ヨサファット テトォコ スリ スマンティヨ

A miniature C-band UAVSAR system has been developed at the Multimedia University, Malaysia. It serves as a test-bed for SAR experiments in remote sensing and surveillance applications. The construction and testing of the SAR sensor were completed in mid of 2010. Since then, a series of indoor and outdoor testing and measurements has been carried out to verify the performance of the system at various levels. At the end of 2010, two flight missions have been conducted: The first one at Semporna, Sabah and the second at Mersing, Johor, Malaysia. This paper presents the current progress and the future plan of the SAR sensor development in Malaysia.

Image fusion is a useful tool for integrating a high resolution panchromatic image (PI) with a low resolution multispectral image (MIs) to produce a high resolution multispectral image for better understanding of the observed earth surface. Various methods proposed for pan-sharpening satellite images are examined from the viewpoint of accuracies with which the color information and spatial context of the original image are reproduced in the fused product image. In this study, methods such as Gram-Schmidt (GS), Ehler, modified intensity-hue-saturation (M-IHS), high pass filter (HPF), and wavelet-principal component analysis (W-PCA) are compared. The quality assessment of the products using these different methods is implemented by means of noise-based metrics. In order to test the robustness of the image quality, Poisson noise, motion blur, or Gaussian blur is intentionally added to the fused image, and the signal-to-noise and related statistical parameters are evaluated and compared among the fusion methods. And to achieve the assessed accurate classification process, we proposed a support vector machine (SVM) based on radial basis function kernel. By testing five methods with WorldView2 data, it is found that the Ehler method shows a better result for spatial details and color reproduction than GS, M-IHS, HPF and W-PCA. For QuickBird data, it is found that all fusion methods reproduce both color and spatial information close to the original image. Concerning the robustness against the noise, the Ehler method shows a good performance, whereas the W-PCA approach occasionally leads to similar or slightly better results. Comparing the performance of various fusion methods, it is shown that the Ehler method yields the best accuracy, followed by the W-PCA. The producer's and user's accuracies of the Ehler method are 89.94% and 90.34%, respectively, followed by 88.14% and 88.26% of the W-PCA method.

Since 29 May 2006 up to now, the hot mud volcano has erupted with enormous volume (the highest rate is 5×10 4 m 3/day) in Sidoarjo, Western Java, Indonesia. It covers the area around 6 km 2 and destructs important infrastructures (road, railroad, electricity, school, etc) including residence houses. The actual cause of this eruption is still debatable among scientist. This incident has been declared as national disaster by the Indonesian government. The location of disaster, which is in between two big cities, not only give a great impact to the citizen who lives in mud eruption surrounding area, but also people in two cities in term of electricity and distribution, communication, transportation and economy. Several actions have failed to stop the eruption, in order to prevent mud spreading area, man-made dam was constructed and the height reaches more than 15 meters. The eruption of sub surface material to the surface also brought up surface deformation problem. In this study, we will monitor this disaster in term of surface deformation and locate the deformation affected area by implementing Differential Interferometry Synthetic Aperture Radar (DInSAR) technique using ALOS-PALSAR data taken between the years 2006 and 2008. The subsidence more than one meter detected in surrounding area outside the dam.

The changes in forest cover in many parts of the world lead to increase the accumulation of atmospheric carbon and thus accelerate the process of global warming. Optical remote sensing has been used to map and quantify deforestation but its application is limited because of the presence of cloud coverage on the images. Recent availability of several space-borne synthetic aperture radar (SAR) missions has widened the scope of utilizing radar images for monitoring of forest cover change. The objective of this investigation is to examine the capability of SAR data to assess and map deforestation. The study area is located at the tropical forest region of southeastern Bangladesh. Shuttle Imaging Radar-C (SIR-C) data of 1994 and Advanced Land Observation Satellite (ALOS) PALSAR data of 2007 were used in this study. ALOS PALSAR data were orthorectified with Shuttle Radar Topographic Mission digital elevation model data. Image to image geometric registration was done between the two SAR scenes. Study area was clipped and separated as subsets. SIR-C data (L- and C-bands) was in dual polarization (HH and HV) and PALSAR (L-band) was in quad-polarization (HH, HV, VH, and VV). Five different categories of land covers (forest, upland soil/shrubs, lowland soil, settlements, and water/wetlands) were recognized on both SAR scenes. An additional class representing forest re-growth could be identified only on SIR-C image. Both the images were classified using maximum likelihood algorithms. The classification accuracy was computed from the randomly selected independent validation pixels. The accuracy for forest is more than 83% except users accuracy computed for PALSAR image. Forest was reduced from 18,000 to 13,800 ha in the region during the study period. The results of this study will be useful for understanding the applicability of SAR to map and quantify forest cover changes in the tropics.

