LANDSAT MSS data contains spectral reflectance information that is mainly about soil and vegetation except of course data from water covered areas. We want to have soil color information in order to recognise soil conditions and for soil mapping.
Therefore, we have developed a method for extracting soil spectral reflectance information from LANDSAT MSS data, as follows.
1) We use a two dimensional graph of log IR and R for the determination of constants for use in the calculation of vegetation covering ratios. The G and R reflectances of plants are determined by IR: G and IR: R graphs.
2) When we need to skip some of the MSS data (compression data) so we pick up the most useful pixels from the data. Therefore, we select the pixels that are the lowest values in the IR band omitting those from water covered areas, and file the pixels of the R and G bands at the same location as the IR band.
3) We calculate vegetation covering ratios with the constants determined by method 1) and new filed data in method 2) . Then, we calculate the soil reflectance of the R and G bands using vegetation covering ratios, new filed data and R and G reflectances of plants.
4) B band reflectance is not discerned by LANDSAT MSS, so we interpolate it from R and G band data using a soil spectral model.
5) We make soil color images using R, G and B band reflectances of the soil.
As to the application of this soil color image, we are able to extract the soil moisture changes between two LANDSAT scans. The method for accomplishing this is as follow.
1) Reconciling of data from two scans of the same location at different times.
2) Extraction of soil spectral reflectance.
3) Calculation of the changing value of soil reflectances between the two times.
We can get useful information about soil distribution and moisture from this color image. We can understand the systems of using water on paddy fields with the image of soil moisture changes.

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This paper proposes a patch by patch correlation method for automatic stereo plotting from aerial photographs for the control of matching wanderings, where matching is made on all over a fairly large patch area rather than at every each point. Its effectiveness is evidenced in the experiments.
The structure of the algorithm is as follows;
1) A stereo image pair is subdevided to overlapping patch pairs, each consisting of about 250 by 250 pixels and 250 by 400 pixels for the left and right patches respectively. Matching process is done independently for each patch pair.
2) The grid points are allocated on the left patch and their conjugates are searched. At the start matchings are done coarsely using low frequency components of the images, converged step by step to fine details using high frequency components. In accordance with this coarse-to-fine process the grid points are disposed sparcely at the first step and their density is increased step by step.
3) It is essential in this method to pose the positional constraint on the matched points to keep the geometrical consistency.
4) Phase distortions of the images due to terrain reliefs are eliminated by iterative correlation.

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Bathymetric soundings in hydrographic survey are ordinarily carried out by using acoustic survey system on survey ships or boats. Then sounding works in shallow water are very troublesome and dangerous. Therefore, some hydrographic authorities have introduced the photogrammetric bathymetry system since 1960's, respectively. On the other hand, some of them have tried to use a aerial laser bathymeter for some purpose. Recently, a combined system of above-mentioned photo-and laser-bathymetry was developed in America and Canada. The technique using to such system is called a Lidar Hydrography.
In this paper we will historically review on the status in hydrography of these systems.

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