Search NASA⌕ Search

SEARCH · Search NASA

Results for “Terrain imagery”

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 163 records · Page 9
All sourcesNASA NTRSDOE OSTI
NASA NTRS2011

Unmanned Ground Vehicle Perception Using Thermal Infrared Cameras

The ability to perform off-road autonomous navigation at any time of day or night is a requirement for some unmanned ground vehicle (UGV) programs. Because there are times when it is desirable for military UGVs to operate without emitting strong, detectable electromagnetic signals, a passive only terrain perception mode of operation is also often a requirement. Thermal infrared (TIR) cameras can be used to provide day and night passive terrain perception. TIR cameras have a detector sensitive to either mid-wave infrared (MWIR) radiation (3-5?m) or long-wave infrared (LWIR) radiation (8-12?m). With the recent emergence of high-quality uncooled LWIR cameras, TIR cameras have become viable passive perception options for some UGV programs. The Jet Propulsion Laboratory (JPL) has used a stereo pair of TIR cameras under several UGV programs to perform stereo ranging, terrain mapping, tree-trunk detection, pedestrian detection, negative obstacle detection, and water detection based on object reflections. In addition, we have evaluated stereo range data at a variety of UGV speeds, evaluated dual-band TIR classification of soil, vegetation, and rock terrain types, analyzed 24 hour water and 12 hour mud TIR imagery, and analyzed TIR imagery for hazard detection through smoke. Since TIR cameras do not currently provide the resolution available from megapixel color cameras, a UGV's daytime safe speed is often reduced when using TIR instead of color cameras. In this paper, we summarize the UGV terrain perception work JPL has performed with TIR cameras over the last decade and describe a calibration target developed by General Dynamics Robotic Systems (GDRS) for TIR cameras and other sensors.

terrain classification↗
NASA NTRS1972

Facilitating the exploitation of ERTS imagery using snow enhancement techniques

The author has identified the following significant results. Analysis of all available (Gemini, Apollo, Nimbus, NASA aircraft) small scale snow covered imagery has been conducted to develop and refine snow enhancement techniques. A detailed photographic interpretation of ERTS-simulation imagery covering the Feather River/Lake Tahoe area was completed and the 580-680nm. band was determined to be the optimum band for fracture detection. ERTS-1 MSS bands 5 and 7 are best suited for detailed fracture mapping. The two bands should provide more complete fracture detail when utilized in combination. Analysis of early ERTS-1 data along with U-2 ERTS simulation imagery indicates that snow enhancement is a viable technique for geological fracture mapping. A wealth of fracture detail on snow-free terrain was noted during preliminary analysis of ERTS-1 images 1077-15005-6 and 7, 1077-15011-5 and 7, and 1079-15124-5 and 7. A direct comparison of data yield on snow-free versus snow-covered terrain will be conducted within these areas following receipt of snow-covered ERTS-1 imagery.

Wobber, F. J.↗
NASA NTRS1974

Tectonic analysis of Baja California and Parras shear belt in Mexico

The author has identified the following significant results. Geological correlation of terrain across the Gulf of California using ERTS-1 imagery revealed significant similarities between Isla Tiburon, Isla Angel de la Guarda, and the San Carlos Range in mainland Mexico. ERTS-1 imagery was used to check the validity of the existence of major trans-Baja fault zones. ERTS-1 imagery also shows that high albedo sediments similar to known late Tertiary marine sediments are widespread in southern and middle Baja and extend in places to the eastern side of the Peninsula. Major faults in northern Mexico and across the border in the United States were mapped, and ample evidence was found that the Parras and parts of the Texas lineament are belts of major transverse shear faults in areas outside the supposed limit of the Texas and Parras lineaments. A fundamental concept which may help explain many complexities in the tectonic development is beginning to emerge: The southwestern part of North America was torn by massive left-lateral shear of transverse trend (east-west) during the compressive stage of the late Mesozoic and early Cenozoic. This tectonic style has changed into tensional rifting (Basin and Range) and right-lateral shear later in the Cenozoic and Quaternary.

Abdel-Gawad, M.↗
NASA NTRS1978

Satellite geological and geophysical remote sensing of Iceland

The author has identified the following significant results. The ERTS imagery has sufficient resolution to map, from MSS color composites, areas of altered ground caused by high temperature geothermal activity at the Namafjall, Torfajokull, and Reykjanes geothermal areas. The major axes of the fallout pattern of tephra from the May - July 1970 volcanic eruption from Hekla Volcano can be mapped where sufficient depth of deposition was present to seriously affect the normal vegetation. Lava flows from the 1961 volcanic eruption at Askja; some of the lava flows from the 1947-48 eruption, and and all of the lava flows from the 1970 eruption at Hekla; and the areas covered by tephra and lava from the 1973 eruption on Heimaey could be delineated. Low sun angle imagery of less than 10 deg of snow covered terrain was particularly valuable in mapping structural and volcanic features concealed beneath glacial ice in the active volcanic zones of Iceland.

