Abstract: A method and system for creating a high spatial resolution image from a multidimensional imagery is disclosed. The technique exploits an intrinsic spatial distortion of the sensor that acquired the imagery and uses it as additional information to increase spatial resolution of the imagery. The method comprises obtaining a baseline image from the multidimensional imagery; deriving n×m?1 sub-pixel shifted images from the multidimensional imagery, where n and m are spatial resolution increase factors in x and y directions respectively, integers and greater than 1; organizing the baseline image and the n×m?1 sub-pixel shifted images from the multidimensional imagery; fusing the organized images using iterative back projection (IBP) to generate a high resolution image; and outputting the generated high resolution image.

Abstract: A method and system for creating a high spatial resolution image from a multidimensional imagery is disclosed. The technique exploits an intrinsic spatial distortion of the sensor that acquired the imagery and uses it as additional information to increase spatial resolution of the imagery. The method comprises obtaining a baseline image from the multidimensional imagery; deriving n×m?1 sub-pixel shifted images from the multidimensional imagery, where n and m are spatial resolution increase factors in x and y directions respectively, integers and greater than 1; organizing the baseline image and the n×m?1 sub-pixel shifted images from the multidimensional imagery; fusing the organized images using iterative back projection (IBP) to generate a high resolution image; and outputting the generated high resolution image.

Abstract: Methods and systems for increasing the signal-to-noise ratio for satellite sensor data or signals, such as hyperspectral imageries (also referred to as datacubes due to their 3-dimensional nature). This is done by reducing the noise in the data or signals by first elevating the noise level temporarily for effective denoising. The denoising process is then performed in this condition and the noise level is then reversibly de-elevated after denoising. The denoising process comprises noise removal in both the spectral and the spatial domains. Once the denoising process is complete, the data is converted back from the spectral and spatial domains. Since this reconstruction process introduces errors, these errors are compensated for using the components from both the original data and denoised data filtered by the low pass filters.

Abstract: The present invention relates to a real-time wideband compressor for multi-dimensional data. The compressor comprises a plurality of compression engines for simultaneously compressing a plurality of data subsets of a set of input data vectors and providing compressed data thereof using one of SAMVQ or HSOCVQ data compression. Each compression engine comprises an along spectral vectors codevector trainer as well as an across spectral bands codevector trainer. The compression engines are programmable to perform either along spectral vectors codevector training or across spectral bands codevector training in combination with one of the SAMVQ or HSOCVQ techniques without changing hardware. The compressor further comprises a network switch for partitioning the set of input data vectors into the plurality of data subsets, for providing each of the plurality of data subsets to one of the plurality of compression engines, and for transmitting the compressed data.

Abstract: Methods and systems for increasing the signal-to-noise ratio for satellite sensor data or signals, such as hyperspectral imageries (also referred to as datacubes due to their 3-dimensional nature). This is done by reducing the noise in the data or signals by first elevating the noise level temporarily for effective denoising. The denoising process is then performed in this condition and the noise level is then reversibly de-elevated after denoising. The denoising process comprises noise removal in both the spectral and the spatial domains. Once the denoising process is complete, the data is converted back from the spectral and spatial domains. Since this reconstruction process introduces errors, these errors are compensated for using the components from both the original data and denoised data filtered by the low pass filters.

Abstract: Methods and systems for compressing a continuous data flow for numerous applications where it is necessary to process large data sets such as hyper-spectral data cubes in real-time. A predetermined number of 2D focal plane frames in a boundary area of a previous regional data cube close to a current regional data cube are included in a training set used for codevector training for the current region. Therefore, no artificial boundary occurs between the two adjacent regions when codevectors trained in this way are used for codebook generation and encoding of the spectral vectors of the current regional data cube. This process substantially reduces image artifacts between adjacent regions. A remedy for the single bit error problem is also provided. Full redundancy of compressed data for a regional data cube is obtained by combining a previous regional data cube and the current regional data cube for codebook training.

Abstract: Methods and systems for compressing a continuous data flow for numerous applications where it is necessary to process large data sets such as hyper-spectral data cubes in real-time. A predetermined number of 2D focal plane frames in a boundary area of a previous regional data cube close to a current regional data cube are included in a training set used for codevector training for the current region. Therefore, no artificial boundary occurs between the two adjacent regions when codevectors trained in this way are used for codebook generation and encoding of the spectral vectors of the current regional data cube. This process substantially reduces image artifacts between adjacent regions. A remedy for the single bit error problem is also provided. Full redundancy of compressed data for a regional data cube is obtained by combining a previous regional data cube and the current regional data cube for codebook training.

Abstract: The present invention relates to a method and system for compressing a continuous data flow in real-time based on lossy compression. In real-time data compression, a series of multi-dimensional data subsets acquired in a given period of time are treated as a regional data cube for the purpose of dividing a continuous series of data subsets into a plurality of data cubes. In a first embodiment implementation of parallel processing using a plurality of compression engines is facilitated by separating a data cube into a plurality of clusters comprising similar spectral vectors. By separating the data cube into clusters of similar spectral vectors no artificial spatial boundaries are introduced substantially improving image quality. Furthermore, the spectral vectors within a cluster are more easily compressed due to their similarity.

