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Timing technique enables recording of images in laser remote sensing of environment. Timings of laser pulse, shutter, and video frame synchronized. Technique also used in industrial or commercial processes requiring clear sighting of laser beams relative to their surroundings.
This paper reviews the activities at OKSI related to imaging spectroscopy presenting current and future applications of the technology. The authors discuss the development of several systems including hardware, signal processing, data classification algorithms and benchmarking techniques to determine algorithm performance. Signal processing for each application is tailored by incorporating the phenomenology appropriate to the process, into the algorithms. Pixel signatures are classified using techniques such as principal component analyses, generalized eigenvalue analysis and novel very fast neural network methods. The major hyperspectral imaging systems developed at OKSI include the Intelligent Missile Seeker (IMS) demonstration project for real-time target/decoy discrimination, and the Thermal InfraRed Imaging Spectrometer (TIRIS) for detection and tracking of toxic plumes and gases. In addition, systems for applications in medical photodiagnosis, manufacturing technology, and for crop monitoring are also under development.
Image analysis techniques applicable to remote sensing data and covering image models, feature detection, segmentation and classification, texture analysis, and matching are studied. Model types for characterizing images examined include random-field, mosaic, and facet models. Edge and corner detection as well as global extraction of linear features are discussed. Pixel clustering and classification are covered in addition to the regional approach to segmentation. Autocorrelation, second-order gray level probability density, and the use of primitive element statistics are discussed in relation to texture analysis. Finally, reducing the cost of (sub)imaging matching methods (e.g., pixelwise comparison of gray levels and normalized cross-correlation between two images) as well as improving match sharpness is considered.
The COxSwAIN project focuses on building an image and video compression scheme that can be implemented in a small or low-power satellite. To do this, we used Compressive Sensing, where the compression is performed by matrix multiplications on the satellite and reconstructed on the ground. Our paper explains our methodology and demonstrates the results of the scheme, being able to achieve high quality image compression that is robust to noise and corruption.
Short-term (sub-diurnal) biological and biogeochemical processes cannot be fully captured by the current suite of polar-orbiting satellite ocean color sensors, as their temporal resolution is limited to potentially one clear image per day. Geostationary sensors, such as the Geostationary Ocean Color Imager (GOCI) from the Republic of Korea, allow the study of these short-term processes because their orbit permit the collection of multiple images throughout each day for any area within the sensor’s field of regard. Assessing the capability to detect sub-diurnal changes in in-water properties caused by physical and biogeochemical processes characteristic of open ocean and coastal ocean ecosystems, however, requires an understanding of the uncertainties introduced by the instrument and/or geophysical retrieval algorithms. This work presents a study of the uncertainties during the daytime period for an ocean region with characteristically low-productivity with the assumption that only small and undetectable changes occur in the in-water properties due to biogeochemical processes during the daytime period. The complete GOCI mission data were processed using NASA’s SeaDAS/l2gen package. The assumption of homogeneity of the study region was tested using three-day sequences and diurnal statistics. This assumption was found to hold based on the minimal diurnal and day-to-day variability in GOCI data products. Relative differences with respect to the midday value were calculated for each hourly observation of the day in order to investigate what time of the day the variability is greater. Also, the influence of the solar zenith angle in the retrieval of remote sensing reflectances and derived products was examined. Finally, we determined that the uncertainties in water-leaving “remote-sensing” reflectance (Rrs) for the 412, 443, 490, 555, 660 and 680 nm bands on GOCI are 8.05 × 10−4, 5.49 × 10−4, 4.48 × 10−4, 2.51 × 10−4, 8.83 × 10−5, and 1.36 × 10−4 sr−1, respectively, and 1.09 × 10−2 mg m−3 for the chlorophyll-a concentration (Chl-a), 2.09 × 10−3 m−1 for the absorption coefficient of chromophoric dissolved organic matter at 412 nm (ag (412)), and 3.7 mg m−3 for particulate organic carbon (POC). These Rrs values can be considered the threshold values for detectable changes of the in-water properties due to biological, physical or biogeochemical processes from GOCI.
Short-term (sub-diurnal) biological and biogeochemical processes cannot be fully captured by the current suite of polar-orbiting satellite ocean color sensors, as their temporal resolution is limited to potentially one clear image per day. Geostationary sensors, such as the Geostationary Ocean Color Imager (GOCI) from the Republic of Korea, allow the study of these short-term processes because their orbit permit the collection of multiple images throughout each day for any area within the sensor’s field of regard. Assessing the capability to detect sub-diurnal changes in in-water properties caused by physical and biogeochemical processes characteristic of open ocean and coastal ocean ecosystems, however, requires an understanding of the uncertainties introduced by the instrument and/or geophysical retrieval algorithms. This work presents a study of the uncertainties during the daytime period for an ocean region with characteristically low-productivity with the assumption that only small and undetectable changes occur in the in-water properties due to biogeochemical processes during the daytime period. The complete GOCI mission data were processed using NASA’s SeaDAS/l2gen package. The assumption of homogeneity of the study region was tested using three-day sequences and diurnal statistics. This assumption was found to hold based on the minimal diurnal and day-to-day variability in GOCI data products. Relative differences with respect to the midday value were calculated for each hourly observation of the day in order to investigate what time of the day the variability is greater. Also, the influence of the solar zenith angle in the retrieval of remote sensing reflectances and derived products was examined. Finally, we determined that the uncertainties in water-leaving “remote-sensing” reflectance (Rrs) for the 412,443, 490, 555, 660 and 680 nm bands on GOCI are 8.05 x 10(exp -4), 5.49 x 10(exp -4), 4.48 x 10(exp -4), 2.51 x 10(exp -4), 8.83 x 10(exp -5), and 1.36 x 10(exp -4)/sr, respectively, and 1.09 x 10(exp -2)/cu.mgm for the chlorophyll-a concentration (Chl-a), 2.09 x 10(exp -3)/m for the absorption coefficient of chromophoric dissolved organic matter at 412 nm (a(sub g) (412)), and 3.7 mg/cu.m for particulate organic carbon (POC). These R(sub rs) values can be considered the threshold values for detectable changes of the in-water properties due to biological, physical or biogeochemical processes from GOCI.
