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GIFTS SM EDU Level 1B Algorithms

The Geosynchronous Imaging Fourier Transform Spectrometer (GIFTS) SensorModule (SM) Engineering Demonstration Unit (EDU) is a high resolution spectral imager designed to measure infrared (IR) radiances using a Fourier transform spectrometer (FTS). The GIFTS instrument employs three focal plane arrays (FPAs), which gather measurements across the long-wave IR (LWIR), short/mid-wave IR (SMWIR), and visible spectral bands. The raw interferogram measurements are radiometrically and spectrally calibrated to produce radiance spectra, which are further processed to obtain atmospheric profiles via retrieval algorithms. This paper describes the GIFTS SM EDU Level 1B algorithms involved in the calibration. The GIFTS Level 1B calibration procedures can be subdivided into four blocks. In the first block, the measured raw interferograms are first corrected for the detector nonlinearity distortion, followed by the complex filtering and decimation procedure. In the second block, a phase correction algorithm is applied to the filtered and decimated complex interferograms. The resulting imaginary part of the spectrum contains only the noise component of the uncorrected spectrum. Additional random noise reduction can be accomplished by applying a spectral smoothing routine to the phase-corrected spectrum. The phase correction and spectral smoothing operations are performed on a set of interferogram scans for both ambient and hot blackbody references. To continue with the calibration, we compute the spectral responsivity based on the previous results, from which, the calibrated ambient blackbody (ABB), hot blackbody (HBB), and scene spectra can be obtained. We now can estimate the noise equivalent spectral radiance (NESR) from the calibrated ABB and HBB spectra. The correction schemes that compensate for the fore-optics offsets and off-axis effects are also implemented. In the third block, we developed an efficient method of generating pixel performance assessments. In addition, a random pixel selection scheme is designed based on the pixel performance evaluation. Finally, in the fourth block, the single pixel algorithms are applied to the entire FPA.

Tian, Jialin↗

Digital Signal Processing Techniques for the GIFTS SM EDU

The Geosynchronous Imaging Fourier Transform Spectrometer (GIFTS) Sensor Module (SM) Engineering Demonstration Unit (EDU) is a high resolution spectral imager designed to measure infrared (IR) radiance using a Fourier transform spectrometer (FTS). The GIFTS instrument employs three Focal Plane Arrays (FPAs), which gather measurements across the long-wave IR (LWIR), short/mid-wave IR (SMWIR), and visible spectral bands. The raw interferogram measurements are radiometrically and spectrally calibrated to produce radiance spectra, which are further processed to obtain atmospheric profiles via retrieval algorithms. This paper describes several digital signal processing (DSP) techniques involved in the development of the calibration model. In the first stage, the measured raw interferograms must undergo a series of processing steps that include filtering, decimation, and detector nonlinearity correction. The digital filtering is achieved by employing a linear-phase even-length FIR complex filter that is designed based on the optimum equiripple criteria. Next, the detector nonlinearity effect is compensated for using a set of pre-determined detector response characteristics. In the next stage, a phase correction algorithm is applied to the decimated interferograms. This is accomplished by first estimating the phase function from the spectral phase response of the windowed interferogram, and then correcting the entire interferogram based on the estimated phase function. In the calibration stage, we first compute the spectral responsivity based on the previous results and the ideal Planck blackbody spectra at the given temperatures, from which, the calibrated ambient blackbody (ABB), hot blackbody (HBB), and scene spectra can be obtained. In the post-calibration stage, we estimate the Noise Equivalent Spectral Radiance (NESR) from the calibrated ABB and HBB spectra. The NESR is generally considered as a measure of the instrument noise performance, and can be estimated as the standard deviation of calibrated radiance spectra from multiple scans. To obtain an estimate of the FPA performance, we developed an efficient method of generating pixel performance assessments. In addition, a random pixel selection scheme is developed based on the pixel performance evaluation. This would allow us to perform the calibration procedures on a random pixel population that is a good statistical representation of the entire FPA. The design and implementation of each individual component will be discussed in details.

