Various natural disasters occur on the earth. In Japan, heavy rains and earthquakes have caused particularly severe damage. We focus on landslides caused by them. This study proposes a landslide detection method using synthetic aperture radar (SAR). SAR uses microwave observations, and microwaves are reflected according to the properties of materials on the earth’s surface. In addition, microwave amplitude and phase information can be obtained, and these are used for various analyses. They are often used to detect disasters, mostly to detect changes caused by disasters. For example, change detection by differential reflection intensity, analysis of terrain variation by phase difference, and detection of material by properties of polarization. Therefore, multiple SAR data are required for disaster detection. However, in the event of a disaster, rapid detection of the damaged area is necessary. For this reason, this study investigates a method for detecting the damaged area from a single SAR data. As a research method, instance segmentation is conducted using YOLOv8. The SAR data used in the experiments were obtained for the Noto Peninsula earthquake. This disaster occurred on January 1st in 2024 in the Noto region of Ishikawa Prefecture and caused extensive damage. Images of landslide areas were obtained from SAR data, annotated and trained instance segmentation by YOLOv8 to evaluate test performance.
In recent years, natural disasters have caused serious damage. In particular, landslides caused by earthquakes are damaging. However, it is difficult to predict when and where natural disasters will occur. Therefore, this study was conducted on early detection of landslides. SAR (Synthetic Aperture Radar) is a remote sensing technology. It uses microwaves and can observe day and night in all weather conditions. But this SAR data is a grayscale image, which is difficult to analyze without specialized knowledge. Therefore, we decided to use machine learning to detect changes in disasters that appear in SAR data. There are two machine learning models called pix2pix and pix2pixHD for image transformation. The objective of this study is to detect changes of surface by transforming pseudo-optical images from SAR data using machine learning. Two machine learning models were used for training, with test images and actual disaster data input. Simple terrain, such as forests only, was highly accurate, but complex terrain was difficult to generate. About actual disaster data, something like disaster-induced changes appeared in the converted images. However, we found it difficult to distinguish bare area from grassland in the output images. In the future, it is necessary to consider the combination of data to be used for learning.
Atmospheric particulate matters (PM) are tiny pieces of solid or liquid matter associated with the Earth’s atmosphere. They are suspended in the atmosphere as atmospheric aerosol. Recently, density of fine particles PM2.5, diameter of 2.5 micrometers or less, from China is serious environmental issue in East part of Asia. In this study, the authors have developed a PM2.5 density distribution visualization system using ground-level sensor network dataset and Mie lidar dataset. The former dataset is used for visualization of horizontal PM2.5 density distribution and movement analysis, the latter dataset is used for visualization of vertical PM2.5 density distribution and movement analysis.
Fourteen and half years of vicarious calibration and cross calibration for Terra/ASTER/VNIR (Visible and Near Infrared Radiometer, one of three ASTER mission instruments onboard Terra satellite) are summarized together with an error budget analysis. As the results, it is found that Onboard Calibration (OBC) data derived Radiometric Calibration Coefficient (RCC) is situated within a range of uncertainty of vicarious calibration except the period after approximately 4000 days after launch. In other word, uncertainty of OBC RCC is overlapped with uncertainty of vicarious calibration for about 4000 days since launch. Meanwhile, cross calibration with MODIS, MISR and ETM+ supports vicarious calibration of VNIR. In other word, Cross RCC shows much consistent trend to Vicarious RCC than OBC RCC.
The authors have developed HuVisCam, a human vision simulation camera, that can simulate not only Purkinje
effect for mesopic and scotopic vision but also dark and light adaptation, abnormal miosis and abnormal mydriasis
caused by the influence of mydriasis medicine or nerve agent This camera consists of a bandpass pre-filter, a
color USB camera, an Illuminator and a small computer. In this article, improvement of HuVisCam for specific
color perception is discussed. For persons with normal color perception, simulation function of various types of
specific color perception is provided. In addition, for persons with specific color perception, color information
analyzing function is also provided.
KEYWORDS: Data acquisition, LIDAR, Meteorology, Data modeling, Ozone, Atmospheric modeling, Data analysis, Cameras, Satellites, Magnetic resonance imaging
A web-base data acquisition and management system for GOSAT (Greenhouse gases Observation SATellite) validation lidar data-analysis has been developed. The system consists of data acquisition sub-system (DAS) and data management sub-system (DMS). DAS written in Perl language acquires AMeDAS (Automated Meteorological Data Acquisition System) ground-level local meteorological data, GPS Radiosonde upper-air meteorological data, ground-level oxidant data, skyradiometer data, skyview camera images, meteorological satellite IR image data and GOSAT validation lidar data. DMS written in PHP language demonstrates satellite-pass date and all acquired data. In this article, we briefly describe some improvement for higher performance and higher data usability. GPS Radiosonde upper-air meteorological data and U.S. standard atmospheric model in DAS automatically calculate molecule number density profiles. Predicted ozone density prole images above Saga city are also calculated by using Meteorological Research Institute (MRI) chemistry-climate model version 2 for comparison to actual ozone DIAL data.
