The letter introduces a novel quantizer suited for medium to high-resolution synthetic aperture radar (SAR) systems, like the forthcoming SENTINEL-1 SAR. The Flexible Dynamic Block Adaptive Quantization (FDBAQ) extends the concept of the Block Adaptive Quantization (BAQ), used in spaceborne SAR since the Magellan mission, by adaptively tuning the quantizer rate according to the local signal-to-noise-ratio (SNR). A design is presented aiming to optimize the average bit-rate, while constraining the minimum SNR. FDBAQ optimized performance is then evaluated using backscatter maps derived from ENVIronment SATellite (ENVISAT) data.

This paper presents a motion compensation algorithm to focus high resolution SAR data taken from an airborne sensor. The basic idea is to fragment the sensor trajectory into smaller segments that can be modelled as linear or quadratic. The synthetic antenna is then considered as an array of smaller subarrays. The algorithm can easily adapt and trade efficiency for sensor path irregularity. In fact the algorithm is more efficient if the segments are longer. Experimental results with real airborne data confirm the correctness of the approach.

We introduce a novel accurate technique to estimate the Doppler centroid (DC) in ScanSAR missions. The technique starts from the ambiguous DC measures in the subswaths and uses a method alternative to standard unwrapping to undo the jumps in estimates induced by modulo pulse repetition frequency (PRF) measures. The proposed alternative is less error prone than the usual unwrapping techniques. Doppler Ambiguity is then solved by implementing a maximum-likelihood estimate that exploits the different PRFs used in different subswaths. An azimuth pointing of the antenna that does not change with subswaths, or that changes in a known way, is assumed. However, if the PRF diversity is strong enough, unknown small changes in azimuth pointing are tolerated and accurately estimated. This estimator is much simpler and more efficient, than those in the literature. Results achieved with both RADARSAT 1 and ENVISAT ScanSAR data are reported.

Field skipping is a variable technique for reducing drastically the bit rate necessary to transmit a television signal. All fields have to be reconstructed at the receiver end, but nearest-neighbor or linear interpolations give poor performances when significant scene activity is present; therefore, some motion compensation scheme is mandatory. Although any of the algorithms proposed for motion estimation can be used for motion compensated interpolation, in this paper only the pel-recursive algorithm is considered. Past experience is briefly summarized and, based on criticism, improvements to the basic algorithm are proposed that seem to lead to an estimated motion field that is accurate enough. Multiple recursions and appropriate selection rules are proposed to counteract streaking effects of the recursive algorithm when it crosses image boundaries. The estimation of a forward and a backward motion field is used to identify image areas where occlusion effects are present. Experimental results are presented that seem to indicate that the proposed interpolation scheme has good performance.>

Various techniques are described to measure, small displacements of television images. If two successive video frames are considered, their differences are approximately a linear combination of the components of the displacement of the object. If all the points of the frame undergo the same movement, then the velocity estimation problem is solved using linear estimation. However, if some points belong to the moving object and the others to the background, the problem can be stated in the same way only if an algorithm is available to segment the image into fixed and moving areas. Afterwards, linear estimation can be applied to the moving area only. In this paper a segmentation algorithm is proposed which uses dynamic programming (Viterbi algorithm with three states). A more complex situation arises when the points belonging to the moving area are subjected to different movements. The problem can be solved once more using dynamic programming if the displacement components are quantized into(2M + 1) (2M + 1)values, and the number of states of the Viterbi algorithm is augmented to(2M + 1)^{2}. To reduce the technical difficulties of this approach, a simpler method that makes possible the estimation of thenmost probable displacements is proposed. Then the image is segmented into n moving areas with different displacements and a background area using a Viterbi algorithm withn + 1states. Experimental results show that the precision obtainable is about 0.1 pel when the displacements are up to 2-3 pels, the object had approximate dimensions of90 \times 90pels, and the signal-to-noise ratio was higher than 33 dB.