Tian, Jialin↗

Using Satellite Data to Characterize the Temporal Thermal Behavior of an Active Volcano: Mount St. Helens, WA

ASTER thermal infrared data over Mt. St Helens were used to characterize its thermal behavior from Jun 2000 to Feb 2006. Prior to the Oct 2004 eruption, the average crater temperature varied seasonally between -12 and 6 C. After the eruption, maximum single-pixel temperature increased from 10 C (Oct 2004) to 96 C (Aug 2005), then showed a decrease to Feb 2006. The initial increase in temperature was correlated with dome morphology and growth rate and the subsequent decrease was interpreted to relate to both seasonal trends and a decreased growth rate/increased cooling rate, possibly suggesting a significant change in the volcanic system. A single-pixel ASTER thermal anomaly first appeared on Oct 1, 2004, eleven hours after the first eruption - 10 days before new lava was exposed at the surface. By contrast, an automated algorithm for detecting thermal anomalies in MODIS data did not trigger an alert until Dec 18. However, a single-pixel thermal anomaly first appeared in MODIS channel 23 (4 um) on Oct 13, 12 days after the first eruption - 2 days after lava was exposed. The earlier thermal anomaly detected with ASTER data is attributed to the higher spatial resolution (90 m) compared with MODIS (1 m) and the earlier visual observation of anomalous pixels compared to the automated detection method suggests that local spatial statistics and background radiance data could improve automated detection methods.

Advanced Spaceborne Thermal Emission and Reflectio↗

Event-driven charge-coupled device design and applications therefor

An event-driven X-ray CCD imager device uses a floating-gate amplifier or other non-destructive readout device to non-destructively sense a charge level in a charge packet associated with a pixel. The output of the floating-gate amplifier is used to identify each pixel that has a charge level above a predetermined threshold. If the charge level is above a predetermined threshold the charge in the triggering charge packet and in the charge packets from neighboring pixels need to be measured accurately. A charge delay register is included in the event-driven X-ray CCD imager device to enable recovery of the charge packets from neighboring pixels for accurate measurement. When a charge packet reaches the end of the charge delay register, control logic either dumps the charge packet, or steers the charge packet to a charge FIFO to preserve it if the charge packet is determined to be a packet that needs accurate measurement. A floating-diffusion amplifier or other low-noise output stage device, which converts charge level to a voltage level with high precision, provides final measurement of the charge packets. The voltage level is eventually digitized by a high linearity ADC.

Doty, John P.↗

High speed CMOS imager with motion artifact supression and anti-blooming

An image sensor includes pixels formed on a semiconductor substrate. Each pixel includes a photoactive region in the semiconductor substrate, a sense node, and a power supply node. A first electrode is disposed near a surface of the semiconductor substrate. A bias signal on the first electrode sets a potential in a region of the semiconductor substrate between the photoactive region and the sense node. A second electrode is disposed near the surface of the semiconductor substrate. A bias signal on the second electrode sets a potential in a region of the semiconductor substrate between the photoactive region and the power supply node. The image sensor includes a controller that causes bias signals to be provided to the electrodes so that photocharges generated in the photoactive region are accumulated in the photoactive region during a pixel integration period, the accumulated photocharges are transferred to the sense node during a charge transfer period, and photocharges generated in the photoactive region are transferred to the power supply node during a third period without passing through the sense node. The imager can operate at high shutter speeds with simultaneous integration of pixels in the array. High quality images can be produced free from motion artifacts. High quantum efficiency, good blooming control, low dark current, low noise and low image lag can be obtained.

Pain, Bedabrata↗

Image sensor with motion artifact supression and anti-blooming

An image sensor includes pixels formed on a semiconductor substrate. Each pixel includes a photoactive region in the semiconductor substrate, a sense node, and a power supply node. A first electrode is disposed near a surface of the semiconductor substrate. A bias signal on the first electrode sets a potential in a region of the semiconductor substrate between the photoactive region and the sense node. A second electrode is disposed near the surface of the semiconductor substrate. A bias signal on the second electrode sets a potential in a region of the semiconductor substrate between the photoactive region and the power supply node. The image sensor includes a controller that causes bias signals to be provided to the electrodes so that photocharges generated in the photoactive region are accumulated in the photoactive region during a pixel integration period, the accumulated photocharges are transferred to the sense node during a charge transfer period, and photocharges generated in the photoactive region are transferred to the power supply node during a third period without passing through the sense node. The imager can operate at high shutter speeds with simultaneous integration of pixels in the array. High quality images can be produced free from motion artifacts. High quantum efficiency, good blooming control, low dark current, low noise and low image lag can be obtained.

