An experiment on detecting moving objects was made by the utilization of a single QuickBird scene. The observation time lag between panchromatic and multispectral images of QuickBird bundle product is around 0.2 seconds. This product can be considered to be two consecutive acquisition images covering the same area within the time lag, though the ground resolutions of those images are different. Applying this principle, moving objects, e.g. aircrafts, boats, vehicles, can be detected by the simple way using overlapping panchromatic and multispectral images in the same size and at the same coordinate. The distances of the target movements and the velocities can be obtained by the measurements of target displacements because they change their positions in this extremely short interval. This paper is to introduce a new method of high resolution satellite data application which has not been considered yet.
Active radar calibrator (ARC) are generally used for calibration of spaceborne or airborne SAR. The Japan Aerospace Exploration Agency (JAXA) and National Institute of Information and Communications Technology (NICT) developed the ARC for calibration of the first spaceborne Precipitation Radar (PR) onboard the Tropical Rainfall Measuring Mission (TRMM) satellite. The main characteristic of this ARC is three function of radar receiver, beacon-signal transmitter, and delayed-transponder function. On the development phase of TRMM-PR, it was confirmed that the ARC is useful for calibration of active array radar such as TRMM-PR. Since the TRMM launch in 1997, TRMM-PR has been calibrated with the ARC per a few months, and its calibration results have been reflected in the PR data processing system as calibration coefficients. At first, TRMM-PR used to be calibrated only by the radar receiver and beacon-signal transmitter function of the ARC, but we established a new calibration technique using delayed-transponder function which can calibrate radar cross section of TRMM-PR directly. Through the five year calibration period using the ARC, transmittion and receiption gains of TRMM-PR have been calibrated to an accuracy of ±0.5 dB or better, and the PR-measured radar cross section has been calibrated to an accuracy of within±1 dB.
For geometric correction of satellite images over rugged terrain, we have proposed an optimaization method in which a simulated irradiance image from digital elevation model is used to determine parameters of geometric transform equation. In this paper, we propose another optimization method which supplements the former method as it is used for coastal area. It is noted that difference between land surface and water surface is the most distinctive feature in satellite images, particularly, in middle infrared bands. Instead of careful selection of GCPs in conventional geometric correction methods, a number of evaluation points are selected both from sea and land over a digital map by using stratified sampling method with regard to distance from coastline. Then, these samples are checked at the corresponding image coordinates calculated by the geometric transform equation. By using misclassification rate as an evaluation function, the parameters (scene center displacements) in the transform equation as well as an threshold between sea and land are optimized. We apply the method to 13 system-corrected Landsat images to check their accuracy and computational efficiency. It is shown that this method can provide scene center displacements with the accuracy of few pixels and with very high computation efficiency. The accuracy could be improved by selecting sample points more carefully.
The atmospheric aerosols play an important role in tropospheric chemistry. In this paper, we make an error analysis of a method for remote sensing of tropospheric aerosols over land using Global Ozone Monitoring Experiment (GOME) sensor. This algorithm, based on Direct Method [Torres et al., 1998], makes use of radiance measured in two UV wavelengths (335 and 395 nm). In this spectral region, the surface reflectivity is lower than that in visible range, for example, which enables us to retrieve aerosol properties easier over land in particular. At first, we present the results of sensitivity study with the two channels. Next, we make error analyses caused.by the uncertainties in the parameters such as surface albedo, height of aerosol layer, or viewing angle within a GOME pixel, so as to retrieve aerosol optical thickness and mode radius.
This paper proposes a new approach of decomposing Mueller matrix of full-polarimetric synthetic aperture radar (SAR) data. The large conducting sphere, the dihedral corner reflector, the short thin cylinder (wire) and the helix are known as simple scatterers. The new approach presents an algorithm of decomposing Mueller matrix into the above four simple scatterers. The components of the above simple scatterers have been applied to classify the planted forest. Two full-polarimetric imagery of Tomakomai in Japan generated by near crossed flights JPL/AIRSAR are used for classification. Four categories in the study area, Broadleaf, Larch, Fir and Spruce, have been classified. The average accuracy of classification is 83.28% by using C, L and P band data, while the average accuracy of classification is 72.25% by only using L band data. Through the new decomposing Mueller matrix method proposed in this paper, it becomes obvious that the wire scatterer is closely correlative with branch. In L-band, the wire angles of Broadleaf show as a uniform distribution and the wire power is weak. On the contrary, the wire angles of Conifer show as a Gaussian distribution with high kurtosis near a horizontal direction and the wire power is strong. As a result of classification, the components of wire scatterer are important factors to classify Broadleaf and Conifer.