In This work, high-speed and precise geometric correction for GIMS S-VISSR data is described. Bi-linear interpolation is used for speeding up the geometric correction. The precise correction is carried out by using the ground control points. Moreover, the geometric correction considering the elevation is applied in land area. In the case of the correction on infrared channels, visible and infrared channels are used. As a result, the maximum of errors in the proposed method is 0.01 degree in the both direction of latitude and longitude.

A fully automated method of extracting narrow rivers in tropical rain forests from JERS-1 SAR images was implemented using both spectral and spatial information. The system was divided into extraction of narrow rivers that appear as a series of line-like blobs and extraction of wide rivers that appear as dark continuous line-like features. The method was assessed by comparing the results from optical JERS-1 VNIR data and by applying 21 scenes in other areas in Amazon basin and Congo basin.

The water surfaces detected by a conventional intensity based method from JERS-1 SAR images do not always coincide with the flood-damaged areas especially existing paddy fields. The authors propose morphological methods as spatial information. The result roughly agree with the reported flood-inundated areas. We concluded that this kind of methods has an advantage of the disaster monitoring using SAR satellite.

A pixel is not an "atom". Most image processing algorithms basically assume that a pixel is not dividable; in other words, its inside is homogeneous consisting of only one classification class. This assumption is, however, not true for images, in particular remote sensing images, taken by a sensor with coarse resolution. In these images even a single image pixel may contain more data than two classification classes, which means that a pixel has an underlying heterogeneous structure internally. This paper discusses the statistical properties of these heterogeneous pixels, namely mixels. The difference between our work and previous work comes from the formulation of the problem-we focus on overall statistical properties that a set of pixels will show in the image intensity space. This formulation introduces our new stochastic model, mixel density, which also provides a proper interpretation for so-called long-tail density.

Lossless gray scale image compression is necessary for many purposes, such as medical imaging, image databases and so on. Lossy images are important as well, because of the high compression ratio. The authors propose a lossless image compression scheme using a lossy image generated with the JPEG-DCT scheme. The concept is, send a JPEG-compressed lossy image primarily, then send residual information and reconstruct the original image using both the lossy image and residual information. 3D adaptive prediction and adaptive arithmetic coding are used, which fully use the statistical parameters of the distribution of the symbol source. The optimal number of neighbor pixels and lossy pixels used for prediction is discussed. The compression ratio is better than previous work and quite close to the original lossless algorithm.>

The performance of the functional disk system with relational database engine (FDS-R2) is evaluated in detail in view of two points. First, the performance evaluation of the combined hash algorithm on FDS-R2 is reported using the projection and aggregation operations in addition to the join operation and they are analyzed in order to verify the effectiveness of the proposed processing method, Second, several measured results of performance evaluations with the expanded version of the Wisconsin Benchmark are given and analyzed. FDS-R2 attained higher performance for very large relations as compared to other large database systems such as Gamma and Teradata. In this evaluation, it is also shown that the performance of the relational operations can be improved largely by using an efficient hashing strategy for large relations on FDS-R2.>

Join algorithms on KD-tree indexed relations are proposed. The join algorithms are based on a concept called wave. The wave is a set of pages that is the object of joining and that propagates over the relation space in the direction of the join attribute axis. Four basic join algorithms that determine the wave from one of the relations and one algorithm that determines the wave from both relations are proposed. The algorithms are described and extensively analyzed with analytical formulas and simulation results. Then a garbage collection mechanism is introduced that discards the unnecessary data loaded in the main memory and extends the previous basic algorithms with an efficient memory management. It is shown that the proposed algorithms perform the join of very large relations with one scan.>

The second version of FDS-R (functional disk system with relational database engine), FDS-RII, which is designed to handle large relations efficiently, is discussed. On FDS-RII, the processing algorithm is selected at run time from two algorithms (nested loop algorithms, grace hash algorithm) by comparing their estimated I/O costs. The processing strategy is discussed in detail. The I/O cost formula is examined by measuring the execution time of a join query on the FDS-RII. With the expanded version of Wisconsin Benchmark, the performance of FDS-RII is measured. FDS-RII attained a high performance level for large relations as compared to other large database systems such as Gamma and Teradata. While FDS uses just one disk and three MC68020s, Teradata uses 40 disks and 20 AMPs and Gamma requires eight disks and 17 VAX 11/750s.>

A retrieval method using the so-called layout structure is proposed. The layout structure is one of two-dimensional visual features that are peculiar to the image data, and is obtained only when human beings perceive the image contents. The description scheme and the extraction procedure of the structural information are described. Some experimental results are also presented.>

The major performance bottle neck in the current computer system is in the low-performance secondary system. The performance of the CPU has increased dramatically so far, about several orders of magnitude improvement has been achieved. On the other hand, that of the disk system has shown little advance since nineteen sixties. The von Neumann bottle neck between the CPU and the secondary storage subsystem has been much more enlarged.