KEYWORDS: Radar, Signal processing, 3D image processing, Extremely high frequency, 3D metrology, Sensors, 3D acquisition, Distance measurement, 3D displays, Signal detection
In recent years, crisis management's response to terrorist attacks and natural disasters, as well as accelerating rescue operations has become an important issue. We aim to make a support system for firefighters using the application of various engineering techniques such as information technology and radar technology. In rescue operations, one of the biggest problems is that the view of firefighters is obstructed by dense smoke. One of the current measures against this condition is the use of search sticks, like a blind man walking in town. The most important task for firefighters is to understand inside situation of a space with dense smoke. Therefore, our system supports firefighters' activity by visualizing the space with dense smoke. First, we scan target space with dense smoke by using millimeter-wave radar combined with a gyro sensor. Then multiple directional scan data can be obtained, and we construct a 3D map from high-reflection point dataset using 3D image processing technologies (3D grouping and labeling processing). In this paper, we introduce our system and report the results of the experiment in the real smoke space situation and practical achievements.
In recent years, crisis management in response to terrorist attacks and natural disasters, as well as accelerating rescue operations has become an important issue. Rescue operations greatly influence human lives, and require the ability to accurately and swiftly communicate information as well as assess the status of the site. Currently, considerable amount of research is being conducted for assisting rescue operations, with the application of various engineering techniques such as information technology and radar technology.
In the present research, we believe that assessing the status of the site is most crucial in rescue and firefighting operations at a fire disaster site, and aim to visualize the space that is smothered with dense smoke. In a space filled with dense smoke, where visual or infrared sensing techniques are not feasible, three-dimensional measurements can be realized using a compact millimeter wave radar device combined with directional information from a gyro sensor. Using these techniques, we construct a system that can build and visualize a three-dimensional geometric model of the space. The
final objective is to implement such a system on a wearable computer, which will improve the firefighters' spatial perception, assisting them in the baseline assessment and the decision-making process. In the present paper, we report the results of the basic experiments on three-dimensional measurement and visualization of a space that is smoke free, using a millimeter wave radar.
This paper describes a precise geometric correction method considering elevation effects for NOAA/AVHRR of GMS images, which is mandatory for long-term global environmental monitoring studies.
First, using the so-called systematic geometric correction, the correspondences of sub-sampled image pixels to their map coordinates are calculated. And, the correspondences of sub-sampled map locations, which are the corner points of blocks, to image pixels are calculated to speed up the inverse transform to find for a pixel on the map coordinates to the corresponding pixel in the image coordinates using the bilinear interpolation of the four corner points of a block. For precise geometric correction, the residual errors of the systematic correction are measured using many GCP templates. GCP templates in the map coordinates are provide using DCW. Templates in the image coordinates are generated using the bilinear Interpolation. Also, the templates of high elevation areas are modified to include the elevation effects, using the height from GTOPO30 and satellite sensor geometry. Then, the residual errors are acquired by template matching and affine transform coefficients are calculated to remove the residual errors. And if the difference between the average error and each GCP is more than one pixel, these GCP’s are removed and new affine transform coefficients are recalculated iteratively until all errors reach within one pixel. Then, mapping of each pixel is done using the correspondence of four corner block points and image coordinates modified by affine transform, but for high elevation areas blocks are divided into pixels according to their elevation. The accuracy of within one pixel; i.e. 0.01 degree for NOAA/AVHRR and GMS/VIS and 0.04 degrees for GMS/IR is obtained for NOAA images received at Tokyo and the stitched ones received at Tokyo and Bangkok and also GMS full disk images.
