Browsing by Subject "Remote sensing"

Now showing 1 - 20 of 33

Open Access
Autofocused spotlight SAR image reconstruction of off-grid sparse scenes
(Institute of Electrical and Electronics Engineers Inc., 2017) Camlıca, S.; Gurbuz, A. C.; Arıkan, Orhan
Synthetic aperture radar (SAR) has significant role in remote sensing. Phase errors due to uncompensated platform motion, measurement model mismatch, and measurement noise can cause degradations in SAR image reconstruction. For efficient processing of the measurements, image plane is discretized and autofocusing algorithms on this discrete grid are employed. However, in addition to the platform motion errors, the reflectors, which are not exactly on the reconstruction grid, also degrade the image quality. This is called the off-grid target problem. In this paper, a sparsity-based technique is developed for autofocused spotlight SAR image reconstruction that can correct phase errors due to uncompensated platform motion and provide robust images in the presence of off-grid targets. The proposed orthogonal matching pursuit-based reconstruction technique uses gradient descent parameter updates with built in autofocus. The technique can reconstruct high-quality images by using sub Nyquist rate of sampling on the reflected signals at the receiver. The results obtained using both simulated and real SAR system data show that the proposed technique provides higher quality reconstructions over alternative techniques in terms of commonly used performance metrics.
Open Access
Automatic detection of compound structures by joint selection of region groups from a hierarchical segmentation
(Institute of Electrical and Electronics Engineers, 2016) Akçay, H. G.; Aksoy, S.
A challenging problem in remote sensing image analysis is the detection of heterogeneous compound structures such as different types of residential, industrial, and agricultural areas that are composed of spatial arrangements of simple primitive objects such as buildings and trees. We describe a generic method for the modeling and detection of compound structures that involve arrangements of an unknown number of primitives in large scenes. The modeling process starts with a single example structure, considers the primitive objects as random variables, builds a contextual model of their arrangements using a Markov random field, and learns the parameters of this model via sampling from the corresponding maximum entropy distribution. The detection task is formulated as the selection of multiple subsets of candidate regions from a hierarchical segmentation where each set of selected regions constitutes an instance of the example compound structure. The combinatorial selection problem is solved by the joint sampling of groups of regions by maximizing the likelihood of their individual appearances and relative spatial arrangements. Experiments using very high spatial resolution images show that the proposed method can effectively localize an unknown number of instances of different compound structures that cannot be detected by using spectral and shape features alone.
Open Access
Automatic detection of geospatial objects using multiple hierarchical segmentations
(Institute of Electrical and Electronics Engineers, 2008-07) Akçay, H. G.; Aksoy, S.
The object-based analysis of remotely sensed imagery provides valuable spatial and structural information that is complementary to pixel-based spectral information in classification. In this paper, we present novel methods for automatic object detection in high-resolution images by combining spectral information with structural information exploited by using image segmentation. The proposed segmentation algorithm uses morphological operations applied to individual spectral bands using structuring elements in increasing sizes. These operations produce a set of connected components forming a hierarchy of segments for each band. A generic algorithm is designed to select meaningful segments that maximize a measure consisting of spectral homogeneity and neighborhood connectivity. Given the observation that different structures appear more clearly at different scales in different spectral bands, we describe a new algorithm for unsupervised grouping of candidate segments belonging to multiple hierarchical segmentations to find coherent sets of segments that correspond to actual objects. The segments are modeled by using their spectral and textural content, and the grouping problem is solved by using the probabilistic latent semantic analysis algorithm that builds object models by learning the object-conditional probability distributions. The automatic labeling of a segment is done by computing the similarity of its feature distribution to the distribution of the learned object models using the Kullback-Leibler divergence. The performances of the unsupervised segmentation and object detection algorithms are evaluated qualitatively and quantitatively using three different data sets with comparative experiments, and the results show that the proposed methods are able to automatically detect, group, and label segments belonging to the same object classes. © 2008 IEEE.
Open Access
Building detection using directional spatial constraints
(IEEE, 2010) Akçay, H. Gökhan; Aksoy, Selim
We propose an algorithm for automatic detection of buildings with complex shapes and roof structures in very high spatial resolution remotely sensed images. First, an initial oversegmentation is obtained. Then, candidate building regions are found using shadow and sun azimuth angle information. Finally, the building regions are selected by clustering the candidate regions using minimum spanning trees. The experiments on Ikonos scenes show that the algorithm is able to detect buildings with complex appearances and shapes. © 2010 IEEE.