Williams, R. S., Jr.↗
NASA NTRS2006

Learning to predict slip for ground robots

In this paper we predict the amount of slip an exploration rover would experience using stereo imagery by learning from previous examples of traversing similar terrain. To do that, the information of terrain appearance and geometry regarding some location is correlated to the slip measured by the rover while this location is being traversed. This relationship is learned from previous experience, so slip can be predicted later at a distance from visual information only.

robots↗
NASA NTRS2009

Using Thermal Radiation in Detection of Negative Obstacles

A method of automated detection of negative obstacles (potholes, ditches, and the like) ahead of ground vehicles at night involves processing of imagery from thermal-infrared cameras aimed at the terrain ahead of the vehicles. The method is being developed as part of an overall obstacle-avoidance scheme for autonomous and semi-autonomous offroad robotic vehicles. The method could also be applied to help human drivers of cars and trucks avoid negative obstacles -- a development that may entail only modest additional cost inasmuch as some commercially available passenger cars are already equipped with infrared cameras as aids for nighttime operation.

Rankin, Arturo L.↗
NASA NTRS2024

Morphometric Characterization of Lunar Landing Sites

As ambitious surface exploration of the Moon commences in the 2020s, it is important to develop reliable and objective metrics for understanding the quality of future landing sites. A key prerequisite for any exploration and utilization of the lunar surface is a safe landing. One of the most important methods to provide understanding of potential metrics for landing site safety in the lunar context is the systematic comparison of candidate and historic landing sites. The goal of this project is to determine the morphometric parameters of the lunar surface at 16 successful lunar landing sites where adequate data exists to execute a quantitative comparison using various parameters. Quantitative analysis of Lunar Reconnaissance Orbiter (LRO) data should inform mission planning activities by providing morphologic metrics for landing sites including slope, Terrain Ruggedness Index (TRI), and rock abundance. These comparisons will assist mission planners and exploration scientists by providing “calibration points” for using Lunar Reconnaissance Orbiter (LRO) data to successfully plan and execute lunar powered descents in the future. The metrics included in this analysis describe the form of the terrain and can be calculated for any potential future landing site. Another objective of this project is to determine how the derived morphologic parameters for the same landing site change between common pixel scales. In ideal lighting conditions, 2m/px Narrow Angle Camera Digital Terrain Models (NAC DTMs) can be assembled using stereoscopic imagery from two or more concentric orbits of the LRO. However, due to low light conditions near the lunar poles, the highest quality data for many potential lunar landing sites comes from the Lunar Orbiter Laser Altimeter (LOLA). Therefore, we seek to assess how the derived slope and TRI values change as a function of changes in the DTM postings from 2m/px to 5m/px.

J M McCallion↗
NASA NTRSDate not supplied

MISR Level 1B2 Local Mode Terrain Radiance Data (MB2LMT_V2)

In addition to the Level 1B2 Global Mode imagery, MISR can be configured to disable the on-board data averaging and provide high resolution images in all 36 channels for selected targets and observation times. This capability is referred to as Local Mode (LM). [Temporal_Coverage: Start_Date=2000-02-24; Stop_Date=] [Spatial_Coverage: Southernmost_Latitude=-90; Northernmost_Latitude=90; Westernmost_Longitude=-180; Easternmost_Longitude=180] [Data_Resolution: Latitude_Resolution=275 m x 360 km (cross-track); Longitude_Resolution=275 m x 300 km (along-track)].

RADIANCE↗
NASA NTRS1983

Relative Elevation Determination from LANDSAT Imagery

In LANDSAT imagery, spectral and spatial information can be used to detect the drainage network as well as the relative elevation model in mountainous terrain. To do this, the mixed information of material reflectance and topographic modulation in the original LANDSAT imagery must be first separated. From the material reflectance information, big visible rivers can be detected. From the topographic modulation information, ridges and valleys can be detected and assigned relative elevations. Finally, a relative elevation model can be generated by interpolating values for nonridge and nonvalley pixels.

