Engineering topics
Publications and source records attributed to Kwok, R..
Digital image-processing system assembles maplike mosaic images of large areas of Earth from overlapping images of smaller areas generated by spaceborne synthetic-aperture radar (SAR). Automatically registers and blends smaller images with each other. Eliminates need for time-consuming, tedious manual processing. Modifiable to accept images from spaceborne optical sensors.
A discussion is presented of two algorithms to perform shape matching on the boundaries of ice floes in SAR (synthetic aperture radar) images in order to produce an ice motion map. The algorithms match a shape descriptor known as the psi-s curve. The first algorithm uses normalized correlation to match the psi-s curves, while the second uses dynamic programming to compute an elastic match that better accommodates deformation of the ice floe boundary.
The 1-3 km resolution Venera 15 and 16 images of Venus and the expected 120-300 m resolution Magellan mission image data are presently simulated through a digital processing of Seasat radar images covering a desert dune complex in the Gran Desierto of Sonora, accreted terranes in the central interior of Alaska, and the Appalachian Valley and Ridge Province. The simulations suggest that the nature and extent of terrain modification on Venus by such exogenic processes as atmosphere-surface weathering, erosion, and deposition, will remain uncertain, since the length scale of features indicative of such processes may be too small to be discerned from Venera data; Magellan data may provide this critical fine-scale morphological data, however, and thereby allow the testing of the two competing resurfacing scenarios.
An automated post-processing system has been developed for rectification and geocoding of SAR (Synthetic Aperture Radar) imagery. The system uses as input a raw uncorrected image from the operational SAR correlator, and produces as a standard output a rectified and geocoded product. The accurate geolocation of SAR image pixels is provided by a spatial transformation model which maps the slant range-azimuth SAR image pixels into their location on a prespecified map grid. This model predicts the geodetic location of each pixel by utilizing: the sensor platform position; a geoid model; the parameters of the data collection system and the processing parameters used in the SAR correlator. Based on their geodetic locations, the pixels are mapped by using the desired cartographic projection equations. This rectification and geocoding technique has been tested with Seasat and SIR-B images. The test results demonstrate absolute location uncertainty of less than 50 m and relative distortion (scale factor and skew) of less than 0.1 percent relative to local variations from the assumed geoid.
Five coding techniques in the spatial and transform domains have been evaluated for SAR image compression: linear three-point predictor (LTPP), block truncation coding (BTC), microadaptive picture sequencing (MAPS), adaptive discrete cosine transform (ADCT), and adaptive Hadamard transform (AHT). These techniques have been tested with Seasat data. Both LTPP and BTC spatial domain coding techniques provide very good performance at rates of 1-2 bits/pixel. The two transform techniques, ADCT and AHT, demonstrate the capability to compress the SAR imagery to less than 0.5 bits/pixel without visible artifacts. Tradeoffs such as the rate distortion performance, the computational complexity, the algorithm flexibility, and the controllability of compression ratios are also discussed.
This paper summarizes the investigation of SAR image data compression for an on-line archive data distribution system. This system is planned for the ground processing system of Alaska SAR Facility (ASF) and Shuttle Imaging Radar (SIR-C). The objective of the SAR image data compression is to enable the data archive system to provide the remote users a large data base with good image quality, short response time, low transfer cost, and minimal decoding complexity. The requirements and limitations of the on-line archive data distribution system are presented. The effects of SAR image data characteristics on data compression are addressed. The users' survey results suggest that compression ratios between 10:1 and 20:1 appear suitable. Based on the algorithm evaluation results, the two-level tree-searched vector quantization technique has been recommended as the SAR image data compression algorithm for the on-line archive data distribution system.