Open Access
Detection of compound structures using hierarchical clustering of statistical and structural features
(IEEE, 2011) Akçay, H. Gokhan; Aksoy, Selim
We describe a new procedure that combines statistical and structural characteristics of simple primitive objects to discover compound structures in images. The statistical information that is modeled using spectral, shape, and position data of individual objects, and structural information that is modeled in terms of spatial alignments of neighboring object groups are encoded in a graph structure that contains the primitive objects at its vertices, and the edges connect the potentially related objects. Experiments using WorldView-2 data show that hierarchical clustering of these vertices can find high-level compound structures that cannot be obtained using traditional techniques. © 2011 IEEE.
Open Access
Detection of compound structures using multiple hierarchical segmentations
(IEEE, 2012) Akçay, H. Gökhan; Aksoy, Selim
In this paper, our aim is to discover compound structures comprised of regions obtained from hierarchical segmentations of multiple spectral bands. A region adjacency graph is constructed by representing regions as vertices and connecting these vertices that are spatially close by edges. Then, dissimilarities between neighboring vertices are computed using statistical and structural features, and are assigned as edge weights. Finally, the compound structures are detected by extracting the connected components of the graph whose edges with relatively large weights are removed. Experiments using WorldView-2 images show that grouping of these vertices according to different criteria can extract high-level compound structures that cannot be obtained using traditional techniques. © 2012 IEEE.
Open Access
Finding compound structures in images using image segmentation and graph-based knowledge discovery
(IEEE, 2009-07) Zamalieva, Daniya; Aksoy, Selim; Tilton J. C.
We present an unsupervised method for discovering compound image structures that are comprised of simpler primitive objects. An initial segmentation step produces image regions with homogeneous spectral content. Then, the segmentation is translated into a relational graph structure whose nodes correspond to the regions and the edges represent the relationships between these regions. We assume that the region objects that appear together frequently can be considered as strongly related. This relation is modeled using the transition frequencies between neighboring regions, and the significant relations are found as the modes of a probability distribution estimated using the features of these transitions. Experiments using an Ikonos image show that subgraphs found within the graph representing the whole image correspond to parts of different high-level compound structures. ©2009 IEEE.
Open Access
Fire detection and 3D fire propagation estimation for the protection of cultural heritage areas
(Copernicus GmbH, 2010) Dimitropoulos, K.; Köse, Kıvanç; Grammalidis, N.; Çetin, A. Enis
Beyond taking precautionary measures to avoid a forest fire, early warning and immediate response to a fire breakout are the only ways to avoid great losses and environmental and cultural heritage damages. To this end, this paper aims to present a computer vision based algorithm for wildfire detection and a 3D fire propagation estimation system. The main detection algorithm is composed of four sub-algorithms detecting (i) slow moving objects, (ii) smoke-coloured regions, (iii) rising regions, and (iv) shadow regions. After detecting a wildfire, the main focus should be the estimation of its propagation direction and speed. If the model of the vegetation and other important parameters like wind speed, slope, aspect of the ground surface, etc. are known; the propagation of fire can be estimated. This propagation can then be visualized in any 3D-GIS environment that supports KML files.
Open Access
Identification of materials with magnetic characteristics by neural networks
(2012) Nazlibilek, S.; Ege, Y.; Kalender O.; Sensoy, M.G.; Karacor, D.; Sazli, M.H.
In industry, there is a need for remote sensing and autonomous method for the identification of the ferromagnetic materials used. The system is desired to have the characteristics of improved accuracy and low power consumption. It must also autonomous and fast enough for the decision. In this work, the details of inaccurate and low power remote sensing mechanism and autonomous identification system are given. The remote sensing mechanism utilizes KMZ51 anisotropic magneto-resistive sensor with high sensitivity and low power consumption. The images and most appropriate mathematical curves and formulas for the magnetic anomalies created by the magnetic materials are obtained by 2-D motion of the sensor over the material. The contribution of the paper is the use of the images obtained by the measurement of the perpendicular component of the Earth magnetic field that is a new method for the purpose of identification of an unknown magnetic material. The identification system is based on two kinds of neural network structures. The MultiLayer Perceptron (MLP) and the Radial Basis Function (RBF) network types are used for training of the neural networks. In this work, 23 different materials such as SAE/AISI 1030, 1035, 1040, 1060, 4140 and 8260 are identified. Besides the ferromagnetic materials, three objects are also successfully identified. Two of them are anti-personal and anti-tank mines and one is an empty can box. It is shown that the identification system can also be used as a buried mine identification system. The neural networks are trained with images which are originally obtained by the remote sensing system and the system is operated by images with added Gaussian white noises. © 2012 Elsevier Ltd. All rights reserved.
