Search NASA⌕ Search

SEARCH · Search NASA

Results for “Pixels”

Search indexed NASA NTRS and DOE OSTI research on propulsion, heat transfer, battery materials and energy systems. Follow report and document links to the original sources.

Quote a phrase for an exact phrase match. Source license links do not imply unrestricted reuse.

At least 145 records · Page 8
All sourcesNASA NTRSDOE OSTI
NASA NTRS2017

Kepler Commissioning Data for Measurement of the Pixel Response Function and Focal Plane Geometry

This document describes the Kepler PRF/FPG data release. This data was taken on April 27-29, 2009, during Kepler's commissioning phase in order to measure the pixel response function (PRF) (Bryson et al., 2010a) and focal plane geometry (FPG) (Tenenbaum and Jenkins, 2010). 33,424 stellar targets were observed for 243 long cadences, each with a duration of 14.7 minutes (half the duration of a normal Kepler long cadence). During these 243 cadences the Kepler photometer was moved, pointing in a dither pattern to facilitate PRF measurement. Motion occurred during the even cadences (second, fourth, etc.), with the telescope in stable fine point at each pointing in the dither pattern during the odd cadences (first, third, etc.). The first and last cadences were at the center of the dither pattern. Motion cadences are included in this release, but they do not contain any data. For details on how this data was used to derive the Kepler PRF and FPG models, see Bryson et al. (2010a) and Tenenbaum and Jenkins (2010). Descriptions of the PRF and FPG models are found in Thompson et al. (2016), x2.3.5.17 and x2.3.5.16 respectively. The data in this release can be used to recompute the Kepler PRF and FPG. Such a reconstruction, however, would not reflect measured seasonal changes in the PRF described in Van Cleve et al. (2016b), x5.2.The dither pattern is shown in Figure 1. The crosses show the commanded pointings and the circles show the measured pointings. Measured pointings are different from the commanded pointings due to the early state of calibration of the fine guidance sensors during commissioning (Van Cleve et al., 2016a). The measured offsets from the center of the pattern are given in RADEC offsets and pixel offsets in Table 1. The order of the offsets was randomized during data collection to avoid time-dependent systematics.

Kepler↗
NASA NTRS2017

Nominal 30-M Cropland Extent Map of Continental Africa by Integrating Pixel-Based and Object-Based Algorithms Using Sentinel-2 and Landsat-8 Data on Google Earth Engine

A satellite-derived cropland extent map at high spatial resolution (30-m or better) is a must for food and water security analysis. Precise and accurate global cropland extent maps, indicating cropland and non-cropland areas, is a starting point to develop high-level products such as crop watering methods (irrigated or rainfed), cropping intensities (e.g., single, double, or continuous cropping), crop types, cropland fallows, as well as assessment of cropland productivity (productivity per unit of land), and crop water productivity (productivity per unit of water). Uncertainties associated with the cropland extent map have cascading effects on all higher-level cropland products. However, precise and accurate cropland extent maps at high spatial resolution over large areas (e.g., continents or the globe) are challenging to produce due to the small-holder dominant agricultural systems like those found in most of Africa and Asia. Cloud-based Geospatial computing platforms and multi-date, multi-sensor satellite image inventories on Google Earth Engine offer opportunities for mapping croplands with precision and accuracy over large areas that satisfy the requirements of broad range of applications. Such maps are expected to provide highly significant improvements compared to existing products, which tend to be coarser in resolution, and often fail to capture fragmented small-holder farms especially in regions with high dynamic change within and across years. To overcome these limitations, in this research we present an approach for cropland extent mapping at high spatial resolution (30-m or better) using the 10-day, 10 to 20-m, Sentinel-2 data in combination with 16-day, 30-m, Landsat-8 data on Google Earth Engine (GEE). First, nominal 30-m resolution satellite imagery composites were created from 36,924 scenes of Sentinel-2 and Landsat-8 images for the entire African continent in 2015-2016. These composites were generated using a median-mosaic of five bands (blue, green, red, near-infrared, NDVI) during each of the two periods (period 1: January-June 2016 and period 2: July-December 2015) plus a 30-m slope layer derived from the Shuttle Radar Topographic Mission (SRTM) elevation dataset. Second, we selected Cropland/Non-cropland training samples (sample size 9791) from various sources in GEE to create pixel-based classifications. As supervised classification algorithm, Random Forest (RF) was used as the primary classifier because of its efficiency, and when over-fitting issues of RF happened due to the noise of input training data, Support Vector Machine (SVM) was applied to compensate for such defects in specific areas. Third, the Recursive Hierarchical Segmentation (RHSeg) algorithm was employed to generate an object-oriented segmentation layer based on spectral and spatial properties from the same input data. This layer was merged with the pixel-based classification to improve segmentation accuracy. Accuracies of the merged 30-m crop extent product were computed using an error matrix approach in which 1754 independent validation samples were used. In addition, a comparison was performed with other available cropland maps as well as with LULC maps to show spatial similarity. Finally, the cropland area results derived from the map were compared with UN FAO statistics. The independent accuracy assessment showed a weighted overall accuracy of 94, with a producers accuracy of 85.9 (or omission error of 14.1), and users accuracy of 68.5 (commission error of 31.5) for the cropland class. The total net cropland area (TNCA) of Africa was estimated as 313 Mha for the nominal year 2015.

