A method was developed for global monitoring of temporal change of coral reef using pan-sharpened color images with
higher accuracy and lower cost. The method consisted of 3 blocks; image co-registration for removing complex
discrepancy due to parallax among original color image and panchromatic one, pan-sharpening with preserving color
information, and change detection with suppressing noise such as sea waves. The method was successfully applied to
an actual FORMOSAT2 multi-temporal data set. After removing the parallax between multi-spectral band images and
panchromatic one, the spatial resolution of multi-spectral images was improved from 8 x 8 m2 to 2 x 2 m2 in the
following pan-sharpening block. The pan-sharpening was performed by replacing brightness component of the original
multi-spectral pixel with the panchromatic pixel density. In order to normalize the recording gain and offset, the
brightness component was estimated by using a linear polynomial model whose coefficients were determined by
applying a multiple regression analysis. Linear shapes of density scatter diagram of each spectral band between pansharpened
density and original one indicated that the pan-sharpened spectral information was perfectly preserved. The
change detection successfully detected some temporal changes with suppressing noise. The method was applicable to
other data sets having lower resolution multi-spectral images and panchromatic one covering all spectral bands.
In order to measure vertical profiles of minor gas concentrations in the stratosphere, Improved Limb Atmosphere Spectrometers (ILAS and ILAS-II) have been developed. ILAS was the first generation sensor and made observations in 1996 and 1997. ILAS-II will measure atmospheric limb transmittance in 66 spectral bands (whereas 44 for ILAS) in the thermal infrared region by observing solar ray passed through the atmosphere. Vertical profiles of minor gases are simultaneously retrieved by a spectral fitting algorithm with an onion-peeling method for vertical profiling. This algorithm adopts a precise radiative transfer calculation and is very accurate, but usually the standard radiative transfer calculation needs huge volume of line-by-line calculations of molecular absorption to simulate theoretical limb transmittance spectra by using the HITRAN database. Methods for accelerating the algorithm have been required. In the ILAS operational program, a table look-up method, which needs an excellent computer system, was used for rapid calculations. We proposed a simplified method, which predicts the gas profiles from the measured limb transmittance spectra and vertical profiles of atmospheric pressure and temperature without iterative calculations by using a multiple regression technique. The Principal Component Expansion (PCE) is used for reducing the scale of the multiple regression model. In the training process, coefficients of the model are estimated from the previously retrieved data sets including measured limb transmittance spectra, vertical profiles of atmospheric pressure and temperature, and retrieved gas profiles. Then, the method predicts gas profiles from the newly measured limb transmittance spectra and pressure and temperature profiles. The validity of the method was confirmed by numerical simulation using the MODTRAN v.3.5 radiative transfer code. The proposed method was also applied to the actual 3474 ILAS observation data sets. The model trained by 3373 data sets well predicted the gas profiles for another 100 data sets which are selected randomly . This proposed method can be used for quick look of ILAS-II measured data and for generating the initial profiles for the operational spectral fitting algorithm.
This paper describes a Synthetic Aperture Radar (SAR) phase processing system and an automated method for estimating baseline length and its inclination angle from only pair of Single Look Complex (SLC) images to be interfered. The information about the baseline parameters are indispensable to produce accurate Digital Elevation Model (DEM) without geometrical distortion and also accurate displacement from differential Interferometric SAR (InSAR) processing. Only the nominal value of the baseline length is currently provided. The fixed value, however, does not represent nonparallel orbits from which raw interferogram is obtained. The ambiguity causes the geometrical distortion in DEM and the apparent displacement in the result of differential InSAR processing. In order to produce the accurate DEM, we have to know the baseline parameters as the function of the azimuth line. The proposed method derives coefficients of both functions for baseline length and for inclination angle by fitting the local disparities to a geometrical model. The proposed method was successfully applied to an actual InSAR data. The baseline estimated by the proposed method reduced apparent displacement to 1 /10 of that yielded by the nominal baseline. A piecewise co-registration method is also described for obtaining highly accurate interferogram.
A new method, Multiple Phase Method (MPM), is proposed for fast unwrapping InSAR raw interferogram. The unwrapping process is regarded as one of phase adding or subtracting (lifting) processes when 2-pi phase jump is detected. The proposed method MPM distinguishes the phase jump from noise in the raw interferogram by using the original and its pi phase shifted interferograms. MPM removes the phase jump from the lifting operation by switching the difference data from the original interferogram to the phase shifted one or from the shifted one to the original when the phase jump is detected. The phase jump is detected using a simple mask operation. The phase integration is carried out by using new index 'confidence' derived from the coherence as a guide to select the integration path. MPM selects the integration path from higher confidence region to lower ones. As residues have locally minimum coherence, MPM does not pass through them. The proposed method MPM was successfully applied to an actual pair of ERS-1 SAR single look complex images.
An automated method is posed to register remotely sensed images with subpixel accuracy. The method is called ARTSPA. ARTSPA improves correspondence among control point pairs from pixel to subpixel accuracy. Registration with subpixel accuracy is realized by using the improved control point pairs. ARTSPA was successfully applied to JERS-1/OPS stereo pair images for deriving terrain height.
A new automated registration method using disparity detection is proposed. In this method, a new pair of control points is automatically generated on a point which has the largest disparity between images to be registered. The procedure is repeatedly applied to the images until the maximum disparity falls below a threshold. Considering the images to be registered as sequential shots of a video image, the disparity is detected as optical flow vectors. The method enables us to ensure the accuracy of registration.
A new method is proposed for clustering remotely sensed multispectral images. This method has a binary division process in which division boundaries are determined by an algorithm of linear discriminant function. In order to realize high speed processing, image data are compressed and projected onto a 2D subspace. Then, the image data are repeatedly divided into groups until stopping conditions are satisfied. In this method, the optimal number of clusters are automatically determined accordingly to the statistical property of the image data. The method has higher speed than ISODATA does, and is successfully applied to actual multispectral images.
A new method is proposed for change analysis with weight of significance between two multi- temporal multi-spectral images. This method gives us areas which indicate the assigned temporal change, for example, from vegetation to bare soil. Image data are projected onto a feature space in which the assigned change is emphasized, and temporal changes between two images are detected with suppression of irrelevant changes. The validity of the method is confirmed by numerical simulation. The method is successfully applied to actual multi- temporal and multi-spectral images.
An automated method is proposed for terrain height estimation from SPOT stereo pair images. This method consists of two procedures: registration between stereo pair images and terrain height estimation. In the first stage, stereo pair images are roughly rectified from initially selected 4 ground control point (GCP) pairs. Automated process is used for generating GCP pairs in the second stage. For the derivation of epipolar constraint, images are registered by piece-wise Affine transformation (ATF) using these GCP pairs. Accurate rectification is made by dynamic programming (DP) matching process applied to pair of epipolar lines. Terrain height is obtained from corresponding points on the stereo pair images by using a triangulation technique. Mean height error of this method is evaluated as less than 30 m.
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