Open Access
Image information mining using spatial relationship constraints
(2012) Karakuş, Fatih
There is a huge amount of data which is collected from the Earth observation satellites and they are continuously sending data to Earth receiving stations day by day. Therefore, mining of those data becomes more important for effective processing of collected multi-spectral images. The most popular approaches for this problem use the meta-data of the images such as geographical coordinates etc. However, these approaches do not offer a good solution for determining what those images contain. Some researches make a big step from the meta-data based approaches in this area by moving the focus of the study to content based approaches such as utilizing the region information of the sensed images. In this thesis, we propose a novel, generic and extendable image information mining system that uses spatial relationship constraints. In this system, we use not only the region content, but also relationships of those regions. First, we extract the region information of the images and then extract pairwise relationship information of those regions such as left, right, above, below, near, far and distance etc. This feature extraction process is defined as a generic process which is independent from how the region segmentation is obtained. In addition to these, since new features and new approaches are continuously being developed by the image information mining researchers, extendability feature of the our system plays a big role while we are designing our system. In this thesis, we also propose a novel feature vector structure in which a feature vector consists of several sub-feature vectors. In the proposed feature vector structure, each sub-feature vector can be exclusively selected to be used for search process and they can have different distance metrics to be used in comparisons between the same sub-feature vector of the other feature vector structures. Therefore, the system gives ability to users to choose which information about the region and its pairwise relationship with other regions to be used when they perform a search on the system. The proposed system is illustrated by using region based retrieval scenarios on very high spatial resolution satellite images.
Open Access
Interactive training of advanced classifiers for mining remote sensing image archives
(ACM, 2004) Aksoy, Selim; Koperski, K.; Tusk, C.; Marchisio G.
Advances in satellite technology and availability of down-loaded images constantly increase the sizes of remote sensing image archives. Automatic content extraction, classification and content-based retrieval have become highly desired goals for the development of intelligent remote sensing databases. The common approach for mining these databases uses rules created by analysts. However, incorporating GIS information and human expert knowledge with digital image processing improves remote sensing image analysis. We developed a system that uses decision tree classifiers for interactive learning of land cover models and mining of image archives. Decision trees provide a promising solution for this problem because they can operate on both numerical (continuous) and categorical (discrete) data sources, and they do not require any assumptions about neither the distributions nor the independence of attribute values. This is especially important for the fusion of measurements from different sources like spectral data, DEM data and other ancillary GIS data. Furthermore, using surrogate splits provides the capability of dealing with missing data during both training and classification, and enables handling instrument malfunctions or the cases where one or more measurements do not exist for some locations. Quantitative and qualitative performance evaluation showed that decision trees provide powerful tools for modeling both pixel and region contents of images and mining of remote sensing image archives.
Open Access
Ionogram scaling using Hidden Markov Models
(Institute of Electrical and Electronics Engineers, 2018) Gök, Gökhan; Alp, Y. K.; Arıkan, Orhan; Arikan, F.
In this paper, a novel method for electron density reconstruction using ionosonde data is proposed. Proposed technique uses Hidden Markov Models for extracting echoes that provides valuable information about electron density distribution in order to provide input to a model based optimization technique that reconstructs the electron density distribution by solving model parameters. Analysis on real ionosonde data shows that proposed technique outperforms standard techniques in the literature.
Open Access
Ionolab grubunun iyonküre uzaktan algılama ve 2-b görüntüleme çalışmaları
(IEEE, 2014-04) Arıkan, F.; Toker, C.; Sezen, U.; Deviren, M. N.; Çilibaş, O.; Arıkan, Orhan
Bu çalışmada, IONOLAB grubunun son 10 yıldır iyonküre uzaktan algılaması ve 2-B görüntüleme çalışmaları özetlenecektir. TÜBİTAK EEEAG 105E171 ve 109E055 projelerinde, çift frekanslı Yerküresel Konumlama Sistemi (YKS) alıcılarının sözde menzil ve faz gecikmesi kayıtlarından özgün Toplam Elektron İçeriği (TEİ) kestirim yöntemi IONOLAB-TEC geliştirilmiştir. Önemli bir Uzay Havası hizmeti olarak www.ionolab.org sitesinden tüm araştırmacılara açılan IONOLAB-TEC, dünyada ilk ve tek gürbüz, güvenilir ve hassas tek istasyon için TEİ kestirimleri yapabilmektedir. Uzayda ve zamanda seyrek YKS-TEİ kestirimlerinin bölgesel ve yerküresel aradeğerlemesi için çalışmalar yapılmış ve Türkiye üzerindeki TUSAGA-Aktif istasyon ağından IONOLAB-TEC yöntemi ile elde edilen Toplam Elektron İçeriği (TEİ) kestirimleri kullanılarak otomatik yüksek çözünürlüklü 2-B TEİ görüntüleri elde edilmiştir. IRI-Plas iyonküre iklimsel modeli altyapısıyla literatürde ilk kez hızlı ve gürbüz elektron yoğunluğu dağılımları elde edilmiş ve iyonküre model parametreleri özgün aradeğerleme ile birleştirilmiştir. www.ionolab.org sitesinde iyonküre kritik frekans ve yükseklik haritaları sunulmaktadır. IONOLAB grubunun bu önemli katkıları TÜBİTAK EEEAG 112E568 projesi kapsamında devam etmektedir.
