall classification accuracy of any of the tested methods and has moderate execution time. Finally, the proposed classification scheme is applied to a large-scale image mosaic of the Red Sea to create a completely classified thematic map of the reef benthos.

Automated classification of underwater multispectral imagery for coral reef monitoring

Survey protocols for monitoring the composition and status of coral reef benthic communities vary in the level of detail acquired, but fundamentally follow one of two approaches: 1) a diver identifies organisms in the field, 2) an analyst identifies organisms from underwater imagery (photos or video). Both methods are highly labor intensive and require a trained biologist/geologist. A method for automated classification of reef benthos would improve coral reef monitoring by reducing the cost of data analysis. Spectral classification of standard (three-band) color underwater imagery does not work well for distinguishing major bottom types. Recent publications of hyperspectral reflectance of corals, algae, and sediment, on the other hand, suggest that careful choice of narrow (-10 nm) spectral bands might improve classification accuracy relative to the three wide bands available on commercial cameras. We built an underwater multispectral camera to test whether narrow spectral bands were actually superior to standard RGB cameras for automated classification of underwater images. A filter wheel was used to acquire imagery in six 10 nm spectral bands, which were chosen from suggestions in the literature. Results indicate that the algorithms suggested in the literature require very careful compensation for variable illumination and water column attenuation for even marginal success in classifying underwater imagery. On the other hand, a new algorithm, based on the normalized difference ratio of images at 568 nm and 546 nm can reliably segment photosynthetic organisms (corals and algae) from non-photosynthetic background. Moreover, when this new algorithm is combined with very simple texture segmentation, the general cover classes of coral and algae can be discriminated from the image background with accuracies on the order of 80%. These results suggest that a combination of high spectral resolution and texture-based image segmentation may be an optimal methodology for automated classification of underwater coral reef imagery.

Detailed validation of the bidirectional effect in various Case I and Case II waters.

Simulated bidirectional reflectance distribution functions (BRDF) were compared with measurements made just beneath the waters surface. In Case I water, the set of simulations that varied the particle scattering phase function depending on chlorophyll concentration agreed more closely with the data than other models. In Case II water, however, the simulations using fixed phase functions agreed well with the data and were nearly indistinguishable from each other, on average. The results suggest that BRDF corrections in Case II water are feasible using single, average, particle scattering phase functions, but that the existing approach using variable particle scattering phase functions is still warranted in Case I water.

Observation of non-principal plane neutral points in the in-water upwelling polarized light field

Neutral points are specific directions in the light field where the three Stokes parameters Q, U, V, and thus the degree of polarization simultaneously go to zero. We have made the first measurement of non-principal-plane neutral points in the upwelling light field in natural waters. These neutral points are located at approximately 40°- 80° nadir angle and between 120° - 160° azimuth to the sun which is well off of the principal plane. Calculations show that the neutral point positions are very sensitive to the balance in the incident light between the partially polarized skylight and the direct solar beam.

Littoral seafloor sensing and characterization using marine electromagnetics, optical imagery, and remotely and autonomously operated platforms

Seafloor sensing in littoral environments is challenged by a combination of technical requirements related to the detection, georegistration, and confirmation or characterization of natural and man-made items on, or beneath the seafloor. Specifically, EM sensing from unmanned systems enables the positioning of array-based sensors directly over targets of interest in a wide range of littoral environments: from surf zone to benthic areas in 100s of meters of water. Targets include both anthropogenic objects such as marine archeology and salvage, infrastructure associated with undersea cables, seabed foundations for windfarms, and unexploded ordnance (UXO) and other munitions hazards as well as shallow natural and geologic objects such as freshwater lens, gas hydrates, mineral ore, and heterogeneous sediment deposits. In this paper, we present aspects of the design, development and testing of array configurations from testing and evaluations in littoral environments. This includes integration and testing with multiple remotely and autonomously operated swimming platforms (ROVs, AUVs, and hybrids). In particular, we demonstrate the deployment of an integrated system based on a hybrid autonomous underwater vehicle and comprising bottom following, station keeping, and waypoint mapping control, a multi-channel frequency-domain EM array, and multiple high resolution imaging sensors. Results from initial testing and pilot studies for UXO surveying, marine archeology, and seabed classification are summarized.

Recovery of coral reef rugosity from stereo image pairs

Summary form only given. Small (cm-m) scale topography of the seabed is important for a number of applications. For example, ecologists are concerned with roughness on this scale (rugosity) because it is an important characteristic of habitat complexity that can be correlated to species diversity, fish abundance, and past disturbance events such as hurricanes. Systematic measurements of rugosity through time can be used to determine changes in habitat due to accretion and erosion of the substrate.

Improved supervised classification of underwater military munitions using height features derived from optical imagery

A supervised classification routine was used to classify munitions targets and basic seabed types from underwater images. Additional features that were based on the local relief, or height, of the seabed were then added to the classifier and new results computed using the expanded feature set. The height data were generated from the input images themselves using structure-from-motion computer vision techniques. The initial image classifier was shown to distinguish munitions from non-munitions (background) with generally > 80% accuracy except that many false positive matches for munitions were observed. Extending the algorithm to also use height data derived from stereo reconstruction showed that incorporating such “2.5-D” data greatly improved the classification results. Using the 2.5-D information reduced the number of false positives. Furthermore, improved accuracy was observed not only on the basic, binary munitions / non-munitions classes. Adding 2.5-D information also improved the capability to discriminate different types of munitions from one another.