Pan-sharpening is also known as image fusion, resolution merge, image integration, and multi sensor data fusion has been widely applied to imaging sensors. The purpose of pan-sharpening is to fuse a low spatial resolution multispectral image with a higher resolution panchromatic image to produces an image with higher spectral and spatial resolution. In this paper, we investigated these existing pan-sharpening methods based on visual and spectral analysis. And to achieve assess the accurate classification process, we proposed a support vector machine (SVM) based on radial basis function (RBF) kernel. In the Experimental results, a comparative performance analysis of techniques by various methods show that Gram-Schmidt followed by PCA perform best among all the techniques. Besides that, higher overall accuracy of Gram-Smidth (GS) fused image increase 0.90 percent. And also, the high producer's and user's accuracy average of Gram-Smidth (GS) fused for each of the classes and methods used was always reported greater than 91.8% and 91.11%, respectively, indicating the overall success of the performed classification. And the followed by PCA was 90.84% and 89.99.

Up till now, only linearly polarized microwave radiation are employed by the Synthetic Aperture Radar (SAR) systems onboard spaceborne platforms. In general, such linearly polarized SAR (LP-SAR) systems are very sensitive to Faraday rotation in the ionosphere and platform posture, both of which will contribute to system noise superposed on the resulting backscattering signature. So as to improve the situation, currently a novel Circularly Polarized Synthetic Aperture Radar (CP-SAR) sensor is developed in our Microwave Remote Sensing Laboratory, Chiba University. As an early stage of the development of this CP-SAR sensor, we will make use of an Unmanned Aerial Vehicle (UAV) platform, called as Josaphat Laboratory Experimental UAV (JX-1) for testing CPSAR capabilities. In this paper, we describe the CP-SAR hardware system design and CP-SAR parameters calculation with its results. The possibility of implementing a smaller antenna using the new CP-SAR technique than with conventional LP-SAR systems is shown. This research will contribute to the realization of a compact CP-SAR sensor, which can be installed on a small and low cost platform yielding a high accuracy SAR image data. The experience and knowledge of CP-SAR UAV experimental will be very valuable to realize a CP-SAR sensor onboard a small satellite platform as the final stage of the CP-SAR sensor development roadmap.

We have investigated elliptical annular ring microstrip antennas having a sine wave periphery both theoretically and experimentally. The proposed antenna gives a good circular polarization at the center frequency of 1.296 GHz, with measured 3 dB axial ratio bandwidth of about 0.73% (9.5 MHz) and impedance bandwidth of about 1.7% (22.0 MHz). Input and radiation characteristics are also examined for different sine wave amplitudes applied to the periphery of elliptical ring patch antenna.

Differential synthetic aperture radar interferometry (D-InSAR) is a technique capable of detecting land surface deformation. In this research, we use Advanced Land Observing Satellite (ALOS) Phased Array L-band SAR (PALSAR) data to investigate land subsidence in Jakarta during 2007 and 2008. It is found that four northern areas in the city exhibit clear indications of land subsidence. The location of the centre of subsidence is estimated, and the subsidence volume is evaluated for each area using the unwrapping method as a further process of using D-InSAR results. The subsidence depth and volume around the centre are estimated to be 10–22 cm and 0.2 × 105 – 4.2 × 105 m3, respectively, in the study time period. Comparison with ground survey data indicates that the D-InSAR analysis gives reliable estimates of the subsidence in an urban environment.

Forest cover is changing in different locations of the tropics and results in increasing atmospheric carbon. Monitoring of forest cover and deforestation using optical remote sensing is often limited because of the presence of cloud cover on the imagery, which restricts the mapping of forest cover in the desired season and time. Synthetic Aperture Radar (SAR) can penetrate cloud and is capable of providing data throughout the year. The objective of this investigation is to examine SAR data for mapping of tropical forest cover and deforestation. The study area is located at the Mato Grosso state of Amazonian forest, Brazil. The study utilized dual polarization Shuttle Imaging Radar (SIR)-C data of 1994 and Advanced Land Observation Satellite (ALOS) PALSAR polarimetric data of 2008. The images were classified using hybrid classifier. Additional optical images of Landsat satellite assisted in image interpretation procedure. Forest cover change is estimated and presented by a change map and matrix. The technique applied in this study can be extended for operational monitoring of forest cover and quantification of deforestation in other parts of the tropics.