Pain, Bedabrata↗

The Impact of Soil Reflectance on the Quantification of the Green Vegetation Fraction from NDVI

The green vegetation fraction (Fg) is an important climate and hydrologic model parameter. A common method to calculate Fg is to create a simple linear mixing rnodeP between two NDVI endmembers: bare soil NDVI (NDVI(sub o)) and full vegetation NDVI (NDVI(sub infinity)). Usually it is assumed that NDVI(sub o), is close to zero (NDVI(sub o) approx.-0.05) and is generally chosen from the lowest observed NDVI values. However, the mean soil NDVI computed from 2906 samples is much larger (NDVI=0.2) and is highly variable (standard deviation=O. 1). We show that the underestimation of NDVI(sub o) yields overestimations of Fg. The largest errors occur in grassland and shrubland areas. Using parameters for NDVI(sub o) and NDVI(sub infinity) derived from global scenes yields overestimations of Fg ((Delta) Fg*) that are larger than 0.2 for the majority of U.S. land cover types when pixel NDVI values are 0.2<NDVI(sub pixel)<0.4. Figure 1 shows how the Fg overestimation varies for the most common land cover types in the conterminous U.S. for typical seasonal NDVI values. When using conterminous U.S. scenes to derive NDV(sub o) and NDVI(sub infinity), the overestimation is less (0.10-0.17 for 0.2<NDVI(sub pixel)<0.4). As a result, parts of the conterminous U.S. are affected at different times of the year depending on the local seasonal NDVI cycle. We propose using global databases of NDVI(sub o) along with information on historical NDVI(sub pixel) values to compute a statistically most-likely estimate of Fg (Fg*). Using in situ measurements made at the Sevilleta LTER, we show that this approach yields better estimates of Fg than using global invariant NDVI(sub o) values estimated from whole scenes (Figure 2). At the two studied sites, the Fg estimate was adjusted by 52% at the grassland and 86% at the shrubland. More significant advances will require information on spatial distribution of soil reflectance.

Montandon, L. M.↗

Array-scale performance of TES X-ray Calorimeters Suitable for Constellation-X

Having developed a transition-edge-sensor (TES) calorimeter design that enables high spectral resolution in high fill-factor arrays, we now present array-scale results from 32-pixel arrays of identical closely packed TES pixels. Each pixel in such an array contains a Mo/Au bilayer with a transition temperature of 0.1 K and an electroplated Au or Au/Bi xray absorber. The pixels in an array have highly uniform physical characteristics and performance. The arrays are easy to operate due to the range of bias voltages and heatsink temperatures over which solution better than 3 eV at 6 keV can be obtained. Resolution better than 3 eV has also been obtained with 2x8 time-division SQUID multiplexing. We will present the detector characteristics and show spectra acquired through the read-out chain from the multiplexer electronics through the demultiplexer software to real-time signal processing. We are working towards demonstrating this performance over the range of count rates expected in the observing program of the Constellation-X observatory. We mill discuss the impact of increased counting rate on spectral resolution, including the effects of crosstalk and optimal-filtering dead time.

Kilbourne, C. A.↗

Vision System Measures Motions of Robot and External Objects

A prototype of an advanced robotic vision system both (1) measures its own motion with respect to a stationary background and (2) detects other moving objects and estimates their motions, all by use of visual cues. Like some prior robotic and other optoelectronic vision systems, this system is based partly on concepts of optical flow and visual odometry. Whereas prior optoelectronic visual-odometry systems have been limited to frame rates of no more than 1 Hz, a visual-odometry subsystem that is part of this system operates at a frame rate of 60 to 200 Hz, given optical-flow estimates. The overall system operates at an effective frame rate of 12 Hz. Moreover, unlike prior machine-vision systems for detecting motions of external objects, this system need not remain stationary: it can detect such motions while it is moving (even vibrating). The system includes a stereoscopic pair of cameras mounted on a moving robot. The outputs of the cameras are digitized, then processed to extract positions and velocities. The initial image-data-processing functions of this system are the same as those of some prior systems: Stereoscopy is used to compute three-dimensional (3D) positions for all pixels in the camera images. For each pixel of each image, optical flow between successive image frames is used to compute the two-dimensional (2D) apparent relative translational motion of the point transverse to the line of sight of the camera. The challenge in designing this system was to provide for utilization of the 3D information from stereoscopy in conjunction with the 2D information from optical flow to distinguish between motion of the camera pair and motions of external objects, compute the motion of the camera pair in all six degrees of translational and rotational freedom, and robustly estimate the motions of external objects, all in real time. To meet this challenge, the system is designed to perform the following image-data-processing functions: The visual-odometry subsystem (the subsystem that estimates the motion of the camera pair relative to the stationary background) utilizes the 3D information from stereoscopy and the 2D information from optical flow. It computes the relationship between the 3D and 2D motions and uses a least-mean-squares technique to estimate motion parameters. The least-mean-squares technique is suitable for real-time implementation when the number of external-moving-object pixels is smaller than the number of stationary-background pixels.

