Engineering Papers⌕ Search

SEARCH · Engineering Papers

Results for “Image analysis”

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 217 records · Page 12
All sourcesNASA NTRSDOE OSTI
NASA NTRS2015

Thirty Years of Change in Subalpine Forest Cover from Landsat Image Analysis in the Sierra Nevada Mountains of California

Landsat imagery was analyzed to understand changes in subalpine forest stands since the mid-1980s in the Sierra-Nevada region of California. At locations where long-term plot measurements have shown that stands are becoming denser in the number of small tree stems (compared to the early 1930s), the 30-year analysis of Landsat greenness index (NDVI) indicated that no consistent increases in canopy leaf cover have occurred at these same locations since the mid-1980s. Interannual variations in stand NDVI closely followed snow accumulation amounts recorded at nearby stations. In contrast, at eastern Sierra whitebark pine stand locations where it has been observed that widespread tree mortality has occurred, decreasing NDVI trends over the past 5-10 years were consistent with rapid loss of forest canopy cover. Landsat imagery was further analyzed to understand patterns of post-wildfire vegetation recovery, focusing on high burn severity (HBS) patches within burned areas dating from the late 1940s. Analysis of landscape metrics showed that the percentage of total HBS area comprised by the largest patch of recovered woody cover was relatively small in all fires that occurred since 1995, but increased rapidly with time since fire. Patch complexity of recovered woody cover decreased notably after more than 50 years of regrowth, but was not readily associated with time for fires that occurred since the mid 1990s. The aggregation level of patches with recovery of woody cover increased steadily with time since fire. The study approach using satellite remote sensing can be expanded to assess the consequences of stand-replacing wildfires in all forests of the region.

Fires↗
NASA NTRS2014

Ten Years of Forest Cover Change in the Sierra Nevada Detected Using Landsat Satellite Image Analysis

A detailed geographic record of recent vegetation regrowth and disturbance patterns in forests of the Sierra Nevada remains a gap that can be filled with remote sensing data. Landsat (TM) imagery was analyzed to detect 10 years of recent changes (between 2000 and 2009) in forest vegetation cover for areas burned by wildfires between years of 1995 to 1999 in the region. Results confirmed the prevalence of regrowing forest vegetation during the period 2000 and 2009 over 17% of the combined burned areas.

Landsat satellites↗
NASA NTRSDate not supplied

3d Particle Geometry Characterization of Apollo Regolith Samples Via Laser Diffraction and Dynamic Image Analysis

Establishing a fundamental understanding of regolith mechanics hinges on the characterization of the foundational physical parameters that dictate granular particle interactions both on micro- and macro-scales. Specifically, particle size distribution, particle geometry and grain density govern the geotechnical properties, including compressibility, shear strength, hydraulic conductivity (i.e., permeability), void ratio (i.e., porosity), and thermal conductivity of the lunar regolith. The absence of terrestrial geologic processes on the lunar surface, a lack of water, clay minerals, and organic material in lunar soils, and the narrow compositional range in terms of mineral diversity of the regolith produces a unique problem when attempting to project terrestrial soil mechanics concepts to the lunar surface. Additionally, the occurrence of agglutinates, exclusive to the lunar surface, further disassociates the physical behavior (e.g., particle crushability, flowability, etc.) of regolith from that observed in terrestrial soils.

R. Kovtun↗
DOE OSTI2020

Critical Infrastructure Classification via CNN-based Modeling and Image Analysis

With recent advances in the fields of satellite imagery and machine learning we now have the ability to develop explainable deep learning models that enhance critical infrastructure analysis. Funded through Idaho National Laboratory’s (INL) Laboratory Directed Research and Development (LDRD) office we are in the process of developing a deep learning model capable of identifying critical infrastructure facilities and embedded features within those facilities. Utilizing current limit of practice techniques in the machine learning areas of explainability and transfer learning our model, once complete, will have the capacity to be used on multiple different imagery data sets and produce results that not only classify critical infrastructure facilities, but also explain why a critical infrastructure facility was classified as a certain type of facility. These advancements eliminate the ‘black box’ approach deep learning models have had in the past, where a user will have to trust the conclusion of a model without understanding what reasoning when into the model’s classification process. They also expand a model’s usefulness, traditionally a deep learning model will have to use the same data set it was originally trained on. Given the long training times of deep learning models this is impractical in a number of scenarios. By utilizing transfer learning advancements, we are eliminating the need to train our model on the same data set it is then run on to classify critical infrastructure facilities. We are also enabling the analysis and classification of data sets that are potentially too small to be divided into a training and testing data set. Once completed this model can provide a foundation to enhanced critical infrastructure analysis, dependency analysis, and potential disaster relief efforts.

97 MATHEMATICS AND COMPUTING↗
NASA NTRS1988

Image processing software for imaging spectrometry data analysis

Imaging spectrometers simultaneously collect image data in hundreds of spectral channels, from the near-UV to the IR, and can thereby provide direct surface materials identification by means resembling laboratory reflectance spectroscopy. Attention is presently given to a software system, the Spectral Analysis Manager (SPAM) for the analysis of imaging spectrometer data. SPAM requires only modest computational resources and is composed of one main routine and a set of subroutine libraries. Additions and modifications are relatively easy, and special-purpose algorithms have been incorporated that are tailored to geological applications.

Mazer, Alan↗
NASA NTRS1992

Three-dimensional temporal reconstruction and analysis of plume images

An experiment with two subsonic jets generating a cross-flow was conducted as part of a study of the structural features of temporal reconstruction of plume images. The flow field structure was made visible using a direct injection flow visualization technique. It is shown that image analysis and temporal three-dimensional visualization can provide new information on the vortical structural dynamics of multiple jets in a cross-flow. It is expected that future developments in image analysis, quantification and interpretation, and flow visualization of rocket engine plume images may provide a tool for correlating the engine diagnostic features by interpreting the evolution of the structures in the plume.

Dhawan, Atam P.↗
NASA NTRS1975

Remote sensor digital image data analysis using the General Electric Image 100 analysis system (a study of analysis speed, cost, and performance)

The author has identified the following significant results. It was found that the high speed man machine interaction capability is a distinct advantage of the image 100; however, the small size of the digital computer in the system is a definite limitation. The system can be highly useful in an analysis mode in which it complements a large general purpose computer. The image 100 was found to be extremely valuable in the analysis of aircraft MSS data where the spatial resolution begins to approach photographic quality and the analyst can exercise interpretation judgements and readily interact with the machine.

Mcmurtry, G. J.↗
NASA NTRS1991

Four dimensional reconstruction and analysis of plume images

Results of a time-history based three-dimensional reconstruction of cross-sectional images corresponding to a specific planar location of the jet structure are reported. The experimental set-up is described, and three-dimensional displays of time-history based reconstruction of the jet structure are presented. Future developments in image analysis, quantification and interpretation, and flow visualization of rocket engine plume images are expected to provide a tool for correlating engine diagnostic features with visible flow structures.

Dhawan, Atam P.↗
NASA NTRS1991

Analysis of simulated image sequences from sensors for restricted-visibility operations

A real time model of the visible output from a 94 GHz sensor, based on a radiometric simulation of the sensor, was developed. A sequence of images as seen from an aircraft as it approaches for landing was simulated using this model. Thirty frames from this sequence of 200 x 200 pixel images were analyzed to identify and track objects in the image using the Cantata image processing package within the visual programming environment provided by the Khoros software system. The image analysis operations are described.

Kasturi, Rangachar↗