Daniela Faur

Data Analytics for Rapid Mapping: Case Study of a Flooding Event in Germany and the Tsunami in Japan Using Very High Resolution SAR Images

In this paper, we present data analytics for a quantitative analysis in a rapid mapping scenario applied for damage assessment of the 2013 floods in Germany and the 2011 tsunami in Japan. These scenarios are created using preand postdisaster TerraSAR-X images and a semi-automated processing chain. All our dataset is tiled into patches and Gabor filters are applied as a primitive feature extraction method to each patch separately. A support vector machine with relevance feedback is implemented in order to group the features into categories. Once all categories are identified, these are semantically annotated using reference data as ground truth. In our investigation, nondamaged and damaged categories were retrieved with their specific taxonomies defined using our previous hierarchical annotation scheme. The classifier supports rapid mapping scenarios (e.g., floods in Germany and tsunami in Japan) and interactive mapping generation. The quantitative damages can be assessed by: 1) flooded agricultural areas (21.66% in the case of floods in Germany and 4.15% in the case of tsunami in Japan) and destroyed aquaculture (2.33% in the case of tsunami in Japan); 2) destroyed transportation infrastructures, such as airport (50% in case tsunami in Japan), bridges, and roads.; and 3) debris that appears in postdisaster images (3.81% in the case of tsunami after the aquaculture was destroyed). The first analysis envisages the floods of Elbe river in June 2013: 30% of the investigated area, about , including agricultural land, forest, river, and some residential and industrial areas close to the river, was covered by water. The second analysis, considering an area of affected by the tsunami, led us to conclude that 3 months after the tsunami, some of the categories returned to previous functionality-the airport, others return to partial functionality such as isolated residents, and some were totally destroyed-the aquaculture. The flooded area was about . The proposed approach goes beyond a simple annotation of the data and provides an intermediate product in order to produce a relevant visual analytics representation of the data. This makes it easier to compare datasets and different quantitative findings in a meaningful manner, accessible both to experts and regular users. Our paper presents an interactive and automatic, fast processing method applicable to large and complex datasets (such as image time series). In addition to enhancing the information content extraction (number of identified categories), this approach enables the discovery and analysis of these categories. The novelty of this paper resides in that this is the first time data analytics have been run on a large dataset and for different scenarios using a semiautomated processing chain.

Dimensionality Reduction for Visual Data Mining of Earth Observation Archives

Modern knowledge discovery systems, empowered by visual data exploration techniques, enable the user to discover and understand the data content. Considering patch-level processing, the visual exploration of Earth Observation archives aims to identify groups of items sharing similar semantic content. Each patch is further represented by certain descriptors, i.e., spectral signatures or Weber local descriptors, to capture structural signature. Later on, the content of the archive is illustrated by a 3-D projection of the high-dimensional space of the descriptors. Aspiring to prove the visual data mining potential, this letter intends to determine the capability of dimensionality reduction techniques to achieve a meaningful 3-D projection of the high-dimensional space. Several real-world data sets were used, i.e., University of California, Merced Land Use data set and a Landsat 7 Enhanced Thematic Mapper Plus image tiled into patches.

Semantic Map Generation from Satellite Images for Humanitarian Scenarios Applications

This paper demonstrates how knowledge driven methods and the associated data analysis algorithms are changing the paradigms of user-data interactions, providing an easier and wider access to the Earth Observation data. Some information theory based algorithms are proposed for anomaly and change detection on SPOT images, relative to a widespread humanitarian crisis scenario: floods. The outcomes of these algorithms define an informational representation of the image, revealing the spatial distribution of a particular theme. Using image analysis and interpretation, the multitude of features from a scene are classified into meaningful classes to create sematic maps.

Data mining and spatial reasoning for satellite image characterization

High level image understanding and content extraction requires image regions analysis to reveal the spatial interaction between them. This paper aims to engender new attributes for scene description considering the relative position of the objects inside. A visual grammar of the scene is built using an extension for a Knowledge Based Image Information Mining system (KIM). The objects are extracted using statistical models and machine learning through the KIM system, according to the user interest. Further, an affine invariant descriptor of the relative position between two objects is computed. This is the force histogram and it is considered to be a spatial signature which characterizes configurations of regions based on the attraction forces between the composing objects. Thereby, new patterns could be defined using similar object configurations, in order to enhance the effectiveness of the content-based image retrieval inside large databases.