Haralick, R. M.↗
NASA NTRSDate not supplied

Building Maps for Terrain Relative Navigation Using Blender: An Open-Source Approach

A persistent challenge for vision-based navigation systems that compare imagery to a reference map is generating high quality maps with similar lighting conditions. Image rendering software can be used to apply variable lighting to reference maps or to generate synthetic imagery for test trajectories. While many image rendering software packages are available, with several developed specifically for spaceflight applications, there are often limitations due to cost, image fidelity, or flexibility. In this paper, we demonstrate the use of an open-source image rendering software, Blender, for use in Terrain Relative Navigation (TRN) applications. A scene in Blender was generated based on elevation data and satellite imagery of the region of West Texas used by Blue Origin for the operation of their New Shepard suborbital rocket. The Blender scene was validated by reproducing imagery collected during a flight of New Shepard in October 2020 and was further used to generate reference maps for use by a TRN algorithm on a subsequent New Shepard flight in August 2021. The work was performed under the NASA Safe and Precise Landing Integrated Capabilities Evolution (SPLICE) project, which is focused on technology advancement for precision landing and hazard avoidance. This work aims to lower the cost of entry and generally promote the adoption and advancement of vision-based navigation technologies.

Kyle W Smith↗
NASA NTRS1981

Spatial reasoning in remotely sensed data

Photointerpreters employ a variety of implicit spatial models to provide interpretations from remotely sensed aerial or satellite imagery. In this paper one application is illustrated: how ridges and valleys can be automatically interpreted from Landsat imagery of a mountainous area, and how a relative elevation terrain model can be constructed from this interpretation. How to examine valleys for the possible presence of streams or rivers is shown, and how a spatial relational model can be set up to make a final interpretation of the river drainage network is explored.

Campbell, J.↗
NASA NTRS1986

Orthographic terrain views using data derived from digital elevation models

A fast algorithm for producing three-dimensional orthographic terrain views uses digital elevation data and co-registered imagery. These views are created using projective geometry and are designed for display on high-resolution raster graphics devices. The algorithm's effectiveness is achieved by (1) the implementation of two efficient gray-level interpolation routines that offer the user a choice between speed and smoothness, and (2) a unique visible surface determination procedure based on horizon angles derived from the elevation data set.

Dubayah, R. O.↗
NASA NTRS1977

Preliminary geologic evaluation of L-band radar imagery: Arkansas test site

The relatively small angles of incidence (steep depression angles) of the L-band system provide minimal shadowing on terrain back-slopes and considerable foreshortening on terrain fore-slopes which sacrifice much of the topographic enhancement afforded by a more oblique angle of illumination. In addition, the dynamic range of the return from vegetated surfaces is substantially less for the L-band system, and many surface features defined primarily by subtle changes in vegetation are lost. In areas having terrain conditions similar to those of northern Arkansas, and where LANDSAT and shorter wavelength aircraft radar data are available, the value of the JPL L-band imagery as either a complimentary or supplementary geologic data source is not obvious.

Macdonald, H.↗
NASA NTRS2007

Processing Satellite Imagery To Detect Waste Tire Piles

A methodology for processing commercially available satellite spectral imagery has been developed to enable identification and mapping of waste tire piles in California. The California Integrated Waste Management Board initiated the project and provided funding for the method s development. The methodology includes the use of a combination of previously commercially available image-processing and georeferencing software used to develop a model that specifically distinguishes between tire piles and other objects. The methodology reduces the time that must be spent to initially survey a region for tire sites, thereby increasing inspectors and managers time available for remediation of the sites. Remediation is needed because millions of used tires are discarded every year, waste tire piles pose fire hazards, and mosquitoes often breed in water trapped in tires. It should be possible to adapt the methodology to regions outside California by modifying some of the algorithms implemented in the software to account for geographic differences in spectral characteristics associated with terrain and climate. The task of identifying tire piles in satellite imagery is uniquely challenging because of their low reflectance levels: Tires tend to be spectrally confused with shadows and deep water, both of which reflect little light to satellite-borne imaging systems. In this methodology, the challenge is met, in part, by use of software that implements the Tire Identification from Reflectance (TIRe) model. The development of the TIRe model included incorporation of lessons learned in previous research on the detection and mapping of tire piles by use of manual/ visual and/or computational analysis of aerial and satellite imagery. The TIRe model is a computational model for identifying tire piles and discriminating between tire piles and other objects. The input to the TIRe model is the georeferenced but otherwise raw satellite spectral images of a geographic region to be surveyed. The TIRe model identifies the darkest objects in the images and, on the basis of spatial and spectral image characteristics, discriminates against other dark objects, which can include vegetation, some bodies of water, and dark soils. The TIRe model can identify piles of as few as 100 tires. The output of the TIRe model is a binary mask showing areas containing suspected tire piles and spectrally similar features. This mask is overlaid on the original satellite imagery and examined by a trained image analyst, who strives to further discriminate against non-tire objects that the TIRe model tentatively identified as tire piles. After the analyst has made adjustments, the mask is used to create a synoptic, geographically accurate tire-pile survey map, which can be overlaid with a road map and/or any other map or set of georeferenced data, according to a customer s preferences.

Skiles, Joseph↗