Open Access
Kentsel yapıların biçimbilimsel bölütlenmesi
(IEEE, 2007-06) Akçay, H. Gökhan; Aksoy, Selim
Yüksek çözünürlükteki uzaktan algılamalı uydu görüntülerinde bölütleme kent uygulamalarında önemli bir problemdir çünkü elde edilen bölütlemelerle sınıflandırma için piksel tabanlı spektral bilginin yanında uzamsal ve yapısal bilgiler elde edilebilir. Bu bildiride, biçimbilimsel işlemlerle çıkarılan yapısal bilgi ve ana bileşenler analizi ile özetlenen spektral bilgi kullanılarak gürültüden etkilenmeyen bölütler elde eden bir yöntem sunduk. Yapılan deneyler yöntemin görüntü üzerinde komşuluk bilgisini ve spektral bilgiyi beraber kullanmayan başka bir yönteme göre daha düzgün ve anlamlı yapılar bulduğunu göstermiştir. Automatic segmentation of high-resolution remote sensing imagery is an important problem in urban applications because the resulting segmentations can provide valuable spatial and structural information that are complementary to pixel-based spectral information in classification. We present a method that combines structural information extracted by morphological processing with spectral information summarized using principal components analysis to produce precise segmentations that are also robust to noise. The experiments show that the method is able to detect structures in the image which are more precise and more meaningful than the structures detected by another approach that does not make strong use of neighborhood and spectral information.
Open Access
Land cover classification with multi-sensor fusion of partly missing data
(American Society for Photogrammetry and Remote Sensing, 2009-05) Aksoy, S.; Koperski, K.; Tusk, C.; Marchisio, G.
We describe a system that uses decision tree-based tools for seamless acquisition of knowledge for classification of remotely sensed imagery. We concentrate on three important problems in this process: information fusion, model understandability, and handling of missing data. Importance of multi-sensor information fusion and the use of decision tree classifiers for such problems have been well-studied in the literature. However, these studies have been limited to the cases where all data sources have a full coverage for the scene under consideration. Our contribution in this paper is to show how decision tree classifiers can be learned with alternative (surrogate) decision nodes and result in models that are capable of dealing with missing data during both training and classification to handle cases where one or more measurements do not exist for some locations. We present detailed performance evaluation regarding the effectiveness of these classifiers for information fusion and feature selection, and study three different methods for handling missing data in comparative experiments. The results show that surrogate decisions incorporated into decision tree classifiers provide powerful models for fusing information from different data layers while being robust to missing data. © 2009 American Society for Photogrammetry and Remote Sensing.
Open Access
Learning bayesian classifiers for scene classification with a visual grammar
(IEEE, 2005-03) Aksoy, Selim; Koperski, K.; Tusk, C.; Marchisio, G.; Tilton, J. C.
A challenging problem in image content extraction and classification is building a system that automatically learns high-level semantic interpretations of images. We describe a Bayesian framework for a visual grammar that aims to reduce the gap between low-level features and high-level user semantics. Our approach includes modeling image pixels using automatic fusion of their spectral, textural, and other ancillary attributes; segmentation of image regions using an iterative split-and-merge algorithm; and representing scenes by decomposing them into prototype regions and modeling the interactions between these regions in terms of their spatial relationships. Naive Bayes classifiers are used in the learning of models for region segmentation and classification using positive and negative examples for user-defined semantic land cover labels. The system also automatically learns representative region groups that can distinguish different scenes and builds visual grammar models. Experiments using Landsat scenes show that the visual grammar enables creation of high-level classes that cannot be modeled by individual pixels or regions. Furthermore, learning of the classifiers requires only a few training examples.
Open Access
Learning bayesian classifiers for scene classification with a visual grammar
(IEEE, 2005) Aksoy, Selim; Koperski, K.; Tusk, C.; Marchisio, G.; Tilton J. C.
