Masafumi Hosokawa

Hybrid radio frequency identification system for use in disaster relief as positioning source and emergency message boards

We developed a system that uses radio frequency identification (RFID) tags both as the source of location information and as data storage units to record messages or information in disaster situations. Our system uses hybrid RFID tags, which consist of a passive (non-battery) tag and an active (battery-driven) tag. The system has been evaluated in disaster prevention trainings by local communities and rescue teams.

A remote sensing data classification method using self-organizing map

A supervised classification method using a self-organizing map (SOM) is proposed to classify remote sensing data. The SOM structure is composed of two layers. One is an input layer with nodes corresponding to spectral bands. The other is an output layer with square array of nodes. First, a feature map on the output layer is generated by inputting training data into SOM. Each node in the feature map cannot be corresponding to a category because the number of nodes is generally greater than those of training data. Thus, a cluster map is generated by comparing differentials among weight vectors in nodes. Secondly, the training data is re-inputted into the cluster map to find the relationship between clusters and categories, that is, the cluster including a fired node is labeled as the category to which the training data belongs. In consequence of mapping, the category map is obtained from the feature map. The proposed classification method extracts liquefied area in Kobe (Japan) damaged by the 1995 Hyogoken Nanbu earthquake using the SPOT HRV data and the category map. As an experimental result, it is shown that classification accuracies of the proposed method are higher than those of the maximum likelihood and the backpropagation methods.

A degree estimation model of earthquake damage using temporal coherence ratio

A degree of earthquake damage can be estimated using temporal decorrelation by employing a coherence ratio which is defined by dividing a post-event coherence image by a pre-event image. In the case of applying both C and L bands SAR data for evaluating the damage of the 1995 Hyogoken-Nanbu Earthquake in Japan, the probability of the degree of damage could be approximated by a linear function of the coherence ratio. In this paper, we examine whether the post-earthquake damage estimation model is applicable for the 1999 Kocaeli earthquake in Turkey as another case. As a result, significant correlation between the probability of the degree of damage and the grade of damage surveyed by disaster researchers is also clarified by employing the coherence ratio computed from three ERS- 1/2 SAR data set including the event.

Earthquake intensity estimation and damage detection using remote sensing data for global rescue operations

In order to support global rescue operations, we propose a new earthquake damage detection method based on a combination of both the result estimated by using earthquake information (magnitude, location of source, detailed ground conditions, and distance attenuation equation), and change detection using multi-temporal SAR data. First, to find collapsed buildings and houses on the earths surface, we adopt a difference image calculated from multi-temporal SAR images observed before and after the earthquake. Next, to estimate seismic intensity and probability of destruction caused by the earthquake, we apply an earthquake engineering model. Finally, damaged area is calculated using a logical AND of difference image and the destruction probability. In order to show that we can obtain a damage detection map which corresponds with the actual damage of houses, we applied the method to simulations of the 2008 Sichuan earthquake in China.

Earthquake damage detection using remote sensing data

In this study, we propose a new earthquake damage detection method based on a combination of the results estimated by using earthquake information (magnitude and location of source) and the change of the earths surface observed by SAR. As a map produced by the detection method has less noise than a coherence ratio image, we found that the proposed method has better detection ability than that which only uses the change of coherence value.

Damage estimation model using temporal coherence ratio

This paper presents a damage estimation model suitable for evaluating earthquake damage using coherence images derived from interferometric SAR data. The damage degree is estimated through temporal decorrelation by employing a coherence ratio which is defined by dividing a post-event coherence image by a pre-event image. The temporal coherence ratio is confirmed to be closely correlated with the probability of the damage degree. As a result of applying both C and L bands SAR data for evaluating the damage of the 1995 Hyogoken-Nanbu Earthquake in Japan, the cumulative probability of the damage degree can be approximated by a linear function of the coherence ratio.

Three-Way Pinpointing of Emergency Call from RFID-Reader-Equipped Cellular Phone

The ability to accurately pinpoint the point of origin of an emergency call can greatly increase response times of emergency services. Emergency calls made from cellular phones can only be traced by the Global Positioning System (GPS) or cell-based positioning, which are sometimes unacceptably inaccurate; they cannot provide information on, e.g. the exact floor of a building and also suffer from blind spots. We have been developing a system that can determine the location of a cellular phone using in-built passive or active radio-frequency identification (RFID) readers and GPS receivers. This paper introduces the outline of the prototype system.

Pinpointing the Place of Origin of a Cellular Phone Emergency Call Using Active RFID Tags

When police, fire or ambulance personnel receive an emergency call, they must pinpoint its place of origin in order to respond quickly. When such a call is made from a cellular phone, its place of origin can be determined by using GPS or a cell-based positioning method. However, these methods are sometimes inaccurate and have blind spots. We developed a system for determining location using RFID-reader- equipped cellular phones and RFID tags. We outline the prototype system here.

Polarimetric SAR data classification method using the self-organizing map

In this paper, we introduce a supervised classification method, which differentiates polarimetric SAR data into three categories using a self-organizing map (SOM) and a counter propagation learning approach after identifying the appropriate scattering classes. This classifier produces category maps corresponding to the Kohonen layers using training data for each scattering class. The SAR data are classified by inputting both like- and cross-polarization power elements into the learned SOM. In the experiment, PI-SAR data are employed since the resolution of aerial SAR data is higher than that of SAR data obtained from space. The proposed method yields higher-accuracy classifications than do conventional methods.

Earthquake risk evaluation using landforms processed by unsupervised classification method

We present an earthquake risk evaluation using landforms classified by unsupervised method and a Digital Elevation Model (DEM) in order to get ground condition without surface study. The classified landforms are adopted for the amplification factor in order to calculate the peak ground velocity (Vmax) of the ground motion. We demonstrate the performance of the proposed method by mean of evaluation of earthquake intensity of the 2008 Iwate-Miyagi earthquake in Japan.