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Hyperspectral imagery offers unique benefits for detection of land and water features due to the information contained in reflectance signatures such as the bi-directional reflectance distribution function or BRDF. The reflectance signature directly shows the relative absorption and backscattering features of targets. These features can be very useful in shoreline monitoring or surveillance applications, for example to detect weathered oil. In real-time detection applications, processing of hyperspectral data can be an important tool and Optimal band selection is thus important in real time applications in order to select the essential bands using the absorption and backscatter information. In the present paper, band selection is based upon the optimization of target detection using contrast algorithms. The common definition of the contrast (using only one band out of all possible combinations available within a hyperspectral image) is generalized in order to consider all the possible combinations of wavelength dependent contrasts using hyperspectral images. The inflection (defined here as an approximation of the second derivative) is also used in order to enhance the variations in the reflectance spectra as well as in the contrast spectrua in order to assist in optimal band selection. The results of the selection in term of target detection (false alarms and missed detection) are also compared with a previous method to perform feature detection, namely the matched filter. In this paper, imagery is acquired using a pushbroom hyperspectral sensor mounted at the bow of a small vessel. The sensor is mechanically rotated using an optical rotation stage. This opto-mechanical scanning system produces hyperspectral images with pixel sizes on the order of mm to cm scales, depending upon the distance between the sensor and the shoreline being monitored. The motion of the platform during the acquisition induces distortions in the collected HSI imagery. It is therefore necessary to apply a motion correction to the imagery. In this paper, imagery is corrected for the pitching motion of a vessel, which causes most of the deformation when the vessel is anchored at 2 points (bow and stern) during the acquisition of the hyperspectral imagry.

Collection of pushbroom sensor imagery from a mobile platform requires corrections using inertial measurement units (IMU's) and DGPS in order to create useable imagery for environmental monitoring and surveillance of shorelines in freshwater systems, coastal littoral zones and harbor areas. This paper describes a suite of imaging systems used during collection of hyperspectral imagery in northern Florida panhandle and Gulf of Mexico airborne missions to detect weathered oil in coastal littoral zones. Underlying concepts of pushbroom imagery, the needed corrections for directional changes using DGPS and corrections for platform yaw, pitch, and roll using IMU data is described as well as the development and application of optimal band and spectral regions associated with weathered oil. Pushbroom sensor and frame camera data collected in response to the recent Gulf of Mexico oil spill disaster is presented as the scenario documenting environmental monitoring and surveillance techniques using mobile sensing platforms. Data was acquired during the months of February, March, April and May of 2011. The low altitude airborne systems include a temperature stabilized hyperspectral imaging system capable of up to 1024 spectral channels and 1376 spatial across track pixels flown from 3,000 to 4,500 feet altitudes. The hyperspectral imaging system is collocated with a full resolution high definition video recorder for simultaneous HD video imagery, a 12.3 megapixel digital, a mapping camera using 9 inch film types that yields scanned aerial imagery with approximately 22,200 by 22,200 pixel multispectral imagery (~255 megapixel RGB multispectral images in order to conduct for spectral-spatial sharpening of fused multispectral, hyperspectral imagery. Two high spectral (252 channels) and radiometric sensitivity solid state spectrographs are used for collecting upwelling radiance (sub-meter pixels) with downwelling irradiance fiber optic attachment. These sensors are utilized for cross calibration and independent acquisition of ground or water reflectance signatures and for calculation of the bi-directional reflectance distribution function (BRDF). Methods are demonstrated for selecting optimal spectral regions and bands for discrimination, detection and characterization of weathered oil in the Northern Gulf of Mexico waters and littoral zones in response to the Deepwater Horizon oil spill disaster. The techniques allow for the use of sun and sky glint regions in imagery to identify water surface wave field characteristics as well as oil slicks. The systems described provide unique data sets for remote sensing algorithm development and future testing of radiative transfer models useful in studying weathered oil fate, distribution and extent.

Biofeedback (BFB) and neurofeedback (NFB) training are two promising approaches for the non-invasive modulation of human physiological activity paired with cognitive, emotional, and behavioral functioning. In this article, we summarize the state of the art with regard to the efficiency of BFB and NFB studies for the optimization of cognitive performance within a non-clinical context. We review the different training protocols with their underlying theoretical perspectives and the different outcomes regarding cognitive performance. This review showed that BFB and NFB are promising training techniques. However, the use of varying terminology to refer to similar concepts, diverse methodological designs, and cognitive assessments along with apparent differences in NFB frequency ranges makes it difficult to compare the outcomes over different studies and to draw general conclusions. Furthermore, a question largely ignored until now remains about the long-term effects of both training and thus the sustainability ofthe achieved cognitive enhancement. Despite promising results of both techniques, this overview summarizes the encountered issues and formulates suggestions to solve them in order to be able to provide a definitive answer to the title question: “do biofeedback and neurofeedback work as a cognitive performance enhancement method?”