A challenging problem in image content extraction and classification is building a system that automatically learns high-level semantic interpretations of images. We describe a Bayesian framework for a visual grammar that aims to reduce the gap between low-level features and high-level user semantics. Our approach includes modeling image pixels using automatic fusion of their spectral, textural, and other ancillary attributes; segmentation of image regions using an iterative split-and-merge algorithm; and representing scenes by decomposing them into prototype regions and modeling the interactions between these regions in terms of their spatial relationships. Naive Bayes classifiers are used in the learning of models for region segmentation and classification using positive and negative examples for user-defined semantic land cover labels. The system also automatically learns representative region groups that can distinguish different scenes and builds visual grammar models. Experiments using Landsat scenes show that the visual grammar enables creation of high-level classes that cannot be modeled by individual pixels or regions. Furthermore, learning of the classifiers requires only a few training examples. © 2005 IEEE.
Open Access
Metamaterial-based wireless RF-MEMS strain sensors
(IEEE, 2010) Melik, Rohat; Ünal, Emre; Perkgoz, Nihan Kosku; Puttlitz, C.; Demir, Hilmi Volkan
Approximately 10% of the fractures do not heal properly because of the inability to monitor fracture healing. Standard radiography is not capable of discriminating whether bone healing is occurring normally or aberrantly. We propose and develop an implantable wireless sensor that monitors strain on implanted hardware in real time telemetrically. This enables clinicians to monitor fracture healing. Here we present the development and demonstration of metamaterial-based radiofrequency (RF) micro-electro-mechanical system (MEMS) strain sensors for wireless strain sensing to monitor fracture healing. The operating frequency of these sensors shifts under mechanical loading; this shift is related to the surface strain of the implantable test material. In this work, we implemented metamaterials in two different architectures as bio-implantable wireless strain sensors for the first time. These custom-design metamaterials exhibit better performance as sensors than traditional RF structures (e.g., spiral coils) because of their unique structural properties (splits). They feature a low enough operating frequency to avoid the background absorption of soft tissue and yield higher Q-factors compared to the spiral structures (because their gaps have much higher electric field density). In our first metamaterial architecture of an 5x5 array, the wireless sensor shows high sensitivity (109kHz/kgf, 5.148kHz/microstrain) with low nonlinearity-error (<200microstrain). Using our second architecture, we then improved the structure of classical metamaterial and obtained nested metamaterials that incorporate multiple metamaterials in a compact nested structure and measured strain telemetrically at low operating frequencies. This novel nested metamaterial structure outperformed classical metamaterial structure as wireless strain sensors. By employing nested metamaterial architecture, the operating frequency is reduced from 529.8 MHz to 506.2 MHz while the sensitivity is increased from 0.72 kHz/kgf to 1.09 kHz/kgf. ©2010 IEEE.
Open Access
Mining of remote sensing image archives using spatial relationship histograms
(IEEE, 2008-07) Kalaycılar, Fırat; Kale, Aslı; Zamalieva, Daniya; Aksoy, Selim
We describe a new image representation using spatial relationship histograms that extend our earlier work on modeling image content using attributed relational graphs. These histograms are constructed by classifying the regions in an image, computing the topological and distance-based spatial relationships between these regions, and counting the number of times different groups of regions are observed in the image. We also describe a selection algorithm that produces very compact representations by identifying the distinguishing region groups that are frequently found in a particular class of scenes but rarely exist in others. Experiments using Ikonos scenes illustrate the effectiveness of the proposed representation in retrieval of images containing complex types of scenes such as dense and sparse urban areas. © 2008 IEEE.
Open Access
Modeling of remote sensing image content using attributed relational graphs
(Springer, 2006-08) Aksoy, Selim
Automatic content modeling and retrieval in remote sensing image databases are important and challenging problems. Statistical pattern recognition and computer vision algorithms concentrate on feature-based analysis and representations in pixel or region levels whereas syntactic and structural techniques focus on modeling symbolic representations for interpreting scenes. We describe a hybrid hierarchical approach for image content modeling and retrieval. First, scenes are decomposed into regions using pixel-based classifiers and an iterative split-and-merge algorithm. Next, spatial relationships of regions are computed using boundary, distance and orientation information based on different region representations. Finally, scenes are modeled using attributed relational graphs that combine region class information and spatial arrangements. We demonstrate the effectiveness of this approach in query scenarios that cannot be expressed by traditional approaches but where the proposed models can capture both feature and spatial characteristics of scenes and can retrieve similar areas according to their high-level semantic content. © Springer-Verlag Berlin Heidelberg 2006.