In order to acquire images of specified bands with high-speed, high time resolution and high spatial resolution, a prototype of the observation system utilizing sequentially controlled LED illumination is developed. As a performance validation of this prototype, the human skin extraction based on Normalized Difference Human Index (NDHI) is attempted. An illumination unit consists of LEDs with 1070nm and 1550nm wavelengths of light. An infrared vidicon camera with high-sensitivity over the range of 400nm to 1700nm wavelengths is installed as an imaging equipment. Light intensities of the LEDs are sequentially controlled by a pulse width modulation (PWM) driver. An improvement method for response delay in the infrared vidicon camera is evaluated by the experiment based on the prototype. Finally, two image-lag elimination methods utilizing series of intensity modulated pulses for LED illuminations are proposed. The effectiveness of the proposed methods is confirmed by simulation results of the estimation of the attenuation coefficient and the true intensities after eliminating the after-image effect.
A method for detection of red tide by means of remote sensing reflectance peak shift is proposed. Although remote sensing reflectance peak is situated at around 550nm for sea water without suffered from red tide, the peak is shifted to the longer wavelength when sea water is suffered from red tide. The proposed system uses web camera with band-pass filter on the optics surface. Acquired imagery data can be transmitted through wireless LAN to Internet terminal and can be archived in server through Internet. Validity of the proposed method is confirmed with the system deployed in Ariake Sea which is situated in northern Kyushu, Japan. Also a method for red tide detection with satellite imagery data is attempted. Furthermore, a possibility of red tide detection with polarized radiance measurements is discussed through polarization camera derived sea surface imagery data, in particular, for non-spherical shape of red tide.
Hyperspectral imaging data collected by airborne and satellite platforms have been used in a variety of fields, such as geology, agriculture and forestry. In order to make such quantitative and precise applications, accurate spectral calibrations of hyperspectral imaging data are necessary to be achieved. The center wavelength for each band obtained in a laboratory needs to be adjusted, since it changes easily by vibration and variation of observation condition. The shift of center wavelength triggers an artifact in atmospheric correction necessary for quantitative estimation of ground surface target. The quantitative estimation of the artifact appeared around absorption bands is important for accurate spectral calibration. This paper describes a new estimation method for the artifact, and shows that the artifact reduction is achieved by calibration of center wavelength with our proposed methods. Furthermore, we evaluate the artifact influence on practical remote sensing application, such as tree classification. Our estimation method accurately evaluates the artifact and makes wavelength corrected hyperspectral image without artifact. We clarify that the spectral change of artifact having a tendency in cross-track that is more influential in classification than spectral change of tree species. This method has been effectively applied to a variety of land use applications using data acquired with airborne hyperspectral imagers.
Abandoned bamboo groves are vigorously spreading their distribution in Japan, causing various problems such as landslides and reduction of biodiversity. In order to help monitoring expansion of the two most common local species of bamboo Phyllostachys bambusoides Sieb. et Zucc. and P. heterocycla (Carr.) Mitf., this study was designed to find out characteristics of land covers by analyzing the distribution of satellite image DN (pixel value) obtained from each land covers. We used ALOS/AVNIR-2 satellite images of the northwestern Chiba Prefecture which have 10m spatial resolution and were taken in October of 2008, and February, March, April, May, August and September of 2009. Google Earth images and data from field research were utilized to set sampling areas for each land covers to be extracted DN. Distributions of DN showed, from the viewpoints of phenology, that data of red and infrared band in Feburary and May are useful to extract P. bambusoides and P. heterocycla and a strong possibility that these two species can be divided by ALOS/AVNIR-2.
Vegetation coverage is one of the well-known parameters indicating the growth of rice plants. It is usually calculated as a ratio of the extracted visible green area of plants, the green visibility ratio, in a camera image using the vertical photography. However, early in the season, growth is mostly vertical and therefore a near-vertical observation angle doesn't observe vegetation coverage sensitively. But, a near-horizontal angle of observation can observe vertical and horizontal growth and allow early season monitoring of vegetation coverage. In this paper, the green visibility ratios were calculated by the visible green areas in different observation angles (the range of the observation angle 0.0 degrees to 22.0 degrees) using camera image data taken from vertical photography during the growing period. The relationship between the green visibility ratio and the observation angle was analyzed. As a result of this analysis, the green visibility ratio was increased with the increase in the observation angle and the according to the progress of growth. In addition, the range of the observation angle with the green visibility ratio almost equivalent as the vegetation coverage was decreased rapidly with the progress of growth in 40 to 50 days after transplanting.
Hyper-spectral sensor that has high spectral resolution is expected to the application of agriculture field. In the rice cultivation in Japan, attention has focused on making high quality rice. In the previous others, several vegetation indices which use various wavelength bands were effective for rice protein contents estimation. For example, Asaka et al. (2003) proposed the vegetation index computed from SPOT/HRV band 2 (610-680nm) and band 3 (790-890nm) data as the predictor variable of the estimate equation, and built the estimate equation by regression analysis. And Inoue et al. (2008) proposed the other vegetation index which uses two hyper-spectrum bands (570nm, 970nm). In the same manner, Suhama et al. (2010) proposed the vegetation index which uses two hyper-spectrum bands (460nm, 510nm). In order to evaluate the validity of these vegetation indices, it is necessary that the theoretical examination which uses radiative transfer simulation in addition to regression analysis. Then, this research shows theoretically the validity of spectral reflectance at these wavelength bands using the PROSPECT model as radiative transfer simulation. The results obtained by this research are shown below. 1)610-680nm and 570nm are the sensitive region of chlorophyll-a, -b contents of rice leaf. 2)790-890nm is the sensitive region of equivalent water thickness of rice leaf. 3)970nm is the sensitive region of equivalent water thickness and dry matter of rice leaf. 4)460nm and 510nm are sensitive region of chlorophyll-a, -b contents and carotenoid contents of rice leaf.
In this technical report, we propose a extraction method of airborne laser scanner data in inundation area. We prove the effectiveness of the proposed method by comparing with inundation area. Finally, we comment on the respects in which the proposed method is improved and on future prospects.
The public surveying standard of Japan introduced JPGIS (Japan Profile for Geographic Information Standards) based on ISO (the International Organization for Standardization) and JIS (the Japanese Industrial Standard) in 2008. JPGIS regulates “accuracy” as the quality definition of the datasets, however “precision” is commonly used for the quality evaluation in the standard. This report expounds definition with respect to “accuracy” and “precision” including numerical examples.