The relationship between a SAR image and a directional wave spectrum was clarified by many studies, and the various inverse analysis methods for estimating a directional wave spectrum from a SAR image were studied. However, it was difficult to estimate a directional wave spectrum from a SAR image because of the so-called inverse problem. For solving this problem, a new estimation method based on the Bayesian theorem was proposed in this study. In this method, the inverse problem for estimating a directional wave spectrum from a two-dimensional wavenumber spectrum of a SAR image was reduced to a problem of a search for the minimum value. For searching a minimum value properly, a priori condition in the Bayesian theorem was considered. From this viewpoint, the smooth change of estimated directional wave spectrum was regarded as a priori condition in this study. In addition, for deciding the minimum value objectively, ABIC was used. To verify the accuracy of the estimation method proposed in this study, a two-dimensional wavenumber spectrum of a SAR image was calculated from the theoretical directional wave spectrum, then, a directional wave spectrum was estimated from a two-dimensional wavenumber spectrum by this estimation method. As the result of comparison between the theoretical directional wave spectrum and the estimated directional wave spectrum, they showed good agreement. Finally, a directional wave spectrum was inversely estimated from a EERS1-SAR image. Simultaneous observations were made with wave-gage array, and the subsequent comparison showed good agreement with the result obtained from the EERS1-SAR image.
This paper proposes a new type of stereo matching algorithm called “Stereo Plane Matching”. Stereo plane matching adopts least square matching (LSM) method under the constraint that all points in an area specified by a polygon should lie on a same plane. This algorithm stands on the fact that correspondence between stereo images becomes 2-D projective transformation (homography) within an area where a common plane is projected. Three corresponding point pairs on stereo image pair are used for parameterization of geometry of a target plane, which allows computation of homography within the polygon. The most powerful feature of this algorithm is that it can impose geometrical constraint in planar direction and position. For example, the target plane can be fixed in horizontal or vertical direction, as well as in other specified direction in 3-D space. Another feature is that it can be applied to unrectified stereo pair, so far as its orientation parameters are known. Experimental results show that this algorithm can measure oblique roof or vertical wall of a building.
Laser scanner has been receiving more attention as a useful tool for real-time 3D data acquisition, and many applications such as city modeling, DTM generation, monitoring electrical power lines and detection of forest areas were proposed. However, effective filtering for distinguish on- and off-terrain points from point cloud 3D data collected by airborne laser scanner is still issues. The paper describes a robust filtering method from a view point of topographic surveying using a terrestrial laser scanner. The method is based on flatness within 0.3×0.3m. Flat area (ground surface, wall of buildings, etc.) and non-flat area (trees, bushes, windows, sky, etc.) are classified using surface flatness, and non-flat areas are interpolated using morphological procedure. The filtering methods show very robust results, and the most remarkable point of this filtering method is its ability to obtain break-line.
Satellite and airborne sensor images are useful for the monitoring of the vegetation states, such as crown cover state and crop growth. However, image data obtained through an optical sensor situated at high-altitude inevitably include mixels. Therefore, unmixing method, which estimates both the pure spectra and the coverage of endmembers simultaneously, is required in order to distinguish the qualitative spectral changes due to the chlorophyll quantity or crop variety, from the quantitative coverage change. We have proposed an ICA (Independent Component Analysis) aided unmixing for periodically distributed hyperspectral data in agricultural land, and demonstrated with simulated mixel data that the technique enables us to estimate the pure spectra and coverage of crop and soil simultaneously even when the mixed spectra in agricultural land include vegetation covering fluctuation, and additive noise such as thermal sensor noise and atmospheric noise. In this paper, we apply our ICA aided unmixing of agricultural land to a Japanese persimmon orchard in airborne hyperspectral sensor image and show the ability of the method against the actual remote sensing data.