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A remote sensing multispectral imaging instrument is being developed that uses a high resolution, fast programmable acoustooptic tunable filter (AOTF) as the spectral bandpass filter. A compact and fully computer controllable AOTF-based imaging spectrometer that operates in the visible wavelength range (0.5-0.8 microns) has been built and tested with success. A second imaging spectrometer operating in the near-infrared wavelength range (1.2-2.4 microns) is also under experimental investigation. The design criteria meeting various system issues, such as imaging quality, spectral response, and field of view (FOV), are discussed. An experiment using this AOTF imaging spectrometer breadboard is described.
A method of wavefront sensing (more precisely characterized as a method of determining the deviation of a wavefront from a nominal figure) has been invented as an improved means of assessing the performance of an optical system as affected by such imperfections as misalignments, design errors, and fabrication errors. The method is implemented by software running on a single-processor computer that is connected, via a suitable interface, to the image sensor (typically, a charge-coupled device) in the system under test. The software collects a digitized single image from the image sensor. The image is displayed on a computer monitor. The software directly solves for the wavefront in a time interval of a fraction of a second. A picture of the wavefront is displayed. The solution process involves, among other things, fast Fourier transforms. It has been reported to the effect that some measure of the wavefront is decomposed into modes of the optical system under test, but it has not been reported whether this decomposition is postprocessing of the solution or part of the solution process.
Digital images derived by scanning air photos and through acquiring aircraft and spcecraft scanner data were studied. Results show that spatial structure in scenes can be measured and logically related to texture and image variance. Imagery data were used of a South Dakota forest; a housing development in Canoga Park, California; an agricltural area in Mississppi, Louisiana, Kentucky, and Tennessee; the city of Washington, D.C.; and the Klamath National Forest. Local variance, measured as the average standard deviation of brightness values within a three-by-three moving window, reaches a peak at a resolution cell size about two-thirds to three-fourths the size of the objects within the scene. If objects are smaller than the resolution cell size of the image, this peak does not occur and local variance simply decreases with increasing resolution as spatial averaging occurs. Variograms can also reveal the size, shape, and density of objects in the scene.
Initial results of the novel remote earth sensing technique of imaging spectrometry, which is technically feasible from both spacecraft and aircraft platforms, indicate that the direct identification of surface materials on a picture-element basis is possible through proper sampling of absorption features in the reflectance spectrum. Sensors of this type are able to acquire images simultaneously in 100-200 contiguous spectral bands. Computerized data reduction and storage techniques are available for the large data sets thus generated, and novel analytic techniques are under development to maximize information content extraction.
In this paper, we present the experimental validation of a focus-diverse wavefront sensing algorithm with comparative interferometric measurments of a perturbed test mirror.
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The cover image incorporates high resolution stereo pairs acquired from the DigitalGlobe(R) QuickBird sensor. It shows a digital elevation model of Meteor Crater, Arizona at approximately 1.3 meter point-spacing. Image analysts used the Leica Photogrammetry Suite to produce the DEM. The outside portion was computed from two QuickBird panchromatic scenes acquired October 2006, while an Optech laser scan dataset was used for the crater s interior elevations. The crater s terrain model and image drape were created in a NASA Constellation Program project focused on simulating lunar surface environments for prototyping and testing lunar surface mission analysis and planning tools. This work exemplifies NASA s Scientific Data Purchase legacy and commercial high resolution imagery applications, as scientists use commercial high resolution data to examine lunar analog Earth landscapes for advanced planning and trade studies for future lunar surface activities. Other applications include landscape dynamics related to volcanism, hydrologic events, climate change, and ice movement.
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A multispectral airborne microwave radar imaging system, capable of obtaining four images simultaneously is described. The system has been successfully demonstrated in several experiments and one example of results obtained, fresh water ice, is given. Consideration of the digitization of the imagery is given and an image digitizing system described briefly. Preliminary results of digitization experiments are included.
Remote sensing and astronomical image formation is often complicated by deficiencies in measurement quality, density, or diversity. Penalized likelihood methods can incorporate additional first-principles physical prior knowledge and improve the image reconstructions, but a systematic bias is unavoidable as a consequence. This work derives theory to understand the bias and develops a computational tool to probe its effect on the reconstructed image and bound resolution limits. Though the focus is on image formation, the contributions of this paper apply to any inference problem that can be expressed under the linear state-space signal model.