Mutual information based measure for image content characterization

An image can be decomposed into different elementary descriptors depending on the observer interest. Similar techniques as used to understand words, regarded as molecules, formed by combining atoms, are proposed to describe images based on their information content. In this paper, we use primitive feature extraction and clustering to code the image information content. Our purpose is to describe the complexity of the information based on the combinational profile of the clustered primitive features using entropic measures like mutual information and Kullback-Leibler divergence. The developed method is demonstrated to asses image complexity for further applications to improve Earth Observation image analysis for sustainable humanitarian crisis response in risk reduction.

Feature space dimensionality reduction for the optimization of visualization methods

Visual data mining methods are of great importance in exploratory data analysis having a high potential for mining large databases. As the data feature space is generally n-dimensional, visual data mining relies on dimensionality reduction techniques. This is the case for image feature spaces which can be visualized by giving each data point a location in a three dimensional space. This paper aims to present a comparative study of several dimensionality reduction methods considering as input image feature spaces, in order to detemine an optimal visualization method to illustrate the separation of the classes. At the beginning, to check the performance of the envisaged method, an artificial dataset consisting of random vectors describing six, 20-dimensional Gaussian distributions with spaced means and low variances was generated. Further, two real images datasets are used to evaluate the contributions of dimensionality reduction algorithms related to data visualization. The analysis focuses on the PCA, LDA and t-SNE dimensionality reduction techniques. Our tests are performed on images for which the computed features include the color histogram and Weber descriptors.

A rapid mapping approach to quantify damages caused by the 2003 bam earthquake using high resolution multitemporal optical images

This paper aims to reveal a methodology used to quantitatively evaluate the impact of an earthquake on a region, considering multi temporal high resolution optical images. The proposed approach was initiated in the frame of GEODIM Project whose goal is to develop a Romanian downstream emergency response service in order to contribute to current disaster and risk management approach based on Earth observation data. The project is focused on developing experimental processing algorithms and mapping products for natural disasters (floods, earthquakes, landslides) damage assessment in urban areas based on very high resolution optical and SAR satellite imagery acquired worldwide. The prospective scenario considers knowledge discovery from pre and post event satellite images by mapping the extracted data features into semantic classes and symbolic representations like “buildings”, “vegetation”, “streets”, “bare land” and “damaged buildings”, etc.

Relevance of Earth Observation Images Information Mining to Humanitarian Crisis Management

Nowadays the earth observation sensors provide images containing detailed information relevant for applications related to hazard or security matters. Unfortunately, image information mining, its interpretation and transformation in products useful to the rescue or decision teams is still a laborious task, effectuated many times by visual inspection and manual annotations of the images, thus not appropriate to react in prerequisite time. This papers presents a data analysis processing chain, which by interactive operations, enables analysis and interpretation of large volumes of images with high accuracy, flexibility and much faster than any existing methods.

A dimensionality reduction approach for the visualization of the cluster space: A trustworthiness evaluation

The data mining systems solve the problem of handling Earth Observation archives counting on a feature vectors based description of the data. Increasing the dimensionality of the feature vectors would offer an effective perspective of the datasets content. The modern systems provide visual exploration of data projecting their high-dimensional feature space in a 3-D space. The dimensionality reduction methods represent the main way to achieve such representation. Several dimensionality reduction methods have been proposed to identify the mapping, bot not all of them retain the same dataset properties. In order to compare their performance, the development of formal measures like “Trustworthiness” or the measures based on Co-ranking matrix was mandatory. These measures objectively evaluate the similarity between the structure detected in the original and the reduced space. In this paper we evaluate six dimensionality reduction methods using “Trustworthiness” and “Continuity” measures. In this regard three datasets have been used: an artificial one and two remote sensing datasets. Each of them have been described by a high-dimensional feature space.

Class evolution data analytics from sar image time series using information theory measures

In this paper we present the result of data analytics techniques applied to a database comprising of 32 SLC SM TerraSAR-X images, acquired over the area of Bucharest, Romania. The methodology follows a two step approach. The first stage consists of a coarse identification of potentially changed areas using a supervised learning image annotation tool with relevance feedback. Gabor texture features are used to describe image patches. The patch size is derived as a function of the resolution and pixel spacing of the data. In the second stage we apply an information theory strategy to refine the regions previously shown to exhibit class dynamics within the image stack, with pixel accuracy. Finally, a series of analytical indicators (absolute extent of areas affected by change, class evolution trends, inter-class correlations) are derived, in order to generate a predictive model for the selected test site.