Talukder, Ashit↗

SOI CMOS Imager with Suppression of Cross-Talk

A monolithic silicon-on-insulator (SOI) complementary metal oxide/semiconductor (CMOS) image-detecting integrated circuit of the active-pixel-sensor type, now undergoing development, is designed to operate at visible and near-infrared wavelengths and to offer a combination of high quantum efficiency and low diffusion and capacitive cross-talk among pixels. The imager is designed to be especially suitable for astronomical and astrophysical applications. The imager design could also readily be adapted to general scientific, biological, medical, and spectroscopic applications. One of the conditions needed to ensure both high quantum efficiency and low diffusion cross-talk is a relatively high reverse bias potential (between about 20 and about 50 V) on the photodiode in each pixel. Heretofore, a major obstacle to realization of this condition in a monolithic integrated circuit has been posed by the fact that the required high reverse bias on the photodiode is incompatible with metal oxide/semiconductor field-effect transistors (MOSFETs) in the CMOS pixel readout circuitry. In the imager now being developed, the SOI structure is utilized to overcome this obstacle: The handle wafer is retained and the photodiode is formed in the handle wafer. The MOSFETs are formed on the SOI layer, which is separated from the handle wafer by a buried oxide layer. The electrical isolation provided by the buried oxide layer makes it possible to bias the MOSFETs at CMOS-compatible potentials (between 0 and 3 V), while biasing the photodiode at the required higher potential, and enables independent optimization of the sensory and readout portions of the imager.

Pain, Bedabrata↗

Nano-Multiplication-Region Avalanche Photodiodes and Arrays

Nano-multiplication-region avalanche photodiodes (NAPDs), and imaging arrays of NAPDs integrated with complementary metal oxide/semiconductor (CMOS) active-pixel-sensor integrated circuitry, are being developed for applications in which there are requirements for high-sensitivity (including photoncounting) detection and imaging at wavelengths from about 250 to 950 nm. With respect to sensitivity and to such other characteristics as speed, geometric array format, radiation hardness, power demand of associated circuitry, size, weight, and robustness, NAPDs and arrays thereof are expected to be superior to prior photodetectors and arrays including CMOS active-pixel sensors (APSs), charge-coupled devices (CCDs), traditional APDs, and microchannelplate/ CCD combinations. Figure 1 depicts a conceptual NAPD array, integrated with APS circuitry, fabricated on a thick silicon-on-insulator wafer (SOI). Figure 2 presents selected aspects of the structure of a typical single pixel, which would include a metal oxide/semiconductor field-effect transistor (MOSFET) integrated with the NAPD. The NAPDs would reside in silicon islands formed on the buried oxide (BOX) layer of the SOI wafer. The silicon islands would be surrounded by oxide-filled insulation trenches, which, together with the BOX layer, would constitute an oxide embedding structure. There would be two kinds of silicon islands: NAPD islands for the NAPDs and MOSFET islands for in-pixel and global CMOS circuits. Typically, the silicon islands would be made between 5 and 10 m thick, but, if necessary, the thickness could be chosen outside this range. The side walls of the silicon islands would be heavily doped with electron-acceptor impurities (p+-doped) to form anodes for the photodiodes and guard layers for the MOSFETs. A nanoscale reach-through structure at the front (top in the figures) central position of each NAPD island would contain the APD multiplication region. Typically, the reach-through structure would be about 0.1 microns in diameter and between 0.3 and 0.4 nm high. The top layer in the reach-through structure would be heavily doped with electron-donor impurities (n+-doped) to make it act as a cathode. A layer beneath the cathode, between 0.1 and 0.2 nm thick, would be p-doped to a concentration .10(exp 17)cu cm. A thin n+-doped polysilicon pad would be formed on the top of the cathode to protect the cathode against erosion during a metal-silicon alloying step that would be part of the process of fabricating the array.

