Satellite on-board processing for earth resources data

Results of a survey of earth resources user applications and their data requirements, earth resources multispectral scanner sensor technology, and preprocessing algorithms for correcting the sensor outputs and for data bulk reduction are presented along with a candidate data format. Computational requirements required to implement the data analysis algorithms are included along with a review of computer architectures and organizations. Computer architectures capable of handling the algorithm computational requirements are suggested and the environmental effects of an on-board processor discussed. By relating performance parameters to the system requirements of each of the user requirements the feasibility of on-board processing is determined for each user. A tradeoff analysis is performed to determine the sensitivity of results to each of the system parameters. Significant results and conclusions are discussed, and recommendations are presented

Processing techniques development, volume 3. Part 2: Data preprocessing and information extraction techniques

There are no author-identified significant results in this report

Development of land based radar polarimeter processor system

The processing subsystem of a land based radar polarimeter was designed and constructed. This subsystem is labeled the remote data acquisition and distribution system (RDADS). The radar polarimeter, an experimental remote sensor, incorporates the RDADS to control all operations of the sensor. The RDADS uses industrial standard components including an 8-bit microprocessor based single board computer, analog input/output boards, a dynamic random access memory board, and power supplis. A high-speed digital electronics board was specially designed and constructed to control range-gating for the radar. A complete system of software programs was developed to operate the RDADS. The software uses a powerful real time, multi-tasking, executive package as an operating system. The hardware and software used in the RDADS are detailed. Future system improvements are recommended

Analysis and preliminary design of optical sensors for propulsion control

A fiber-optic sensor concept screening study was performed. Twenty sensor subsystems were identified and evaluated. Two concepts selected for further study were the Fabry-Perot fiber-optic temperature sensor and the pulse-width-modulated phosphorescent temperature sensor. Various designs suitable for a Fabry-Perot temperature sensor to be used as a remote fiber-optic transducer were investigated. As a result, a particular design was selected and constructed. Tests on this device show that spectral peaks are produced from visible white light, and the change in wavelength of the spectral peaks produced by a change in temperature is consistent with theory and is 36 nm/C for the first order peak. A literature search to determine a suitable phosphor for implementing the pulse-width-modulated fiber optic temperature sensor was conducted. This search indicated that such a device could be made to function for temperatures up to approximately 200 C. Materials like ZnCdS and ZnSe activated with copper will be particularly applicable to temperature sensing in the cryogenic to room temperature region. While this sensing concept is probably not applicable to jet engines, the simplicity and potential reliability make the concept highly desirable for other applications

Recent advancements in information extraction methodology and hardware for Earth Resources Survey Systems

There are no author-identified significant results in this report

MIDAS prototype Multispectral Interactive Digital Analysis System for large area earth resources surveys. Volume 2: Charge coupled device investigation

MIDAS is a third-generation, fast, low cost, multispectral recognition system able to keep pace with the large quantity and high rates of data acquisition from large regions with present and projected sensors. MIDAS, for example, can process a complete ERTS frame in forty seconds and provide a color map of sixteen constituent categories in a few minutes. A principal objective of the MIDAS Program is to provide a system well interfaced with the human operator and thus to obtain large overall reductions in turn-around time and significant gains in throughput. The need for advanced onboard spacecraft processing of remotely sensed data is stated and approaches to this problem are described which are feasible through the use of charge coupled devices. Tentative mechanizations for the required processing operations are given in large block form. These initial designs can serve as a guide to circuit/system designers

Radar systems for the water resources mission, volume 2

The application of synthetic aperture radar (SAR) in monitoring and managing earth resources was examined. The function of spaceborne radar is to provide maps and map imagery to be used for earth resource and oceanographic applications. Spaceborne radar has the capability of mapping the entire United States regardless of inclement weather; however, the imagery must have a high degree of resolution to be meaningful. Attaining this resolution is possible with the SAR system. Imagery of the required quality must first meet mission parameters in the following areas: antenna patterns, azimuth and range ambiguities, coverage, and angle of incidence

GPU-Accelerated Algorithms for Compressed Signals Recovery with Application to Astronomical Imagery Deblurring

Compressive sensing promises to enable bandwidth-efficient on-board compression of astronomical data by lifting the encoding complexity from the source to the receiver. The signal is recovered off-line, exploiting GPUs parallel computation capabilities to speedup the reconstruction process. However, inherent GPU hardware constraints limit the size of the recoverable signal and the speedup practically achievable. In this work, we design parallel algorithms that exploit the properties of circulant matrices for efficient GPU-accelerated sparse signals recovery. Our approach reduces the memory requirements, allowing us to recover very large signals with limited memory. In addition, it achieves a tenfold signal recovery speedup thanks to ad-hoc parallelization of matrix-vector multiplications and matrix inversions. Finally, we practically demonstrate our algorithms in a typical application of circulant matrices: deblurring a sparse astronomical image in the compressed domain