Prepoznavanje građevina pogođenih potresom temeljem korelacijske detekcije promjena obilježja teksture na SAR snimkama

The detection of building damage due to earthquakes is crucial for disaster management and disaster relief activities. Change detection methodologies using satellite images, such as synthetic aperture radar (SAR) data, have being applied in earthquake damage detection. Information contained within SAR data relating to earthquake damage of buildings can be disturbed easily by other factors. This paper presents a multitemporal change detection approach intended to identify and evaluate information pertaining to earthquake damage by fully exploiting the abundant texture features of SAR imagery. The approach is based on two images, which are constructed through principal components of multiple texture features. An independent principal components analysis technique is used to extract multiple texture feature components. Then, correlation analysis is performed to detect the distribution information of earthquake-damaged buildings. The performance of the technique was evaluated in the town of Jiegu (affected by the 2010 Yushu earthquake) and in the Kathmandu Valley (struck by the 2015 Nepal earthquake) for which the overall accuracy of building detection was 87.8% and 84.6%, respectively. Cross-validation results showed the proposed approach is more sensitive than existing methods to the detection of damaged buildings. Overall, the method is an effective damage detection approach that could support post-earthquake management activities in future events.Detekcija oštećenja građevina uzrokovanih potresom od presudne je važnosti za upravljanje rizicima od katastrofa i aktivnostima prilikom elementarnih nepogoda. Metodologije detekcije promjena, koristeći satelitske snimke kao što su podaci radara sa sintetičkim otvorom antene (SAR), korištene su u detekciji oštećenja od potresa. Informacije sadržane unutar SAR podataka, koje se odnose na oštećenja građevina uzrokovana potresom, mogu lako sadržavati šumove zbog drugih faktora. Ovaj rad prikazuje viševremenski pristup detekciji promjena kako bi se identificirale i procijenile informacije koje se odnose na oštećenja od potresa koristeći u potpunosti značajke teksture SAR snimaka. Pristup se temelji na dvije snimke koje su izrađene kroz glavne komponente višestrukih osobina tekstura. Neovisna analiza glavnih komponenti koristi se kako bi se izdvojile komponente višestrukih tekstura. Nakon toga provodi se korelacijska analiza kako bi se detektirale informacije o distribuciji građevina oštećenih potresom. Učinkovitost ove tehnike ispitana je u gradu Jiegu (kojega je 2010. godine pogodio potres Yushu) te u dolini Kathmandu (koju je 2015. godine pogodio potres Nepal), u kojoj je ukupna točnost detektiranja građevina bila 87,8%, odnosno 84,6%. Rezultati međusobne provjere valjanosti pokazali su da je predloženi pristup osjetljiviji od postojećih metoda za detektiranje oštećenih građevina. Općenito govoreći, metoda je učinkovit pristup detektiranja oštećenja koji može u budućnosti pružati potporu u aktivnostima upravljanja nakon potresa

Automatic Selection of Relevant Attributes for Multi-Sensor Remote Sensing Analysis: A Case Study on Sea Ice Classification

It is of considerable benefit to combine information obtained from different satellite sensors to achieve advanced and improved characterization of sea ice conditions. However, it is also true that not all the information is relevant. It may be redundant, corrupted, or unnecessary for the given task, hence decreasing the performance of the algorithms. Therefore, it is crucial to select an optimal set of image attributes which provides the relevant information content to enhance the efficiency and accuracy of the image interpretation and retrieval of geophysical parameters. Comprehensive studies have been focused on the analysis of relevant features for sea ice analysis obtained from different sensors, especially synthetic aperture radar. However, the outcomes of these studies are mostly data and application-dependent and can, therefore, rarely be generalized. In this article, we employ a feature selection method based on graph Laplacians, which is fully automatic and easy to implement. The proposed approach assesses relevant information on a global and local level using two metrics and selects relevant features for different regions of an image according to their physical characteristics and observation conditions. In the recent study, we investigate the effectiveness of this approach for sea ice classification, using different multi-sensor data combinations. Experiments show the advantage of applying multi-sensor data sets and demonstrate that the attributes selected by our method result in high classification accuracies. We demonstrate that our approach automatically considers varying technical, sensor-specific, environmental, and sea ice conditions by employing flexible and adaptive feature selection method as a pre-processing step