Greenhouse gases Observation SATellite (GOSAT) was launched to enable the precise monitoring of the density
of carbon dioxide by combining global observation data sent from space with data obtained on land, and with
simulation models. In addition, observation of methane, another greenhouse gas, has been considered. For
validation of GOSAT data products, ground-base observation with Fourier Transform Spectrometer (FTS),
aerosol lidar and ozone-DIAL (DIfferencial Absorption Lidar) at Saga University, JAPAN has been continued
since March, 2011. In this article, observation results obtained from aerosol lidar are reported.
An web-base data acquisition and management system for GOSAT (Greenhouse gases Observation SATellite)
validation lidar data analysis is developed. The system consists of data acquisition sub-system (DAS) and data
management sub-system (DMS). DAS written in Perl language acquires AMeDAS ground-level meteorological
data, Rawinsonde upper-air meteorological data, ground-level oxidant data, skyradiometer data, skyview camera
images, meteorological satellite IR image data and GOSAT validation lidar data. DMS written in PHP language
demonstrates satellite-pass date and all acquired data.
KEYWORDS: Device simulation, Cameras, Cones, Rods, Luminous efficiency, Human vision and color perception, Medicine, Nerve agents, Retina, Imaging systems
HuVisCam, a human vision simulation camera, that can simulate not only Purkinje effect for mesopic and scotopic
vision but also dark and light adaptation, abnormal miosis and abnormal mydriasis caused by the influence of
mydriasis medicine or nerve agent is developed. In this article, details of the system are described.
A method for detection of red tide by means of remote sensing reflectance peak shift is proposed together with suspended solid
influence eliminations. 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. Based on this fact, it is capable to detect
red tide using high wavelength resolution of spectral-radiometers. 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 with suspended solid influence
eliminations. Furthermore, a possibility of red tide detection with polarized radiance measurements is discussed through polarization
camera derived sue surface imagery data, in particular, for non-spherical shape of red tide.
A new change detection method for remotely sensed images is proposed. This method can be applied to two
images which have different number of spectral bands and/or have different spectral ranges. The proposed
method converts two multi-spectral-multi-temporal images into two sets of canonical variate images which have
limited correlation called the canonical correlation. Then, one or more canonical variate images which are the
most suitable for change detection are selected and change detection regions in the original images are extracted
by using statistical modeling and statistical test. In this paper, the detail of the proposed method is described.
Some experiments using simulated multi-spectral-multi-temporal images based on spectral profiles in ASTER
Spectral Library are conducted to confirm change detection accuracy. The experimental results show reasonable
changed regions and their change quantities.
A method for aerosol refractive index estimation with ground based polarization measurement data is proposed. The
proposed method uses a dependency of refractive index on p and s polarized down welling solar diffuse irradiance. It is
much easy to measure p and s polarized irradiance on the ground with a portable measuring instrument rather than solar
direct, diffuse and aureole measurements. Through theoretical and simulation studies, it is found that the proposed
method show a good estimation accuracy of refractive index using measured down welling p and s polarized irradiance
data with a measuring instrument pointing to the direction which is perpendicular to the sun in the principal plane. Field
experimental results also show a validity of the proposed method in comparison to the estimated results from the
conventional method with solar direct, diffuse and aureole measurement data.
A method for reflectance based vicarious calibration with aerosol refractive index and size distribution estimation using
atmospheric polarization irradiance data is proposed. It is possible to estimate aerosol refractive index and size
distribution with atmospheric polarization irradiance measured with the different observation angles (scattering angles).
The Top of the Atmosphere (TOA) or at-sensor radiance is estimated based on atmospheric codes with estimated
refractive index and size distribution then vicarious calibration coefficient can be calculated by comparing to the
acquired visible to near infrared instrument data onboard satellites. The estimated TOA radiance based on the proposed
method is compared to that with aureole-meter based approach which is based on refractive index and size distribution
estimated with solar direct, diffuse and aureole (Conventional AERONET approach). It is obvious that aureole-meter is
not portable, heavy and large while polarization irradiance measurement instruments are light and small (portable size
and weight).