Abstract: The present invention relates to a real-time wideband compressor for multi-dimensional data. The compressor comprises a plurality of compression engines for simultaneously compressing a plurality of data subsets of a set of input data vectors and providing compressed data thereof using one of SAMVQ or HSOCVQ data compression. Each compression engine comprises an along spectral vectors codevector trainer as well as an across spectral bands codevector trainer. The compression engines are programmable to perform either along spectral vectors codevector training or across spectral bands codevector training in combination with one of the SAMVQ or HSOCVQ techniques without changing hardware. The compressor further comprises a network switch for partitioning the set of input data vectors into the plurality of data subsets, for providing each of the plurality of data subsets to one of the plurality of compression engines, and for transmitting the compressed data.

Abstract: The present invention relates to a real-time wideband compressor for multi-dimensional data. The compressor comprises a plurality of compression engines for simultaneously compressing a plurality of data subsets of a set of input data vectors and providing compressed data thereof using one of SAMVQ or HSOCVQ data compression. Each compression engine comprises an along spectral vectors codevector trainer as well as an across spectral bands codevector trainer. The compression engines are programmable to perform either along spectral vectors codevector training or across spectral bands codevector training in combination with one of the SAMVQ or HSOCVQ techniques without changing hardware. The compressor further comprises a network switch for partitioning the set of input data vectors into the plurality of data subsets, for providing each of the plurality of data subsets to one of the plurality of compression engines, and for transmitting the compressed data.

Abstract: The present invention relates to a real-time wideband compressor for multi-dimensional data. The compressor comprises a plurality of compression engines for simultaneously compressing a plurality of data subsets of a set of input data vectors and providing compressed data thereof using one of SAMVQ or HSOCVQ data compression. Each compression engine comprises an along spectral vectors codevector trainer as well as an across spectral bands codevector trainer. The compression engines are programmable to perform either along spectral vectors codevector training or across spectral bands codevector training in combination with one of the SAMVQ or HSOCVQ techniques without changing hardware. The compressor further comprises a network switch for partitioning the set of input data vectors into the plurality of data subsets, for providing each of the plurality of data subsets to one of the plurality of compression engines, and for transmitting the compressed data.

Abstract: The present invention relates to a method and system for compressing a continuous data flow in real-time based on lossy compression. In real-time data compression, a series of multi-dimensional data subsets acquired in a given period of time are treated as a regional data cube for the purpose of dividing a continuous series of data subsets into a plurality of data cubes. In a first embodiment implementation of parallel processing using a plurality of compression engines is facilitated by separating a data cube into a plurality of clusters comprising similar spectral vectors. By separating the data cube into clusters of similar spectral vectors no artificial spatial boundaries are introduced substantially improving image quality. Furthermore, the spectral vectors within a cluster are more easily compressed due to their similarity.

Abstract: The present invention relates to a method for compressing a continuous data flow based on lossy compression. In real-time data compression, a series of data subsets acquired in a given period of time are treated as a regional data cube for the purpose of dividing a continuous series of data subsets into a plurality of data cubes. Reuse of existing codevectors is important in achieving high compression performance. For encoding spectral vectors on a subset-by-subset basis in a current region two types of codevectors are used, codevectors that have been newly trained for previous data subsets in the current region and codevectors trained for the previous region. The problem of a visible spatial boundary between two adjacent regions after decompression is overcome by reusing the codevectors trained from a previous region to encode the spectral vectors in the current region in order to attain a seamless conjunction of the two adjacent regions.

Abstract: The present invention relates to a method of encoding a hyper-spectral image datacube using vector quantization. According to the invention, a temporary codebook having a small number, n, of codevectors is generated from the datacube. The datacube is processed using the temporary codebook to form n clusters (subsets) of vectors. A codevector corresponds to a cluster and is the centre of gravity for the cluster. In the compression process, vectors in each cluster are encoded by the corresponding codevector. Then the reconstruction fidelity of the encoded cluster is evaluated. When the fidelity of an encoded cluster is better than a predetermined fidelity, the codevector relating to that cluster is stored in a final codebook and the vectors in the cluster are expressed with the index (address) of the codevector in the final codebook. When the fidelity of an encoded cluster is not suitable, the cluster is reencoded with a new temporary codebook generated from this cluster, and the same process is repeated.

Abstract: The present method relates to a method for encoding image data using vector quantization. According to the invention, a small first codebook is determined. Each image vector of the image data is then encoded by determining a codevector within the first codebook that best approximates the image vector within the image data. A first index map is generated by replacing each image vector with an index indicative of the codevector's location within the first codebook. Then difference data are evaluated based on the original image data and the encoded image data. Each error vector of the difference data is then encoded using another small codebook. In another index map the error vectors are then replaced with an index indicative of the codevector's location within the other codebook. Evaluation of the error based on the difference data and the encoded difference data provides new difference data which is used to evaluate the fidelity of the approximation process performed for compression.

Abstract: The present invention relates to a method of viewing and processing hyper-spectral image data compressed using a VQ algorithm. According to the invention the data is compressed using a codebook of codevectors including binary spectral vectors, which allows processing of the compressed data and viewing of data within a datacube without expanding the compressed data into the complete datacube. In order to view an image derived from a datacube, for each pixel within the image a location within the datacube is selected, an index value from the index map at that location is retrieved, and a spectral value from a spectral vector within the codebook is retrieved for said pixel, the spectral vector identified by the retrieved index. A single spectral vector is easily viewed by displaying a spectral vector from the codebook.