A Comprehensive Survey of Deep Learning in Remote Sensing: Theories, Tools and Challenges for the Community

In recent years, deep learning (DL), a re-branding of neural networks (NNs), has risen to the top in numerous areas, namely computer vision (CV), speech recognition, natural language processing, etc. Whereas remote sensing (RS) possesses a number of unique challenges, primarily related to sensors and applications, inevitably RS draws from many of the same theories as CV; e.g., statistics, fusion, and machine learning, to name a few. This means that the RS community should be aware of, if not at the leading edge of, of advancements like DL. Herein, we provide the most comprehensive survey of state-of-the-art RS DL research. We also review recent new developments in the DL field that can be used in DL for RS. Namely, we focus on theories, tools and challenges for the RS community. Specifically, we focus on unsolved challenges and opportunities as it relates to (i) inadequate data sets, (ii) human-understandable solutions for modelling physical phenomena, (iii) Big Data, (iv) non-traditional heterogeneous data sources, (v) DL architectures and learning algorithms for spectral, spatial and temporal data, (vi) transfer learning, (vii) an improved theoretical understanding of DL systems, (viii) high barriers to entry, and (ix) training and optimizing the DL.Comment: 64 pages, 411 references. To appear in Journal of Applied Remote Sensin

Graph-based Data Modeling and Analysis for Data Fusion in Remote Sensing

Hyperspectral imaging provides the capability of increased sensitivity and discrimination over traditional imaging methods by combining standard digital imaging with spectroscopic methods. For each individual pixel in a hyperspectral image (HSI), a continuous spectrum is sampled as the spectral reflectance/radiance signature to facilitate identification of ground cover and surface material. The abundant spectrum knowledge allows all available information from the data to be mined. The superior qualities within hyperspectral imaging allow wide applications such as mineral exploration, agriculture monitoring, and ecological surveillance, etc. The processing of massive high-dimensional HSI datasets is a challenge since many data processing techniques have a computational complexity that grows exponentially with the dimension. Besides, a HSI dataset may contain a limited number of degrees of freedom due to the high correlations between data points and among the spectra. On the other hand, merely taking advantage of the sampled spectrum of individual HSI data point may produce inaccurate results due to the mixed nature of raw HSI data, such as mixed pixels, optical interferences and etc. Fusion strategies are widely adopted in data processing to achieve better performance, especially in the field of classification and clustering. There are mainly three types of fusion strategies, namely low-level data fusion, intermediate-level feature fusion, and high-level decision fusion. Low-level data fusion combines multi-source data that is expected to be complementary or cooperative. Intermediate-level feature fusion aims at selection and combination of features to remove redundant information. Decision level fusion exploits a set of classifiers to provide more accurate results. The fusion strategies have wide applications including HSI data processing. With the fast development of multiple remote sensing modalities, e.g. Very High Resolution (VHR) optical sensors, LiDAR, etc., fusion of multi-source data can in principal produce more detailed information than each single source. On the other hand, besides the abundant spectral information contained in HSI data, features such as texture and shape may be employed to represent data points from a spatial perspective. Furthermore, feature fusion also includes the strategy of removing redundant and noisy features in the dataset. One of the major problems in machine learning and pattern recognition is to develop appropriate representations for complex nonlinear data. In HSI processing, a particular data point is usually described as a vector with coordinates corresponding to the intensities measured in the spectral bands. This vector representation permits the application of linear and nonlinear transformations with linear algebra to find an alternative representation of the data. More generally, HSI is multi-dimensional in nature and the vector representation may lose the contextual correlations. Tensor representation provides a more sophisticated modeling technique and a higher-order generalization to linear subspace analysis. In graph theory, data points can be generalized as nodes with connectivities measured from the proximity of a local neighborhood. The graph-based framework efficiently characterizes the relationships among the data and allows for convenient mathematical manipulation in many applications, such as data clustering, feature extraction, feature selection and data alignment. In this thesis, graph-based approaches applied in the field of multi-source feature and data fusion in remote sensing area are explored. We will mainly investigate the fusion of spatial, spectral and LiDAR information with linear and multilinear algebra under graph-based framework for data clustering and classification problems

Development of an unsupervised remote sensing methodology of detect surface leakage from terrestrial CO2 storage sites

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