KEYWORDS: Thermography, Data acquisition, Cameras, Image retrieval, Temperature metrology, Data conversion, Imaging devices, Data storage, Human-machine interfaces, Control systems
TZ-SCAN is a simple and low cost thermal imaging device which consists of a single point radiation thermometer
on a tripod with a pan-tilt rotator, a DC motor controller board with a USB interface, and a laptop computer for
rotator control, data acquisition, and data processing. TZ-SCAN acquires a series of zig-zag scanned data and
stores the data as CSV file. A 2-D thermal distribution image can be retrieved by using the second quefrency
peak calculated from TZ-SCAN data. An experiment is conducted to confirm the validity of the thermal retrieval
algorithm. The experimental result shows efficient accuracy for 2-D thermal distribution image retrieval.
"HYCLASS", a new hybrid classification method for remotely sensed multi-spectral images is proposed. This
method consists of two procedures, the textural edge detection and texture classification. In the textural edge
detection, the maximum likelihood classification (MLH) method is employed to find "the spectral edges", and
the morphological filtering is employed to process the spectral edges into "the textural edges" by sharpening the
opened curve parts of the spectral edges. In the texture classification, the supervised texture classification method
based on normalized Zernike moment vector that the authors have already proposed. Some experiments using a
simulated texture image and an actual airborne sensor image are conducted to evaluate the classification accuracy
of the HYCLASS. The experimental results show that the HYCLASS can provide reasonable classification results
in comparison with those by the conventional classification method.
At the previous conference, the authors are proposed a new unsupervised texture classification method based on the genetic algorithms (GA). In the method, the GA are employed to determine location and size of the typical textures in the target image. The proposed method consists of the following procedures: 1) the determination of the number of classification category; 2) each chromosome used in the GA consists of coordinates of center pixel of each training area candidate and those size; 3) 50 chromosomes are generated using random number; 4) fitness of each chromosome is calculated; the fitness is the product of the Classification Reliability in the Mixed Texture Cases (CRMTC) and the Stability of NZMV against Scanning Field of View Size (SNSFS); 5) in the selection operation in the GA, the elite preservation strategy is employed;
6) in the crossover operation, multi point crossover is employed and two parent chromosomes are selected by the roulette strategy; 7) in mutation operation, the locuses where the bit inverting occurs are decided by a mutation rate; 8) go to the procedure 4. However, this method has not been automated because it requires not only target image but also the number of categories for classification. In this paper, we describe some improvement for implementation of automated texture classification. Some experiments are conducted to evaluate classification capability of the proposed method by using images from Brodatz's photo album and actual airborne multispectral scanner. The experimental results show that the proposed method can select appropriate texture samples and can provide reasonable classification results.
A new unsupervised texture classification method based on the genetic algorithms (GA) is proposed. In the method, the GA are employed to determine location and size of the typical textures in the target image. The proposed method consists of the following procedures: (1) the determination of the number of classification category; (2) each chromosome used in the GA consists of coordinates of center pixel of each training area candidate and those size; (3) 50 chromosomes are generated using random number; (4) fitness of each chromosome is calculated; the fitness is the product of the Classification Reliability in the Mixed Texture Cases (CRMTC) and the Stability of NZMV against Scanning Field of View Size (SNSFS); (5) in the selection operation in the GA, the elite preservation strategy is employed; (6) in the crossover operation, multi point crossover is employed and two parent chromosomes are selected by the roulette strategy; (7) in mutation operation, the locuses where the bit inverting occurs are decided by a mutation rate; (8) go to the procedure 4. Some experiments are conducted to evaluate classification capability of the proposed method by using images from Brodatz's photo album and actual airborne multispectral scanner. The experimental results show that the proposed method can select appropriate texture samples and can provide reasonable classification results.
A new method for selection of appropriate training areas which are
used for supervised texture classification is proposed. In the method, the genetic algorithms (GA) are employed to determine the appropriate location and the appropriate size of each texture category's training area. The proposed method consists of the following procedures: 1) the determination of the number of classification category and those kinds; 2) each chromosome
used in the GA consists of coordinates of center pixel of each training area candidate and those size; 3) 50 chromosomes are generated using random number; 4) fitness of each chromosome is calculated; the fitness is the product of the Classification Reliability in the Mixed Texture Cases (CRMTC) and the Stability of NZMV against Scanning Field of View Size (SNSFS); 5) in the selection operation in the GA, the elite preservation strategy is employed;
6) in the crossover operation, multi point crossover is employed and two parent chromosomes are selected by the roulette strategy; 7) in mutation operation, the locuses where the bit inverting occurs are decided by a mutation rate; 8) go to the procedure 4. Some experiments are conducted to evaluate searching capability of appropriate training areas of the proposed method by using images from Brodatz's photo album and their rotated images. The experimental results show that the proposed method can select appropriate training areas much faster than conventional try-and-error method. The proposed method has been also applied to supervised texture classification of airborne multispectral scanner images. The experimental results show that the proposed method can provide appropriate training areas for reasonable classification results.
