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NASA NTRS2004

Space-Based Sensor Web for Earth Science Applications: An Integrated Architecture for Providing Societal Benefits

There is a significant interest in the Earth Science research and user remote sensing community to substantially increase the number of useful observations relative to the current frequency of collection. The obvious reason for such a push is to improve the temporal, spectral, and spatial coverage of the area(s) under investigation. However, there is little analysis available in terms of the benefits, costs and the optimal set of sensors needed to make the necessary observations. Classic observing system solutions may no longer be applicable because of their point design philosophy. Instead, a new intelligent data collection system paradigm employing both reactive and proactive measurement strategies with adaptability to the dynamics of the phenomena should be developed. This is a complex problem that should be carefully studied and balanced across various boundaries including: science, modeling, applications, and technology. Modeling plays a crucial role in making useful predictions about naturally occurring or human-induced phenomena In particular, modeling can serve to mitigate the potentially deleterious impacts a phenomenon may have on human life, property, and the economy. This is especially significant when one is interested in learning about the dynamics of, for example, the spread of forest fires, regional to large-scale air quality issues, the spread of the harmful invasive species, or the atmospheric transport of volcanic plumes and ash. This paper identifies and examines these challenging issues and presents architectural alternatives for an integrated sensor web to provide observing scenarios driving the requisite dynamic spatial, spectral, and temporal characteristics to address these key application areas. A special emphasis is placed on the observing systems and its operational aspects in serving the multiple users and stakeholders in providing societal benefits. We also address how such systems will take advantage of technological advancement in small spacecraft and emerging information technologies, and how sensor web options may be realized and made affordable. Specialized detector subsystems and precision flying techniques may still require substantial innovation, development time and cost: we have presented the considerations for these issues. Finally, data and information gathering and compression techniques are also briefly described.

Habib, Shahid↗

NASA NTRS1993

A browse facility for Earth science remote sensing data: Center director's discretionary fund final report

An image data visual browse facility is developed for a UNIX platform using the X Windows 11 system. It allows one to visually examine reduced resolution image data to determine which data are applicable for further research. Links with a relational data base manager then allow one to extract not only the full resolution image data, but any other ancillary data related to the case study. Various techniques are examined for compression of the image data in order to reduce data storage requirements and time necessary to transmit the data on the internet. Data used were from the WetNet project.

Meyer, P. J.↗

NASA NTRS1991

Techniques for containing error propagation in compression/decompression schemes

Data compression has the potential for increasing the risk of data loss. It can also cause bit error propagation, resulting in catastrophic failures. There are a number of approaches possible for containing error propagation due to data compression: (1) data retransmission; (2) data interpolation; (3) error containment; and (4) error correction. The most fruitful techniques will be ones where error containment and error correction are integrated with data compression to provide optimal performance for both. The error containment characteristics of existing compression schemes should be analyzed for their behavior under different data and error conditions. The error tolerance requirements of different data sets need to be understood, so guidelines can then be developed for matching error requirements to suitable compression algorithms.

Kobler, Ben↗

NASA NTRS1989

Ice-type classifications from airborne pulse-limited radar altimeter return waveform characteristics

During mid-March 1978, the NASA C-130 aircraft was deployed to Eielson Air Force Base in Fairbanks, Alaska, to make a series of flights over ice in the Beaufort Sea. The radar altimeter data analyzed were obtained northeast of Mackenzie Bay on March 14th in the vicinity of 69.9 deg N, 134.2 deg W. The data were obtained with a 13.9 GHz radar altimeter developed under the NASA Advanced Applications Flight Experiments (AAFE) Program. This airborne radar was built as a forerunner of the Seasat radar altimeter, and utilized the same pulse compression technique. Pulse-limited radar data taken with the altimeter from 1500-m altitude over sea ice are registered to high-quality photography. The backscattered power is statistically related the surface conductivity and to the number of facets whose surface normal is directed towards the radar. The variations of the radar return waveform shape and signal level are correlated with the variation of the ice type determined from photography. The AAFE altimeter has demonstrated that the return waveform shape and signal level of an airborne pulse-limited altimeter at 13.9 GHz respond to sea ice type. The signal level responded dramatically to even a very small fracture in the ice, as long as it occurred directly at the altimeter nadir point. Shear zones and regions of significant compression ridging consistently produced low signal levels. The return waveforms frequently evidenced the characteristics of both specular and diffuse scattering, and there was an indication that the power backscattered at 3 deg off-nadir in a shear zone was actually somewhat higher than that from nadir.

Fedor, L. S.↗

NASA NTRS1977

Status report: Data management program algorithm evaluation activity at Marshall Space Flight Center

An algorithm evaluation activity was initiated to study the problems associated with image processing by assessing the independent and interdependent effects of registration, compression, and classification techniques on LANDSAT data for several discipline applications. The objective of the activity was to make recommendations on selected applicable image processing algorithms in terms of accuracy, cost, and timeliness or to propose alternative ways of processing the data. As a means of accomplishing this objective, an Image Coding Panel was established. The conduct of the algorithm evaluation is described.

