Hyperspectral Imager Suite (HISUI) is a spaceborne multispectral/hyperspectral system being developed by the Ministry of Economy, Trade and Industry (METI) of Japan. It consists of a high spatial resolution multispectral spectrometer that collects data at 5 m ground sampling distance with wide 90 km swath and a high spectral resolution hyperspectral spectrometer that provides physical properties of the surface materials with 185 bands. Critical Design Review (CDR) will be held in late 2012. HISUI is expected to be launched in 2015 or later. For the ground segment, designing of the overall system and algorithms for data processing are under development. Automated tool to generate the observation schedule is also developed to best accommodate the data acquisition requests with limited observation resources.
This paper presents a monitoring method for paddy fields with hyperspectral remote sensing images in West Java, Indonesia. The statistical modeling method called sparse reguralization is introduced in two forms, that is, LASSO regression for the rice yield estimation and sparse discriminant analysis for the growth stage classification of rice plants, in order to take advantages of the detailed reflectance spectrum measured by numerous bands and to overcome the difficulties in hyperspectral image analysis such as model overfitting. Results of the experiment with airborne hyperspectral images measured by HyMap indicate that sparse regularization can predict paddy conditions with higher degree of accuracy than several estimation methods commonly used in remote sensing applications, such as normalized difference spectral index, partial least squares, or support vector machines. Besides, the prediction models have a limited number of bands which are expected to be informative to figure out the rice growth situation. The overall error between predicted rice yield of the target area and agricultural statistics is 6.40 %, showing the potential effectiveness of methods described in this paper.
This research aims at the development of the mineral identification technique to inquire into the resource using hyper spectral data in arid or semiarid regions. We designed the method by two steps where the minerals were identified from the spectrum. At the first step, to identify the absorption position from the spectrum, we use first derivation spectrums. However, the derivation method emphasizes a noise in the spectrums. To solve this problem, we designed the improved one i.e. giving a median filter to the differentiated spectrum. This method has two advantages, i.e. a rigorous atmospheric correction is not required, and identification is unaffected from the noise of the data. Once the algorithm detects the absorption, we applied least squares fitting of a quadratic curve to around wavelength region of it, for accurate identification of the absorption position. The next step is identification and quantification of the minerals, using the position and the depth of the absorption features. The content of the minerals is calculated by comparing the content turned out in the identification score and the results of in situ investigation. As a result, we developed the method by which 13 kinds of minerals were able to be identified. Especially, the calcite and the kaolinite were able to calculate the content on the ground.
Elemental techniques and its applicable conditions for hyperspectral data in petroleum exploration had summarized. When considering the applicability of hyperspectral data for oil exploration, the ability of mineral identification of hyperspectral data will be important. This paper reviews elemental techniques that enable minerals to indicate the elements of the petroleum systems such as source, reservoir and cap rock in addition to assess the degree of maturity and oil and gas seepages using hyperspectral data and proposes the methodology to conduct petroleum systems analysis based on the hyperspectral data.
In mineral exploration using remote sensing, vegetation disturbs to extract topsoil spectral features. Previous studies of spectral mixture analysis are using the linear mixture model with end member spectra. However, it is usually difficult to estimate the end member spectra, number of components and mixture ratio. This paper proposes a new method to reduce the vegetation influence from the mixed spectrum in vegetated areas. In the mixture analysis of topsoil and vegetation, the mixture ratio is the vegetation cover ratio estimated by NDVI. However, NDVI may vary even on the same surface in the case of using the apparent reflectance. To address this issue, we introduce the unit vectorized reflectance (UVR). The UVR achieves to calculate the same NDVI values regardless of the differences in the apparent reflectance and stably express NDVI as vegetation cover ratio. For this reason, the regression curve, called “the reduction curve of vegetation influence”, is obtained from the NDVI and the UVR ratio. The ratio is calculated using the mixed and topsoil’s UVR. The regression model proposed presents therefore a new method to estimate the topsoil spectra. To evaluate the applicability of this method to satellite images, ASTER and Hyperion data were used. For each band, NDVI and UVR ratio, calculated from the average of UVR at each NDVI value, had a significantly high correlation (R2>0.9). This result suggested the practical applicability of the method to satellite images. Therefore, the method proposed achieves to create vegetation-influence-reduced UVR images. Ground truth observation was conducted in central Chile to compare rock sample’s UVR and the vegetation-influence-reduced UVR. As a result, the vegetation-influence-reduced UVR seemed to be similar to the rock sample’s UVR. Therefore, this method was able to emphasize the absorption bands of minerals.
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