An automated method that can select corresponding point candidates is developed. This method has the following three features: 1) employment of the RIN-net for corresponding point candidate selection; 2) employment of multi resolution analysis with Haar wavelet transformation for improvement of selection accuracy and noise tolerance; 3) employment of context information about corresponding point candidates for screening of selected candidates. Here, the 'RIN-net' means the back-propagation trained feed-forward 3-layer artificial neural network that feeds rotation invariants as input data. In our system, pseudo Zernike moments are employed as the rotation invariants. The RIN-net has N x N pixels field of view (FOV). Some experiments are conducted to evaluate corresponding point candidate selection capability of the proposed method by using various kinds of remotely sensed images. The experimental results show the proposed method achieves fewer training patterns, less training time, and higher selection accuracy than conventional method.
A sensitivity analysis of our own atmospheric code for a vicarious calibration of ADEOS/AVNIR was conducted. It was found that the most significant error source is the imaginary part of the complex refractive index of aerosol under the certain condition as well as a Mie phase function derived from the index through an inversion. A sensitivity of the phase function on the top of the atmosphere radiance was evaluated with our own atmospheric code including a radiative transfer code.
The Advanced Spaceborne Thermal Emission and Reflection Radiometer (ASTER) consisting of a visible to near infrared radiometer, a shortwave infrared (SWIR) radiometer and thermal infrared radiometer will be onboard the Earth Observing System's (EOS) AM-1 platform. The characteristics of ASTER have been published in several papers. In particular, the calibration plan for ASTER has been described in detail. One of the important issues for the calibration plan of ASTER is the determination of a set of calibration coefficients using preflight calibration, onboard calibration, cross-calibration and vicarious calibration data. In order to establish a method for determination of a set of calibration coefficients, a preliminary field campaign was conducted at Lunar Lake and Railroad Valley Playas in Central Nevada in the USA in June 1996. The procedures and methods used and the data collected during the field campaign are briefly described here together with the current plans for ASTER calibration activities and a method for determining a set of calibration coefficients.
ASTER is a high resolution optical sensor for observing the Earth in a five-year mission on the EOS AM1 platform to be launched in 1998. ASTER consists of three radiometers. VNIR has three bands in the visible and near-infrared region, SWIR has six bands in the shortwave infrared region, and TIR has five bands in the thermal infrared region. The ASTER project is establishing a calibration plan including calibration requirements to the contractors. The major instrument characteristics specified are spectral characteristics, offset, nonlinearity of response, absolute responsivity, polarization effect and stray light effect. The ASTER pre- flight calibration of VNIR and SWIR adopts the working standard large integrating sphere of 1 m in diameter whose radiance levels are traceable to the primary standard fixed point blackbody. This is similar to the prelaunch calibration system of OPS of JERS-1 launched in 1992. The onboard calibration devices of VNIR and SWIR are halogen lamps and photodiode monitors used once in sixteen days. These calibrators are duplicated and used alternately to increase the reliability. The offsets of VNIR and SWIR are checked by looking at the dark side of the Earth. The TIR is unable to see the dark space. The temperature of the onboard blackbody of TIR remains at 270 K in the short term calibration for the offset calibration, and is varied from 270 K to 340 K in the long term calibration for the offset and gain calibration once in sixteen days. The TIR onboard blackbody is calibrated against a standard blackbody in a vacuum chamber before launch. The standard blackbody has a hood of 330 mm diameter and 600 mm length, the emissivity of more than 0.995 and the temperature range of 100 K to 400 K.
KEYWORDS: Data processing, Data acquisition, Data archive systems, Telecommunications, Double positive medium, Spatial resolution, Short wave infrared radiation, Radiometry, Space operations, Algorithm development
ASTER instrument is a high spatial resolution imager on board the EOS-AM1 platform, which will be launched in mid 1998, and will provide the Earth's surface spectral data in the VNIR, SWIR, and TIR wavelength regions to the science community in the world. ASTER data will be captured by U.S. ground system via TDRSS, processed to level 0 data and then transferred to the ASTER ground data system in Japan for level 1 data products generation within an appropriate timeline. The system will produce approximately 780 scenes (about 80 GByte) per day to meet scientists requirements on data processing, data distribution, and so on. In this paper, key issues of the development of ASTER ground data processing system are pointed out and then a basic concept of its possible implementation is discussed to resolve them, considering COTS technology. Level 1 data product is ASTER standard product and the level 1 data product generation is an essential part of the ASTER data processing system. Discussion concentrates on this part. Finally, the development program of the system also is mentioned.
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