Zheng, Xinyu↗

Fabricating Radial Groove Gratings Using Projection Photolithography

Projection photolithography has been used as a fabrication method for radial grove gratings. Use of photolithographic method for diffraction grating fabrication represents the most significant breakthrough in grating technology in the last 60 years, since the introduction of holographic written gratings. Unlike traditional methods utilized for grating fabrication, this method has the advantage of producing complex diffractive groove contours that can be designed at pixel-by-pixel level, with pixel size currently at the level of 45 45 nm. Typical placement accuracy of the grating pixels is 10 nm over 30 nm. It is far superior to holographic, mechanically ruled or direct e-beam written gratings and results in high spatial coherence and low spectral cross-talk. Due to the smooth surface produced by reactive ion etch, such gratings have a low level of randomly scattered light. Also, due to high fidelity and good surface roughness, this method is ideally suited for fabrication of radial groove gratings. The projection mask is created using a laser writer. A single crystal silicon wafer is coated with photoresist, and then the projection mask, with its layer of photoresist, is exposed for patterning in a stepper or scanner. To develop the photoresist, the fabricator either removes the exposed areas (positive resist) of the unexposed areas (negative resist). Next, the patterned and developed photoresist silicon substrate is subjected to reactive ion etching. After this step, the substrate is cleaned. The projection mask is fabricated according to electronic design files that may be generated in GDS file format using any suitable CAD (computer-aided design) or other software program. Radial groove gratings in off-axis grazing angle of incidence mount are of special interest for x-ray spectroscopy, as they allow achieving higher spectral resolution for the same grating area and have lower alignment tolerances than traditional in-plane grating scheme. This is especially critical for NASA Constellation- X project that will utilize hundreds of gratings all of which need to be precisely aligned for x-ray observation of space.

Iazikov, Dmitri↗

Mosaic-Detector-Based Fluorescence Spectral Imager

A battery-powered, pen-sized, portable instrument for measuring molecular fluorescence spectra of chemical and biological samples in the field has been proposed. Molecular fluorescence spectroscopy is among the techniques used most frequently in laboratories to analyze compositions of chemical and biological samples. Heretofore, it has been possible to measure fluorescence spectra of molecular species at relative concentrations as low as parts per billion (ppb), with a few nm spectral resolution. The proposed instrument would include a planar array (mosaic) of detectors, onto which a fluorescence spectrum would be spatially mapped. Unlike in the larger laboratory-type molecular fluorescence spectrometers, mapping of wavelengths to spatial positions would be accomplished without use of relatively bulky optical parts. The proposed instrument is expected to be sensitive enough to enable measurement of spectra of chemical species at relative concentrations <1 ppb, with spectral resolution that could be tailored by design to be comparable to a laboratory molecular fluorescence spectrometer. The proposed instrument (see figure) would include a button-cell battery and a laser diode, which would generate the monochromatic ultraviolet light needed to excite fluorescence in a sample. The sample would be held in a cell bounded by far-ultraviolet-transparent quartz or optical glass. The detector array would be, more specifically, a complementary metal oxide/ semiconductor or charge-coupled- device imaging photodetector array, the photodetectors of which would be tailored to respond to light in the wavelength range of the fluorescence spectrum to be measured. The light-input face of the photodetector array would be covered with a matching checkerboard array of multilayer thin film interference filters, such that each pixel in the array would be sensitive only to light in a spectral band narrow enough so as not to overlap significantly with the band of an adjacent pixel. The wavelength interval between adjacent pixels (and, thus, the spectral resolution) would typically be chosen by design to be approximately equal to the width of the total fluorescence wavelength range of interest divided by the number of pixels. The unitary structure comprising the photodetector array overlaid with the matching filter array would be denoted a hyperspectral mosaic detector (HMD) array.

