Constrained and unconstrained algorithms of the multisensor multiresolution technique (MMT) are discussed. They can be applied to unmix low-resolution images using the information about their pixel composition from co-registered high-resolution images. This makes it possible to fuse the low- and high-resolution images for a synergetic interpretation. The constrained unmixing preserves all the available radiometric information of the low-resolution image. On the other hand, the unconstrained unmixing may be preferable in case of noisy data. An analysis of the MMT sensitivity to sensor errors showed that the strongest requirement is the accuracy of geometric co-registration of the data; the co-registration errors should not exceed 0.1-0.2 of the low-resolution pixel size. Applications of the constrained and unconstrained algorithms are illustrated on examples of unmixing and fusion of the multiresolution reflective and thermal bands of a real TM/LANDSAT image as well as of a simulated image of the future ASTER/EOS-AMI sensor.

Unmixing-based multisensor multiresolution image fusion

Priroda microwave radiometer satellite observations were acquired and analysed in conjunction with soil moisture and other important ground and meteorological observations. Priroda was a remote sensing module on the MIR Space Station. The experiment was a component of the Southern Great Plains 1997 (SGP97) hydrology experiment. Problems with the MIR platform during the experiment resulted in a very limited set of data. Priroda sensor performance and some analyses of soil moisture conditions were conducted. These results provide important information for utilizing Priroda data and for soil moisture algorithm development.

Priroda microwave radiometer observations in the Southern Great Plains 1997 hydrology experiment

Constrained and unconstrained algorithms of the multisensor multiresolution technique (MMT) are discussed. They can be applied to unmix low-resolution images using the information about their pixel composition from co-registered high-resolution images. This makes it possible to fuse the low- and high-resolution images for a synergetic interpretation. The constrained unmixing preserves all the available radiometric information of the low-resolution image. On the other hand, the unconstrained unmixing may be preferable in case of noisy data. An analysis of the MMT sensitivity to sensor errors showed that the strongest requirement is the accuracy of geometric co-registration of the data; the co-registration errors should not exceed 0.1-0.2 of the low-resolution pixel size. Applications of the constrained and unconstrained algorithms are illustrated on examples of unmixing and fusion of the multiresolution reflective and thermal bands of a real TM/LANDSAT image as well as of a simulated image of the future ASTER/EOS-AM1 sensor.

Unmixing-Based Multisensor

The horizontal inhomogeneity of the atmosphere within a satellite microwave radiometer's field of view (FOV) has always been considered as a source of rainfall retrieval errors. The hydrometeor profile retrieval algorithm presented exploits it to obtain an approximation of a radiative transfer model, which allows relatively simple inversion. The atmosphere within the FOV is treated as a combination of horizontally homogeneous domains. Assuming that one of known "basic" hydrometeor profiles occurs in each domain, the inverse problem is reduced to a determination of "beamfilling coefficients." The online procedure includes determination of beamfilling coefficients and a footprint-averaged hydrometeor profile as a linear combination of "basic" ones. Off-line procedures involve the selection of a minimum number of necessary "basic" brightness temperature vectors and correction of "basic" hydrometeor profiles to provide the best retrieval accuracy for a given cloud/radiative simulation. The performance of the algorithm is tested for both numerical simulations and TRMM/TMI data. Numerical simulation has allowed a comparison of the information content of radiometer measurements from SSM/I, TMI, and the future AMSR. The effectiveness of the algorithm is being tested for rain water integral and rain rate retrievals from TRMM TMI measurements.

The beamfilling algorithm for retrieval of hydrometeor profile parameters from passive microwave measurements

Estimation of Rain Rate by Microwave Radiometry and Active Radar during CLEOPATRA 92

A technique tolerant to an inexactness of the antenna pattern form is developed for resampling microwave radiometer data on the base of robust principles of statistical estimation. Two types of algorithms are considered: a single-channel algorithm and an interchannel algorithm to resample low frequency channel data taking into account a higher frequency channel. The performance of the algorithms is demonstrated by results of DMSP SSM/I data processing.

http://ieeexplore.ieee.org/iel3/3772/11016/00516379.pdf

Robust restoration of microwave brightness contrasts from the DMSP SSM/I data

External Calibration Of Satellite Passive And Retrieving Of Geophysical Microwave Measurements Parameters

The multichannel microwave scanning radiometer DELTA is being flown aboard the PRIRODA module of the Russian MIR orbital station. Data of this radiometer are being processed to the level 1a and stored in the center for processing and storing of space information of the RAS Institute of Radioengineering and Electronics (CPSSI IRE RAS). The paper describes algorithms developed for DELTA data primary processing taking into account real features of the radiometer.

Methods and results of the microwave scanning radiometer DELTA primary data processing

The problem of the adjustment of the sounding instrument and sounded medium a priori models is posed for the satellite passive microwave remote sensing, The linear problem of the total model adjustment is formulated on the base of the external calibration technique developed earlier. The applications of the adaptive approach to the optimum calibration of the microwave remote sensed data and to the radiative transfer models adjustment are described. >

Adaptive algorithms for satellite passive microwave data processing

A technique is described which embodies a robust approach to enhancing spatial resolution of aerospace images based on images made with another sensor with a better spatial resolution. The technique does not require an exact knowledge of the sensor angular functions replacing it with the analysis of a local signals spatial structure. The extent of correction of a low resolution image depends on its correlation with high resolution images. The universality of the technique is demonstrated by its applications to data of microwave and optical sensors.

http://ieeexplore.ieee.org/iel3/4810/13413/00615892.pdf

B.Z. Petrenko

Papers

Robust restoration of microwave brightness contrasts from the DMSP SSM/I data

External Calibration Of Satellite Passive And Retrieving Of Geophysical Microwave Measurements Parameters

Methods and results of the microwave scanning radiometer DELTA primary data processing

Adaptive algorithms for satellite passive microwave data processing

The universal multichannel technique for enhancing images obtained from different sensors