A simplified cloud mask algorithm suitable for present-day onboard processing is developed based on the operational MODIS cloud mask algorithm. The code is reduced to less than 45% of its original size while retaining 90% of the accuracy. Clear sky data compression is tested using our simplified cloud mask followed by application of the CCSDS lossless compression algorithm. For most of the channels, compressing the cloud masked MODIS L1B data can reduce the data volume by about 40% compared with compression without using cloud mask. The cloud mask was also applied to L1A data with comparable or even better data reduction performance depending on local data filling schemes. The cloud mask itself can be compressed to less than 0.5 bit-per-pixel. This paper summarizes our approach and provides results in detail.

Lossless data compression has been studied for many NASA missions to achieve the benefit of increased science return; reduced onboard memory requirement, station contact time and communication bandwidth. This paper first addresses the requirement for onboard applications and provides a rationale for the selection of the Rice algorithm among other available techniques. A top-level description of the Rice algorithm will be given, along with some new capabilities already implemented in both software and hardware VLSI forms. The status of the technology will be presented with its performance in several applications including remote sensing, medical imaging and seismology.

A VLSI implementation of a lossless data compression algorithm is reported. This is the first implementation of an encoder/decoder chip set that uses the Rice (see JPL Publication 91-1, 1991) algorithm and provides an introduction to the algorithm and a description of the high-performance hardware. The algorithm is adaptive over a aide entropy range. Its performance on several 8-b test images exceeds other techniques employing differential pulse code modulation (DPCM) followed by arithmetic coding, adaptive Huffman coding, and a Lempel-Ziv-Welch (LZW) algorithm. A major feature of the algorithm is that it requires no look-up tables or external RAM. There are only 71000 transistors required to implement the encoder and decoder. Each chip was fabricated in a 1.0- mu m CMOS process and both are only 5 mm on a side. A comparison is made with other hardware realizations. Under laboratory conditions, the encoder compresses at a rate in excess of 50 Msamples/s and the decoder operates at 25 Msamples/s. The current implementation processes quantized data from 4 to 14 b/sample.>

A method is presented which corrects the geometric distortion of pictures with sub-pixel precision. Correction is performed in two passes. The first pass, called calibration, creates correction tables which represent the deviation value of each pixel of the input picture. The second pass, called decalibration, corrects a distorted picture by gray level interpolation using the correction tables. The deviation value is computed with sub-pixel accuracy.

An algorithm is presented which directly computes the 3-D coordinates of the vertices of a quadrangle relative to the camera frame. This method is extended to determine the camera's exterior orientation in a world coordinate system. A shape restoration procedure is designed to reduce the effects of noise and thereby improve the accuracy of the algorithm.