Son, Kyung-Ah↗

Infrared Camera System for Visualization of IR-Absorbing Gas Leaks

Leak detection and location remain a common problem in NASA and industry, where gas leaks can create hazardous conditions if not quickly detected and corrected. In order to help rectify this problem, this design equips an infrared (IR) camera with the means to make gas leaks of IR-absorbing gases more visible for leak detection and location. By comparing the output of two IR cameras (or two pictures from the same camera under essentially identical conditions and very closely spaced in time) on a pixel-by-pixel basis, one can cancel out all but the desired variations that correspond to the IR absorption of the gas of interest. This can be simply done by absorbing the IR lines that correspond to the gas of interest from the radiation received by one of the cameras by the intervention of a filter that removes the particular wavelength of interest from the "reference" picture. This can be done most sensitively with a gas filter (filled with the gas of interest) placed in front of the IR detector array, or (less sensitively) by use of a suitable line filter in the same location. This arrangement would then be balanced against the unfiltered "measurement" picture, which will have variations from IR absorption from the gas of interest. By suitable processing of the signals from each pixel in the two IR pictures, the user can display only the differences in the signals. Either a difference or a ratio output of the two signals is feasible. From a gas concentration viewpoint, the ratio could be processed to show the column depth of the gas leak. If a variation in the background IR light intensity is present in the field of view, then large changes in the difference signal will occur for the same gas column concentration between the background and the camera. By ratioing the outputs, the same signal ratio is obtained for both high- and low-background signals, even though the low-signal areas may have greater noise content due to their smaller signal strength. Thus, one embodiment would use a ratioed output signal to better represent the gas column concentration. An alternative approach uses a simpler multiplication of the filtered signal to make the filtered signal equal to the unfiltered signal at most locations, followed by a subtraction to remove all but the wavelength-specific absorption in the unfiltered sample. This signal processing can also reveal the net difference signal representing the leaking gas absorption, and allow rapid leak location, but signal intensity would not relate solely to gas absorption, as raw signal intensity would also affect the displayed signal. A second design choice is whether to use one camera with two images closely spaced in time, or two cameras with essentially the same view and time. The figure shows the two-camera version. This choice involves many tradeoffs that are not apparent until some detailed testing is done. In short, the tradeoffs involve the temporal changes in the field picture versus the pixel sensitivity curves and frame alignment differences with two cameras, and which system would lead to the smaller variations from the uncontrolled variables.

Youngquist, Robert↗

Visual Object Recognition and Tracking of Tools

A method has been created to automatically build an algorithm off-line, using computer-aided design (CAD) models, and to apply this at runtime. The object type is discriminated, and the position and orientation are identified. This system can work with a single image and can provide improved performance using multiple images provided from videos. The spatial processing unit uses three stages: (1) segmentation; (2) initial type, pose, and geometry (ITPG) estimation; and (3) refined type, pose, and geometry (RTPG) calculation. The image segmentation module files all the tools in an image and isolates them from the background. For this, the system uses edge-detection and thresholding to find the pixels that are part of a tool. After the pixels are identified, nearby pixels are grouped into blobs. These blobs represent the potential tools in the image and are the product of the segmentation algorithm. The second module uses matched filtering (or template matching). This approach is used for condensing synthetic images using an image subspace that captures key information. Three degrees of orientation, three degrees of position, and any number of degrees of freedom in geometry change are included. To do this, a template-matching framework is applied. This framework uses an off-line system for calculating template images, measurement images, and the measurements of the template images. These results are used online to match segmented tools against the templates. The final module is the RTPG processor. Its role is to find the exact states of the tools given initial conditions provided by the ITPG module. The requirement that the initial conditions exist allows this module to make use of a local search (whereas the ITPG module had global scope). To perform the local search, 3D model matching is used, where a synthetic image of the object is created and compared to the sensed data. The availability of low-cost PC graphics hardware allows rapid creation of synthetic images. In this approach, a function of orientation, distance, and articulation is defined as a metric on the difference between the captured image and a synthetic image with an object in the given orientation, distance, and articulation. The synthetic image is created using a model that is looked up in an object-model database. A composable software architecture is used for implementation. Video is first preprocessed to remove sensor anomalies (like dead pixels), and then is processed sequentially by a prioritized list of tracker-identifiers.

English, James↗

Multiple Spectral-Spatial Classification Approach for Hyperspectral Data

A .new multiple classifier approach for spectral-spatial classification of hyperspectral images is proposed. Several classifiers are used independently to classify an image. For every pixel, if all the classifiers have assigned this pixel to the same class, the pixel is kept as a marker, i.e., a seed of the spatial region, with the corresponding class label. We propose to use spectral-spatial classifiers at the preliminary step of the marker selection procedure, each of them combining the results of a pixel-wise classification and a segmentation map. Different segmentation methods based on dissimilar principles lead to different classification results. Furthermore, a minimum spanning forest is built, where each tree is rooted on a classification -driven marker and forms a region in the spectral -spatial classification: map. Experimental results are presented for two hyperspectral airborne images. The proposed method significantly improves classification accuracies, when compared to previously proposed classification techniques.