We have recently developed a new, fully automated method for detecting and visualizing narrow rivers in Amazon forests from 3-look JERS-1 SAR images using strong isolated scatterers lined up intermittently along rivers. The resulting approximate range of waterways correspond astonishingly well with waterways visible in the cloudless areas of the near IR images of JERS-1 VNIR data observed one week later. We applied this method to more than 250 images of multi-temporal 4 look JERS-1 SAR scenes of tropical forest areas, such as the Amazon from May, 1996 to February, 1997, New Guinea from November, 1995 to March 1996, and Congo Basin of February and November, 1996. The resulting change in brightness (or length) of rivers corresponds quite well with the change in the average monthly rainfall data of the nearby areas. We therefore conclude that the brightness of the waterways in the JERS-1 SAR images are qualitative indicators of water flow in these rivers.
We have developed a new, fully automatic method for detecting and visualizing narrow open-water rivers from 3- look JERS-1 SAR images of Amazon rainforests. We expect this method to become an operational tool for detecting river water levels in remote tropical rainforests for the purposes of environmental and disaster monitoring. To demonstrate its use, we apply this method to eight continuous and one isolated Amazon scene. The resulting approximate range of waterways corresponds highly with those seen in JERS-1 VNIR image data observed one week later in cloud-free areas. Estimates from the optical JERS-1 VNIR images give the minimum detectable width of those visualized rivers to be approximately 20 m. The brightness of the river can be used to estimate the width of the fiver from approximately 20m to approximately 150m. In wider rivers, dark areas become predominant.
Recent attention on global environmental changes has stimulated the development of large scale global information systems. Satellite images play a very important role for understanding these global changes, but their data size are very large. Magnetic tape archivers are often used for them because of their capacity, but their capacity cannot be increased except by adding a new archiver which is independent of the others. We designed a scalable tape archiver, which consists of several element archivers and can be extended to any number of archivers. Each element archiver can transfer a cassette to a neighboring one. In the scalable tape archiver, performance strongly depends on data placement and usage of the tape drives. In this paper, we propose several cassette tape migration algorithms to balance the load across the element archivers and also evaluate the performance of the various proposed algorithms through simulation.
Techniques have been developed which have been imaging optically opaque regions using an electromagnetic wave radar in order to estimate the location of the objects in those regions. One important application of these techniques is the detection of buried pipes and cables. In the case of underground radar, its image quality often becomes low because the nature of the soil is not uniform and an electromagnetic wave is attenuated in soil. Hence, the method which improves the quality of the radar images is required. In this paper, we point out that the quality of underground images can be improved significantly by means of the block migration method. In this method LOT (Lapped Orthogonal Transform) was applied. LOT is a new block transform method in which basis functions overlap in adjacent blocks, and it has a fast computation algorithm. In addition to above, we propose a method of estimating dielectric constant in soil using the processed images. The result of applying the block migration method to the underground radar images are presented. It points out the good capability for the image quality improvement and the application of LOT can improve the influence by blocking and the processing time. Also the dielectric constant in each block can be estimated accurately.
An attributed relational graph (ARG) is introduced into our NOAA satellite image database system. The node and the branch of an ARG denotes a classified region and a spatial relationship between adjacent regions, respectively. Furthermore, a few attributes of a node/branch help to express numerical shape features of regions. Similarity retrieval thereby turns out to be equivalent to graph matching. The similarity retrieval process of the system is as follows: (1) select a visual example image as a query and generate its graph structure, (2) calculate an optimal graph matching cost between a query graph and an archived graph in the database, utilizing algorithm A* with heuristic information, (3) choose archived images in the ascending order of a corresponding matching cost.
A new image magnification method, called 'IM-GPDCT' (image magnification applying the Gerchberg-Papoulis (GP) iterative algorithm with discrete cosine transform (DCT)), is described and its performance evaluated. This method markedly improves image quality of a magnified image using a concept which restores the spatial high frequencies which are conventionally lost due to use of a low pass filter. These frequencies are restored using two known constraints applied during iterative DCT: (1) correct information in a passband is known and (2) the spatial extent of an image is finite. Simulation results show that the IM- GPDCT outperforms three conventional interpolation methods from both a restoration error and image quality standpoint.
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