Cropland mapping; cropland areas; 30-m; Landsat-8;↗
NASA NTRS2019

No Pixel Left Behind: Interactively Visualizing ''Everything'' from NASA's Earth Observations

The problem: satellite swath overlaps. Polar orbiting satellites like Terra, Aqua, and the joint NASA/NOAA Suomi-NPP satellite circle the globe from pole to pole, collecting data daily, swath by swath. Having this density of data is great for building a comprehensive mosaic of the planet, but sometimes there is something interesting occurring in one swath but is covered by a subsequent swath when the satellite passes over 90 minutes later. With our new prototype based on Worldview and the Global Imagery Browse Services (GIBS), we combine the best of both worlds to interactively visualize the entire globe as a mosaic and allow the user to _peel away the overlaps_ to see every pixel that was observed by the satellite. This ability to look at every pixel - and to know when they were captured - is especially important near the poles where swath overlaps are most common.

Boller, R.↗
NASA NTRS2003

640 X 512 pixel long-wavelength infrared narrowband, multiband, and broadband QWIP focal plane arrays

A 640 512 pixel, long-wavelength cutoff, narrowband(delta lambda/lambda ~ 10%) quantum-well infrared photodetector(QWIP) focal plane array (FPA), a four-band QWIP FPA in the 4??m spectral region, and a broadband (delta lambda/lambda ~ 42%)QWIP FPA having a 15.4 m cutoff have been demonstrated. In this paper, we discuss the electrical and optical characterization of these FPAs, and their performance. In addition, we discuss the development of a very sensitive (NEDT ~ 10.6 mK) 640 X 512 pixel thermal imaging camera having a 9 lambda m cutoff.

broadband↗
NASA NTRS2017

4-Pixel Heterodyne Receiver at 1.9 THz using a CMOS Spectrometer

We present results from a 4x1-pixel 1.9 THz heterodyne array for the astrophysically important [CII] spectral line. A 4-pixel multiplier chain at 1.9 THz, with a compact spacing of 5 mm, is used as an LO. Receiver performance is verified by y-factor measurements with measured sensitivities of 900 K. Finally, using a coherent source near the target frequency of 1.9 THz, we down convert to baseband and measure spectra with both an IBOB and a new CMOS-based spectrometer. The CMOS spectrometer provides several advantages over more traditional spectrometers because of its compact size and low power consumption. This receiver prototype is easily scalable to other frequencies and larger focal plane arrays. Furthermore, because of its compact configuration, it can be easily packaged for orbital or sub-orbital missions.