Tarabalka, Yuliya↗

Ambiguity of Quality in Remote Sensing Data

This slide presentation reviews some of the issues in quality of remote sensing data. Data "quality" is used in several different contexts in remote sensing data, with quite different meanings. At the pixel level, quality typically refers to a quality control process exercised by the processing algorithm, not an explicit declaration of accuracy or precision. File level quality is usually a statistical summary of the pixel-level quality but is of doubtful use for scenes covering large areal extents. Quality at the dataset or product level, on the other hand, usually refers to how accurately the dataset is believed to represent the physical quantities it purports to measure. This assessment often bears but an indirect relationship at best to pixel level quality. In addition to ambiguity at different levels of granularity, ambiguity is endemic within levels. Pixel-level quality terms vary widely, as do recommendations for use of these flags. At the dataset/product level, quality for low-resolution gridded products is often extrapolated from validation campaigns using high spatial resolution swath data, a suspect practice at best. Making use of quality at all levels is complicated by the dependence on application needs. We will present examples of the various meanings of quality in remote sensing data and possible ways forward toward a more unified and usable quality framework.

Lynnes, Christopher↗

Miniaturized Airborne Imaging Central Server System

In recent years, some remote-sensing applications require advanced airborne multi-sensor systems to provide high performance reflective and emissive spectral imaging measurement rapidly over large areas. The key or unique problem of characteristics is associated with a black box back-end system that operates a suite of cutting-edge imaging sensors to collect simultaneously the high throughput reflective and emissive spectral imaging data with precision georeference. This back-end system needs to be portable, easy-to-use, and reliable with advanced onboard processing. The innovation of the black box backend is a miniaturized airborne imaging central server system (MAICSS). MAICSS integrates a complex embedded system of systems with dedicated power and signal electronic circuits inside to serve a suite of configurable cutting-edge electro- optical (EO), long-wave infrared (LWIR), and medium-wave infrared (MWIR) cameras, a hyperspectral imaging scanner, and a GPS and inertial measurement unit (IMU) for atmospheric and surface remote sensing. Its compatible sensor packages include NASA s 1,024 1,024 pixel LWIR quantum well infrared photodetector (QWIP) imager; a 60.5 megapixel BuckEye EO camera; and a fast (e.g. 200+ scanlines/s) and wide swath-width (e.g., 1,920+ pixels) CCD/InGaAs imager-based visible/near infrared reflectance (VNIR) and shortwave infrared (SWIR) imaging spectrometer. MAICSS records continuous precision georeferenced and time-tagged multisensor throughputs to mass storage devices at a high aggregate rate, typically 60 MB/s for its LWIR/EO payload. MAICSS is a complete stand-alone imaging server instrument with an easy-to-use software package for either autonomous data collection or interactive airborne operation. Advanced multisensor data acquisition and onboard processing software features have been implemented for MAICSS. With the onboard processing for real time image development, correction, histogram-equalization, compression, georeference, and data organization, fast aerial imaging applications, including the real time LWIR image mosaic for Google Earth, have been realized for NASA fs LWIR QWIP instrument. MAICSS is a significant improvement and miniaturization of current multisensor technologies. Structurally, it has a complete modular and solid-state design. Without rotating hard drives and other moving parts, it is operational at high altitudes and survivable in high-vibration environments. It is assembled from a suite of miniaturized, precision-machined, standardized, and stackable interchangeable embedded instrument modules. These stackable modules can be bolted together with the interconnection wires inside for the maximal simplicity and portability. Multiple modules are electronically interconnected as stacked. Alternatively, these dedicated modules can be flexibly distributed to fit the space constraints of a flying vehicle. As a flexibly configurable system, MAICSS can be tailored to interface a variety of multisensor packages. For example, with a 1,024x1,024 pixel LWIR and a 8,984x6,732 pixel EO payload, the complete MAICSS volume is approximately 7x9x11 in. (=18x23x28 cm), with a weight of 25 lb (=11.4 kg).

Sun, Xiuhong↗