Jayroe, R. R., Jr.↗

NASA NTRS1993

LVQ and backpropagation neural networks applied to NASA SSME data

Feedfoward neural networks with backpropagation learning have been used as function approximators for modeling the space shuttle main engine (SSME) sensor signals. The modeling of these sensor signals is aimed at the development of a sensor fault detection system that can be used during ground test firings. The generalization capability of a neural network based function approximator depends on the training vectors which in this application may be derived from a number of SSME ground test-firings. This yields a large number of training vectors. Large training sets can cause the time required to train the network to be very large. Also, the network may not be able to generalize for large training sets. To reduce the size of the training sets, the SSME test-firing data is reduced using the learning vector quantization (LVQ) based technique. Different compression ratios were used to obtain compressed data in training the neural network model. The performance of the neural model trained using reduced sets of training patterns is presented and compared with the performance of the model trained using complete data. The LVQ can also be used as a function approximator. The performance of the LVQ as a function approximator using reduced training sets is presented and compared with the performance of the backpropagation network.

Doniere, Timothy F.↗

NASA NTRS1991

CRUSH: The NSI data compression utility

CRUSH is a data compression utility that provides the user with several lossless compression techniques available in a single application. It is intended that the future development of CRUSH will depend upon feedback from the user community to identify new features and capabilities desired by the users. CRUSH provides an extension to the UNIX Compress program and the various VMS implementations of Compress that many users are familiar with. An important capability added by CRUSH is the addition of additional compression techniques and the option of automatically determining the best technique for a given data file. The CRUSH software is written in C and is designed to run on both VMS and UNIX systems. VMS files that are compressed will regain their full file characteristics upon decompression. To the extent possible, compressed files can be transferred between VMS and UNIX systems, and thus be decompressed on a different system than they were compressed on. Version 1 of CRUSH is currently available. This version is a VAX VMS implementation. Version 2, which has the full range of capabilities for both VMS and UNIX implementations, will be available shortly.

Seiler, ED↗

NASA NTRS1977

Survey of adaptive image coding techniques

The general problem of image data compression is discussed briefly with attention given to the use of Karhunen-Loeve transforms, suboptimal systems, and block quantization. A survey is then conducted encompassing the four categories of adaptive systems: (1) adaptive transform coding (adaptive sampling, adaptive quantization, etc.), (2) adaptive predictive coding (adaptive delta modulation, adaptive DPCM encoding, etc.), (3) adaptive cluster coding (blob algorithms and the multispectral cluster coding technique), and (4) adaptive entropy coding.

Habibi, A.↗

NASA NTRS1991

Emerging standards for still image compression: A software implementation and simulation study

The software implementation is described of an emerging standard for the lossy compression of continuous tone still images. This software program can be used to compress planetary images and other 2-D instrument data. It provides a high compression image coding capability that preserves image fidelity at compression rates competitive or superior to most known techniques. This software implementation confirms the usefulness of such data compression and allows its performance to be compared with other schemes used in deep space missions and for data based storage.

Pollara, F.↗

NASA NTRS1996

Communications and information research: Improved space link performance via concatenated forward error correction coding

With the development of new advanced instruments for remote sensing applications, sensor data will be generated at a rate that not only requires increased onboard processing and storage capability, but imposes demands on the space to ground communication link and ground data management-communication system. Data compression and error control codes provide viable means to alleviate these demands. Two types of data compression have been studied by many researchers in the area of information theory: a lossless technique that guarantees full reconstruction of the data, and a lossy technique which generally gives higher data compaction ratio but incurs some distortion in the reconstructed data. To satisfy the many science disciplines which NASA supports, lossless data compression becomes a primary focus for the technology development. While transmitting the data obtained by any lossless data compression, it is very important to use some error-control code. For a long time, convolutional codes have been widely used in satellite telecommunications. To more efficiently transform the data obtained by the Rice algorithm, it is required to meet the a posteriori probability (APP) for each decoded bit. A relevant algorithm for this purpose has been proposed which minimizes the bit error probability in the decoding linear block and convolutional codes and meets the APP for each decoded bit. However, recent results on iterative decoding of 'Turbo codes', turn conventional wisdom on its head and suggest fundamentally new techniques. During the past several months of this research, the following approaches have been developed: (1) a new lossless data compression algorithm, which is much better than the extended Rice algorithm for various types of sensor data, (2) a new approach to determine the generalized Hamming weights of the algebraic-geometric codes defined by a large class of curves in high-dimensional spaces, (3) some efficient improved geometric Goppa codes for disk memory systems and high-speed mass memory systems, and (4) a tree based approach for data compression using dynamic programming.

Rao, T. R. N.↗

NASA NTRS1993

Cassini radio and plasma wave investigation - Data compression and scientific applications

The Radio and Plasma Wave Science (RPWS) experiment being built for the Cassini spacecraft will study a wide range of plasma and radio wave phenomena in the magnetosphere of Saturn and will also make valuable measurements during the cruise phase and at other encounters. A feature of data from wave receivers is the capability of producing vastly more data than the spacecraft telemetry link is capable of transmitting back to the Earth. Thus, techniques of on-board data compression and data reduction are important. The RPWS instrument has one processor dedicated to data compression tasks.