Mehdi, Imran↗
NASA NTRS2023

A Multi-Pixel Split-Window Approach to Sea Surface Temperature Retrieval from Thermal Imagers with Relatively High Radiometric Noise: Preliminary Studies

In the following decade(s), a set of satellite missions carrying thermal infrared (TIR) imagers with a relatively high noise equivalent differential temperature (NEdT) are expected, e.g., the high resolution TIR imagers flying on the future Thermal infraRed Imaging Satellite for High-resolution Natural resource Assessment (TRISHNA), Land Surface Temperature Monitoring (LSTM) and NASA-JPL/ASI Surface Biology and Geology Thermal (SBG) missions or the secondary payload on board the ESA Earth Explorer 10 Harmony. The instruments on board these missions are expected to be characterized by an NEdT of ⪆0.1 K. In order to reduce the impact of radiometric noise on the retrieved sea surface temperature (SST), this study investigates the possibility of applying a multi-pixel atmospheric correction based on the hypotheses that (i) the spatial variability scales of radiatively active atmospheric variables are, on average, larger than those of the SST and (ii) the effect of atmosphere is accounted for via the split window (SW) difference. Based on 32 Sentinel 3 SLSTR case studies selected in oceanic regions where SST features are mainly driven by meso to sub-mesoscale turbulence (e.g., corresponding to major western boundary currents), this study documents that the local spatial variability of the SW difference term on the scale of ≃3 × 3 km2 is comparable with the noise associated with the SW difference. Similarly, the power spectra of the SW term are shown to have, for small scales, the behavior of white noise spectra. On this basis, we suggest to average the SW term and to use it for the atmospheric correction procedure to reduce the impact of radiometric noise. In principle, this methodology can be applied on proper scales that can be dynamically defined for each pixel. The applicability of our findings to high-resolution TIR missions is discussed and an example of an application to ECOSTRESS data is reported.

sea surface temperature↗
NASA NTRS1996

CMOS Active Pixel Sensors for Low Power, Highly Miniaturized Imaging Systems

The complementary metal-oxide-semiconductor (CMOS) active pixel sensor (APS) technology has been developed over the past three years by NASA at the Jet Propulsion Laboratory, and has reached a level of performance comparable to CCDs with greatly increased functionality but at a very reduced power level.

CMOS complementary metal-oxide-semiconductor activ↗
NASA NTRS1994

Development of CMOS Active Pixel Image Sensors for Low Cost Commercial Applications

This paper describes ongoing research and development of CMOS active pixel image sensors for low cost commercial applications. A number of sensor designs have been fabricated and tested in both p-well and n-well technologies. Major elements in the development of the sensor include on-chip analog signal processing circuits for the reduction of fixed pattern noise, on-chip timing and control circuits and on-chip analog-to-digital conversion (ADC). Recent results and continuing efforts in these areas will be presented.

active pixel image sensors CMOS signal processing ↗
NASA NTRS1994

Development of CMOS Active Pixel Image Sensors for Low Cost Commercial Applications

JPL, under sponsorship from the NASA Office of Advanced Concepts and Technology, has been developing a second-generation solid-state image sensor technology. Charge-coupled devices (CCD) are a well-established first generation image sensor technology. For both commercial and NASA applications, CCDs have numerous shortcomings. In response, the active pixel sensor (APS) technology has been under research. The major advantages of APS technology are the ability to integrate on-chip timing, control, signal-processing and analog-to-digital converter functions, reduced sensitivity to radiation effects, low power operation, and random access readout.

solid-state image sensors active pixel sensor APS ↗
NASA NTRS1996

Programmable Multiresolution CMOS Active Pixel Sensor

CMOS active pixel sensors (APS) allow the flexibility of placing signal processing circuitry on the imaging focal plane. The multiresolution CMOS APS can perform block averaging on-chip to eliminate the off-chip software intensive image processing. This 128 x 128 APS array can be read out at any user-defined resolution by configuring a set of digital shift registers. The resolution frame rate is 30 Hz with higher rates for all other image resolutions.

APS↗
NASA NTRS1997

Monolithic InGaAs JFET Active Pixel Tunable Image Sensor (MAPTIS)

A new Monolithic InGaAs Active Pixel Multispectral Image Sensor is described. This Infrared sensor will utilize high quality InGaAs grown by Molecular Beam Epitaxy on InP substrate for the fabrication of a high speed Junction Field Effect Transistor array.

hyper↗
NASA NTRS1996

256X256 CMOS Active Pixel Sensor Camera-on-a-Chip

A CMOS imaging sensor is described that uses active pixel sensor (APS) technology which permits the integration of the detector array with on-chip timing, control and signal chain electronics. This sensor has been used to implement a CMOS APS camera-on-a-chip.

active↗