Anca Popescu

Contextual Descriptors for Scene Classes in Very High Resolution SAR Images

The new generation of spaceborne SAR instruments with meter or submeter resolution finds enormous applications for the observation of urban, industrial, in general of man-made scenes. Thus, targets are not any more observed in isolation, instead the groups of objects, e.g., house, bridge, and road, etc., need to be recognized in their spatial context. This paper proposes a feature extraction method for image patches in order to capture the spatial context. The method is based on the characteristics of the spectra of the SAR data, integrating radiometric, geometric, and texture properties of the SAR image patch. The method is demonstrated for TerraSAR-X High Resolution Spotlight data. To account for the spatial context in which a group of targets is located, it uses an image patch covering typically 200 × 200m2 of the scene. A comparative evaluation of our descriptors and grey-level co-occurrence matrix (GLCM) texture features has been performed over a database of 6916 patches. The method allowed for the robust recognition of over 30 different scene classes, with precision between 50% and 93%. Numerical results show that our method is able to discriminate between scene classes better than GLCM texture parameters.

Complex SAR image characterization using space variant spectral analysis

This paper proposes a new parameter based method of SAR image feature extraction and complex image information retrieval. The methodpsilas groundwork is the Fast Fourier Transform, each of the proposed parameters being built on a Fourier Transform basis. We suggest that by the use of several image bands formed of distinct spectral signatures of the original complex image, one can obtain a valid spectral characterization of the SAR image that can be afterwards subject to a clustering algorithm. The classification algorithm proposed in this paper is unsupervised K- means. The main advantages of the algorithm are the simplicity and robustness of the implementation.

Wavelet analysis for audio signals with music classification applications

Audio data like speech and music can be analyzed and processed with Fourier methods, having as one constraint the constant product of time and frequency resolutions. This problem can be avoided applying the Wavelet transform, ensuring good resolutions on both time and frequency supports. We propose in this paper to determine features of music in a combined framework using multi-resolution (wavelet) analysis and spectral analysis in order to realize the classification of musical pieces in genre classes. The proposed approach also uses a number of features commonly employed for speech recognition, such as Mel-cepstral coefficients, zero crossing rate or the signal energy. Moreover, the rhythm audio content is considered, the corresponding feature parameters being extracted from beat-histograms.

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.

Persistent Scatterers Detection on Synthetic Aperture Radar Images Acquired by Sentinel-1 Satellite

Persistent Scatterers Interferometry (PS-InSAR) has become a popular method in remote sensing because of its capability to measure terrain deformations with very high accuracy. It relies on multiple Synthetic Aperture Radar (SAR) acquisitions, to monitor points with stable proprieties over time, called Persistent Scatterers (PS)[1]. These points are unaffected by temporal decorrelation, therefore by analyzing their interferometric phase variation we can estimate the scene’s deformation rates within a given time interval. In this work, we apply two incoherent detection algorithms to identify Persistent Scatterers candidates in the city of Focșani, Romania. The first method studies the variation of targets’ intensities along the SAR acquisitions and the second method analyzes the spectral proprieties of the scatterers. The algorithms were implemented on a dataset containing 11 complex images of the region covering Buzău, Brăila and Focșani cities. Images were acquired by Sentinel-1 satellite in a time span of 5 months, from October 2014 to February 2015. The processing chain follows the requirements imposed by the new C-band SAR images delivered by the Sentinel-1 satellite (launched in April 2014) imaging in Interferometric Wide (IW) mode. Considering the particularities of the TOPS (Terrain Observation with Progressive Scans in Azimuth) imaging mode[2], special requirements had to be considered for pre-processing steps. The PS detection algorithms were implemented in Gamma RS program, a software which contains various function packages dedicated to SAR images focalization, analysis and processing.

Generic object recognition in high resolution SAR images

This paper presents a non-parametric modeling scheme for high resolution SAR data, based on Short Time Fourier Transform which is able to integrate the radiometrical and morphological properties of the data, for object recognition, scene and target indexing, addressing the problem of large data base queries and information retrieval.. The method is assessed by using a Bayesian Support Vector Machine image search engine based on a hierarchical learning model. The method allowed for the recognition of over 30 different classes, both homogeneous and heterogeneous urban objects with high levels of details. Qualitative and quantitative measures for evaluation are presented and discussed.

Damage Assessment Based on SAR Image Analysis: Flood Scenario for Romanian Eastern Carpathian Region

When natural disasters occur, it is necessary for the authorities to make fast and effective decisions in order to prevent the occurrence of more damage, as well as to find solutions for the affected population that needs to be relocated. Satellite imagery can prove to be a useful instrument in decision support during emergency situations of such nature (floods), and especially SAR data, due to its all weather capabilities. This paper makes an assessment of the utility of satellite radar products (TerraSAR-X and Radarsat) in the frame of emergency situations management. A real case study is presented, where radar data were processed by human specialists on one hand, and automatically on the other hand, using an intelligent information extraction system.

A bistatic SAR simulator for ground-based fixed-receiver geometry

In this paper we present the early development of a SAR simulator for ground-based fixed-receiver bistatic geometry. Firstly, we describe the assumptions the simulator is based on, then a short presentation of the mathematical model that lays behind the simulator is given. Furthermore, we give some details about its implementation and finally, we present some results.

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.

Semantic annotation of high resolution TerraSAR-X images using Information Mining

This paper addresses the problem of High Resolution Synthetic Aperture Radar (SAR) image semantic annotation using a Knowledge Based Information Mining (KIM) System. The authors propose the assessment of the capabilities of KIM to perform an automatic urban classification on TerraSAR-X data. Four test sites have been used in the experiment to prove that the system is generic and data independent. The performance is evaluated by matching the results with GeoEye optical representations of the selected areas. For the evaluation a number of three classes are presented and discussed (water bodies, green urban areas and tall buildings).