Woolliscroft, L. J. C.↗

NASA NTRS1993

Cassini radio and plasma wave investigation: Data compression and scientific applications

Woolliscroft, L. J. C.↗

NASA NTRS1982

Potential of multisensor data and strategies for data acquisition and analysis

Registration and simultaneous analysis of multisensor images is useful because the multiple data sets can be compressed through image processing techniques to facilitate interpretation. This also allows integration of other spatial data sets. Techniques being developed to analyze multisensor images involve comparison of image data with a library of attributes based on physical properties measured by each sensor. This results in the ability to characterize geologic units based on their similarity to the library attributes, as well as discriminate among them. Several studies can provide information on ways to optimize multisensor remote sensing. Continued analyses of the Death Valley and San Rafael Swell data sets can provide insight into tradeoffs in spectral and spatial resolutions of the various sensors used to obtain the coregistered data sets. These include imagery from LANDSAT, SEASAT, HCMM, SIR-A, 11-channel VIS-NIR, thermal inertia images, and aircraft L- and X-band radar.

Evans, D.↗

NASA NTRS1972

Multispectral data compression through transform coding and block quantization

Transform coding and block quantization techniques are applied to multispectral aircraft scanner data, and digitized satellite imagery. The multispectral source is defined and an appropriate mathematical model proposed. The Karhunen-Loeve, Fourier, and Hadamard encoders are considered and are compared to the rate distortion function for the equivalent Gaussian source and to the performance of the single sample PCM encoder.

Ready, P. J.↗

NASA NTRS1976

A study and evaluation of image analysis techniques applied to remotely sensed data

An analysis of phenomena causing nonlinearities in the transformation from Landsat multispectral scanner coordinates to ground coordinates is presented. Experimental results comparing rms errors at ground control points indicated a slight improvement when a nonlinear (8-parameter) transformation was used instead of an affine (6-parameter) transformation. Using a preliminary ground truth map of a test site in Alabama covering the Mobile Bay area and six Landsat images of the same scene, several classification methods were assessed. A methodology was developed for automatic change detection using classification/cluster maps. A coding scheme was employed for generation of change depiction maps indicating specific types of changes. Inter- and intraseasonal data of the Mobile Bay test area were compared to illustrate the method. A beginning was made in the study of data compression by applying a Karhunen-Loeve transform technique to a small section of the test data set. The second part of the report provides a formal documentation of the several programs developed for the analysis and assessments presented.

Atkinson, R. J.↗

NASA NTRS1993

Transform coding for space applications

Data compression coding requirements for aerospace applications differ somewhat from the compression requirements for entertainment systems. On the one hand, entertainment applications are bit rate driven with the goal of getting the best quality possible with a given bandwidth. Science applications are quality driven with the goal of getting the lowest bit rate for a given level of reconstruction quality. In the past, the required quality level has been nothing less than perfect allowing only the use of lossless compression methods (if that). With the advent of better, faster, cheaper missions, an opportunity has arisen for lossy data compression methods to find a use in science applications as requirements for perfect quality reconstruction runs into cost constraints. This paper presents a review of the data compression problem from the space application perspective. Transform coding techniques are described and some simple, integer transforms are presented. The application of these transforms to space-based data compression problems is discussed. Integer transforms have an advantage over conventional transforms in computational complexity. Space applications are different from broadcast or entertainment in that it is desirable to have a simple encoder (in space) and tolerate a more complicated decoder (on the ground) rather than vice versa. Energy compaction with new transforms are compared with the Walsh-Hadamard (WHT), Discrete Cosine (DCT), and Integer Cosine (ICT) transforms.

Glover, Daniel↗

NASA NTRS1994

Multiresolution representation and numerical algorithms: A brief review

In this paper we review recent developments in techniques to represent data in terms of its local scale components. These techniques enable us to obtain data compression by eliminating scale-coefficients which are sufficiently small. This capability for data compression can be used to reduce the cost of many numerical solution algorithms by either applying it to the numerical solution operator in order to get an approximate sparse representation, or by applying it to the numerical solution itself in order to reduce the number of quantities that need to be computed.

Harten, Amiram↗

NASA NTRS2023

Expanding the Deep Space Network to Support the Heliophysics System Observatory

Difficulties associated with receiving telemetry from satellites severely limit the volume of scientific data that can be downlinked to the ground. Current missions employ techniques such as compressing and pruning datasets to reduce the data volume they transmit. While existing mission designs are already restricted by limited telemetry budgets, future Heliophysics System Observatory missions will produce ever larger data volumes with higher resolution and cadence observations from constellations of satellites spread throughout the heliosphere1. In addition, heliophysics missions often produce data for the operational Space Weather community that requires a low latency between observation and downlink. In light of current limitations, the infrastructure to receive NASA satellite telemetry must be expanded and modernized to support the science needs of future data-rich heliophysics missions.

deep space network (DSN)↗