Communications: directive radio wave systems and devices (e.g. – Synthetic aperture radar
Reexamination Certificate
2002-10-11
2004-03-30
Pihulic, Daniel T. (Department: 3662)
Communications: directive radio wave systems and devices (e.g.,
Synthetic aperture radar
C341S050000
Reexamination Certificate
active
06714154
ABSTRACT:
FIELD OF THE INVENTION
The present invention relates to compressing and decompressing data such as synthetic aperture radar data.
BACKGROUND OF THE INVENTION
Compression of Synthetic Aperture Radar (SAR) data may require that both magnitude and phase information be preserved.
FIG. 1
shows data processing of synthetic aperture radar data according to prior art. Synthetic aperture radar data
102
are typically collected in analog format by an antenna
101
and is converted to digital format through an Analog-to-Digital (A/D) converter
103
. The raw, unprocessed data are referred to as Video Phase History (VPH) data
104
, and comprise two components: In-phase (I) and Quadrature (Q). Video phase history data
104
having multiple components, such as I and Q, are typically referred as complex SAR data Complex SAR data are essential for the generation of complex SAR applications products such as interferograms, polarimetry, and coherent change detection, in which a plurality of such images must be processed and compared.
Video phase history data
104
are then passed through a Phase History Processor (PHP)
105
where data
104
are focused in both range (corresponding to a range focusing apparatus
107
) and azimuth (corresponding to an azimuth focusing apparatus
109
). The output of phase history processor
105
is referred to as Single Look Complex (SLC) data
110
. A detection function
111
processes SLC data
110
to form a detected image
112
.
Existing complex SAR sensors collect increasingly large amounts of data. Processing the complex data information and generating resultant imagery products may utilize four to eight times the memory storage and bandwidth that is required for the detected data (I&Q). In fact, some studies suggest exponential growth in associated data throughput over the next decade. However, sensors are typically associated with on-board processors that have limited processing and storage capabilities. Moreover, collected data are often transmitted to ground stations over a radio channel having a limited frequency bandwidth. Consequently, collected data may require compression in order to store or transmit collected data within resource capabilities of data collecting apparatus. Also, a SAR compression algorithm should be robust enough to compress both VPH data
104
and SLC SAR data
110
, should produce visually near-lossless magnitude image, and should cause minimal degradation in resultant products
112
.
Several compression algorithms have been proposed to compress SAR data. However, while such compression algorithms generally work quite well for magnitude imagery, the compression algorithms may not efficiently compress phase information. Moreover, the phase component may be more important in carrying information about a SAR signal than the magnitude component. With SAR data
102
, compression algorithms typically do not achieve compression ratios of more than ten to one without significant degradation of the phase information. Because many of the compression algorithms are typically designed for Electro/Optical (EO) imagery, the compression algorithms rely on high local data correlation to achieve good compression results and typically discard phase data prior to compression. Table 1 lists several compression algorithms discussed in the literature and provides a brief description of each.
TABLE 1
Popular Alternative SAR Data Compression Algorithms
Compression Algorithm
Description
Block Adaptive
Choice of onboard data compression
Quantization (BAQ)
methods due to simplicity in coding and
decoding hardware. Low compression
ratios achieved (<4:1).
Vector Quantization
Codebook created assigning a number for a
(VQ)
sequence of pixels. Awkward
implementation since considerable
complexity required in codebook
formulation.
Block Adaptive
Consists of first compressing data with
Vector Quantization
BAQ and then following up with VQ.
(BAVQ)
Similar to BAQ.
Karhunen-Loeve
Statistically optimal transform for
Transform (KLT)
providing uncorrelated coefficients;
however, computational cost is large.
Fast Fourier
2-D Fast Fourier Transform (FFT)
Transform BAQ
performed on raw SAR data. Before raw
(FFT-BAQ)
data is transformed, dynamic range for
each block is decreased using a BAQ.
Uniform Sampled
Emphasizes phase accuracy of selected
Quantization (USQ)
points.
Flexible BAQ (FBAQ)
Based on minimizing mean square error
between original and reconstructed data.
Trellis-Coded
Unique quantizer optimization design.
Quantization (TCQ)
Techniques provide superior signal to noise
ratio (SNR) performance to BAQ and VQ
for SAR.
Block Adaptive
BSAQ's adaptive technique provides some
Scalar Quantization
performance improvement.
(BSAQ)
Existing optical algorithms are inadequate for compressing complex multi-dimensional data, such as SAR data compression. For example with optical imagery, because of a human eyesight's natural high frequency roll-off, the high frequencies play a less important role than low frequencies. Also, optical imagery has high local correlation and the magnitude component is typically more important than the phase component. However, such characteristics may not be applicable to complex multi-dimensional data. Consequently, a method and apparatus that provides a large degree of compression without a significant degradation of the processed signal are beneficial in advancing the art in storing and transmitting complex multi-dimensional data Furthermore, the quality of the processed complex multi-dimensional data is not typically visually assessable. Thus, a means for evaluating the effects of compression on the resulting processed signal is beneficial to adjusting and to evaluating the compression process.
BRIEF SUMMARY OF THE INVENTION
The present invention provides methods and apparatus for compressing data comprising an In-phase (I) component and a Quadrature (Q) component. The compressed data may be saved into a memory or may be transmitted to a remote location for subsequent processing or storage. Statistical characteristics of the data are utilized to convert the data into a form that requires a reduced number of bits in accordance with its statistical characteristics. The data may be further compressed by transforming the data, as with a discrete cosine transform, and by modifying the transformed data in accordance with a quantization conversion table that is selected using a data type associated with the data. Additionally, a degree of redundancy may be removed from the processed data with an encoder. Subsequent processing of the compressed data may decompress the compressed data in order to approximate the original data by reversing the process for compressing the data with corresponding inverse operations.
In a first embodiment of the invention, data are compressed with an apparatus comprising a preprocessor, a transform module, a quantizer, an encoder, and a post-processor. The preprocessor separates the data into an I component and a Q component and bins each component according to statistical characteristics of the data. The transform module transforms the processed data into a discrete cosine transform that is quantized by the quantizer using a selected quantization conversion table. The encoder partially removes redundancy from the output of the quantizer using Huffman coding. The resulting data can be formatted by a post-processor for storage or transmittal. With a second embodiment, the preprocessor converts the I and Q components into amplitude and phase components and forms converted I and Q components.
Variations of the embodiment may use a subset of the apparatus modules of the first or the second embodiment. In a variation of the embodiment, the apparatus comprises a preprocessor, a transform module, and a quantizer.
REFERENCES:
patent: 4801939 (1989-01-01), Jones
patent: 5661477 (1997-08-01), Moreira et al.
S. A. Kuschel, B. Howlett, S. Wei and S. Werness, “ASARS-2 Complex Image Compression Studies Final Report”, ERIM, Mar. 1997.
G. Poggi, A. R. P. Ragozini, and L. Verdoliva, “Compression of SAR Data Through Rang
Cirillo Francis R.
Poehler Paul L.
Pihulic Daniel T.
Science Applications International Corporation
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