Change detection analysis of tornado disaster using conditional copulas and Data Fusion for cost-effective disaster management

Abstract

The up-to-date results are presented from an ongoing study of the Data Fusion of multi-temporal and multi-sensor satellite datasets for near real time damage and debris assessment after a tornado disaster event. The space-borne sensor datasets comprising of: (i) C-band SAR dataset from RADARSAT-2; (ii) Multi-Spectral (MS) optical dataset including NIR from RapidEye; (iii) MS and panchromatic dataset of Advanced Linear Imaging (ALI), are studied for multi-sensor data fusion. A combined approach of multipolarized radiometric and textural feature extraction, and statistical learning based feature classification is devised for fine tuning of the complex and generalized change detection model. We also investigated the use of multi-variate conditional copula as a classifier technique, by formulating the change and no-change as a binary-class classification problem in this study. The classification results from the above technique are used for assessment of damage and debris cover after the tornado disaster event. The performance of the above approach yields a very significant Kappa accuracy up to 75%. A 10-fold cross validation strategy is used for quantitative analysis of the performance of the classification model. This study will be further extended for modelling the effect of incidence angle discrepancies or climatic condition variances, which will address the heterogeneity factor in terms of local statistics of the dataset.

Natural Disaster Application on Big Data and Machine Learning: A Review

“…Citation Long-term risk assessment and reduction [12], [13], [14], [15] [16] Forecasting and Predicting [17], [18], [19], [20], [21] [27], [28], [29], [30], [31], [32], [33], [ 34], [35], [36] Early warning Damage [47], [48], [49], [50][51] [52][53][54 ] Damage Assessment [55], [15], [56], [15], [55], [56][57][5 8] Post-disaster Coordination and Response [59], [60][61][62] [63] Based on the results of table 2, part of the research is often done in the area of monitoring and detection. The monitoring and detection section is a component that is usually researched because based on the results of the paper to obtain data sources in the process of monitoring and detection is straightforward.…”

“…One paper that uses these data is Mendoza's research [21] and Resch's research [15].The satellite image is also used as a data source. The satellite image came from NASA like the Gokaraju study [30]. Besides that, there is satellite imagery that uses SAR (Synthetic Aperture Radar)…”

Post-Hurricane Damage Assessment on Greenhouse Fields With Use of Sar Data

“…These are especially significant for monitoring and management of disaster events. In the literature, disaster management and post-disaster assessment studies that use SAR data examined, it is remarkable that there are more research works on two topics: forest damage detection and flood detection (Richards et al, 1987;Fransson et al, 2002;Ermert et al, 2012;Eriksson et al, 2012;Gokaraju and Turlapaty, 2015). In this study, a method is proposed using S1A data and S2A satellite data for near real time damage assessment after a tornado disaster event.…”

Change Detection Based on Multi-Grained Cascade Forest and Multi-Scale Fusion for SAR Images

“…Remote sensing image change detection is a process to detect and extract surface changes between images obtained at the same scene but at different times [1][2][3]. In recent years, the technology of remote sensing image change detection has become widely used in many fields [4][5][6][7][8][9], such as the aspect on acquisition and update of geographic information data [10,11], the detection and assessment of natural disaster [12], and the military [13,14]. In particular, in the case of natural disasters evaluation [15,16], if the subtle changes in areas where disaster will occur can be detected promptly and the corresponding measure is taken, the loss of life and property caused by natural disasters will be greatly reduced.…”