Tropical forest cover is changing in many parts of the World and got attention to the global community since it is one of the main drivers of climate change. Microwave sensor data is useful to monitor forest coverage since presence of cloud often obscures for a continuous and periodic monitoring by optical sensors. The objective of this investigation was to examine the newly available Advanced Land Observation Satellite (ALOS) PALSAR polarimetric data for forest interpretation and mapping. The study area was located in the tropical forest region of South-eastern Bangladesh. The area covers with tropical wet evergreen and semi-evergreen forest, plantation, mixed bamboo and scrubby re-growth. Forest and forest mosaic could be separated from other land covers (water, agriculture shrimp farms etc). Forest appears in cyan in VV, VH and HV (RGB) composition, while forest mosaic in dark red and cyan. Large-scale plantation is distinguishable from natural vegetation based on texture. The study developed an interpretation key for the study region. Visually interpreted PALSAR image was digitized on-screen to prepare forest cover map. The map was verified with Landsat Enhanced Thematic Mapper Plus (ETM+) image, which was earlier validated on the ground. The result of this investigation will be useful for interpretation, delineation and mapping of forest cover using PALSAR polarimetric data in similar forest ecosystems.

A satellite-tracking left-handed circularly polarised triangular-patch array antenna has been developed to support the next generation of mobile satellite communications using Japanese Engineering Test Satellite VIII. The targeted minimum gain of the antenna was set to 5 dBic at an elevation of 48° in the Tokyo area, for applications of about a hundred kbit/s data transfer. The antenna is composed of three equilateral triangular patches and fed by a dual-proximity feed. The influence of the array configuration on the characteristic of this antenna is investigated using a method of moments (MoM) approach and a simple point source model. The measurement of the fabricated antenna was then performed to confirm the simulation results. The result showed that the 10 mm distance between the patch apex to the centre of the array antenna satisfies the target. The frequency characteristic of the fabricated antenna is 0.7% shifted to the higher frequency, and the maximum gain is 0.9 dB lower than the simulation results. The result also shows that the characteristic of the patch array antenna satisfies the specification at elevation angle El=48°, especially the 5 dBic gain coverage of the results covers the whole azimuth angle in a conical-cut plane at El=48°.

本論文では, ETS-VIIIを利用した移動体衛星通信実験に用いる移動局用アンテナとして開発した2周波共用三角形パッチアレーアンテナと, 衛星通信実験へ向けて行われた衛星通信模擬実験について述べる.はじめに送受信の2周波共用と20dB以上のアイソレーション, 更に5dBic以上の利得を確保するために, 給電のON, OFFのみで方位角方向のみにビーム切換を行うアンテナについて, モーメント法を利用した数値計算と測定から検討した.次に実際に使用した場合における放射特性を把握するため, アンテナを実験車両の屋根に搭載し, 高さ32mの建物に設置した模擬衛星局からの電波を利用して衛星通信模擬実験を行った.実験によるデータの解析結果により, 車両搭載時においても給電のON, OFFのみによりビーム切換が可能であることを確認し, 本アンテナが有効であることを示した.

A physical method was conducted to analyse scattered waves from burnt coal seam in order to estimate the thickness of fire scars. The model was composed of three media namely; air, burnt coal seam and peat. For computation purposes, the equivalent circuit of this model was conducted using a classical transmission line circuit method. The relationship between backscattering coefficient and thickness of burnt coal seam was defined in terms of the logarithm of the reflection coefficient (in power). The analysis result was confirmed by simulation using a Finite Difference Time Domain (FDTD) method. The simulation was performed using a two-dimensional (2-D) finite-difference model for scattered waves from a burnt coal seam. The model used the equations of scattered electromagnetic fields that were derived from Maxwell's equations. A Mur method was used to surround the simulation space and absorb the outward travelling waves. Analysis and simulation results were similar. Subsequently, the model was applied to estimate the thickness of burnt coal seam in central Borneo fire events, that occurred in 1997, using a Japanese Earth Resources Satellite (JERS-1) SAR data. Results showed that fire scars in the study area reached 0.52 m in depth (thickness). This agrees with ground measurements.

We analysed the interaction of microwaves with a burnt coal seam. This analysis approximated a burnt coal seam as a microwave absorber. The impedance of an incident wave with horizontal polarization (transverse magnetic mode) was derived in order to calculate the relationship between burnt coal seam thickness and backscattering coefficient. The result was confirmed by simulating scattering on a burnt coal seam using the Finite Difference Time Domain (FDTD) method. Both were similar. This relationship was used to estimate burnt coal seam thickness in central Borneo using Japanese Earth Resources Satellite (JERS-1) SAR data. Estimation results and ground data were similar.

The objective of this study was to investigate improvement of classification accuracy using synergism between textural features and spectral information. Satellite data used in this study are multispectral SPOT HRV, Landsat-TM, and JERS-1 SAR images. Spectral information applied for data compression, is standard principal component analysis, while speckle noise present at JERS-1 SAR image was reduced using wavelet transform. The first order statistic of variance and the second order texture statistic of entropy found in the literature were used. Several datasets were generated using spectral extraction, textural features, and their combination. Based on the maximum likelihood classifier, land use categories of the study area were discriminated. The result shows that combined use of spectral and texture information together significantly improved the accuracy of land use classification.