Top PDF An object based approach for coastline extraction from Quickbird multispectral images

An object based approach for coastline extraction from Quickbird multispectral images

An object based approach for coastline extraction from Quickbird multispectral images

Abstract— Because of the reduced dimensions of pixels, in the last years high resolution satellite images (Quickbird, IKONOS, GeoEye, …..) are considered very important data to extract information for coastline monitoring and engineering opera planning. They can integrate detail topographic maps and aerial photos so to contribute to modifications recognition and coastal dynamics reconstruction. Many studies have been carried out on coastline detection from high resolution satellite images: unsupervised and supervised classification, segmentation, NDVI (Normalized Difference Vegetation Index) and NDWI (Normalized Difference Water Index) are only some of the methodological aspects that have been already considered and experimented. This paper is aimed to implement an object based approach to extract coastline from Quickbird multispectral imagery. Domitian area near Volturno River mouth in Campania Region (Italy), an interesting zone for its dynamics and evolution, is considered. Object based approach is developed for automatic detection of coastline from Quickbird imagery using the Feature Extraction Workflow implemented in ENVI Zoom software. The resulting vector polyline is performed using the smoothing algorithm named PAEK (Polynomial Approximation with Exponential Kernel).
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A TWO STAGE METHOD FOR BENGALI TEXT EXTRACTION FROM STILL IMAGES CONTAINING TEXT

A TWO STAGE METHOD FOR BENGALI TEXT EXTRACTION FROM STILL IMAGES CONTAINING TEXT

Bengali text extraction from multimedia images is a new research area and a large number of researchers have been working in this field. Some of the earlier works in this field were first done on scanned images which contained only textual portions where the background was in white and the text was in black [2]. The pre-processing phase becomes more challenging when the text has to be extracted from images containing non-connected components. Pre-processing of a picture including character string with multi-coloured background is followed by text recognition phase. Bhattacharya et al. [3] proposed a morphological approach of text area extraction obtaining connected components by applying horizontal line segmentation, then bridging them and calculating features like height, mean and standard deviation. Based on that, they performed image operations such as dilation, erosion and opening. The precision and recall values of their algorithm obtained on the basis of the present set of 100 images were respectively 68.8% and 71.2%. Captured images of head lighted text in Bengali suffer from proper character segmentation also. Another morphological approach was proposed by Ghoshal et al. [4] based on detecting unattached text area and segmenting connected components of Bengali/Devnagari characters from an image. Their online and automated system of Bengali character recognition was based on calculating features of connected components such as elongations ratio, number of holes present in the segmented character, aspect ratio and object to background pixels ratio. However the approach is restricted to capture pictures of high lighted text only. This algorithm of text extraction can be extended to scanned copy of the Bengali pictorial text also.
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Object-Based Building Extraction from High Resolution Satellite Imagery

Object-Based Building Extraction from High Resolution Satellite Imagery

This paper tends to extract building using an object based image analysis approach. This method allows us to use neighborhood, contextual and geometrical features in addition to spectral features. It has been attempted to present a general algorithm for building extraction from different satellite image. In the first step, the image pixels from the image are grouped to form objects with the aid of multiresolution segmentation algorithm. Then the features that lead to robust building extraction will be determined. Features are divided into two classes in this algorithm: stable and variable. Stable features are derived from inherent characteristics of building phenomenon and they provide us the possibility to be implemented on different satellite images. Variable features, depending on the case, are extracted using a feature analysis tool (SEaTH). Implementing this algorithm on a part of Isfahan QuickBird imagery was
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Moment feature based fast feature extraction algorithm for moving object detection using aerial images.

Moment feature based fast feature extraction algorithm for moving object detection using aerial images.

Fast and computationally less complex feature extraction for moving object detection using aerial images from unmanned aerial vehicles (UAVs) remains as an elusive goal in the field of computer vision research. The types of features used in current studies concerningmov- ing object detection are typically chosen based on improving detection rate rather than on providing fast and computationally less complex feature extraction methods. Because mov- ing object detection using aerial images from UAVs involves motion as seen from a certain altitude, effective and fast feature extraction is a vital issue for optimum detection perfor- mance. This research proposes a two-layer bucket approach based on a new feature ex- traction algorithm referred to as the moment-based feature extraction algorithm (MFEA). Because a moment represents thecoherent intensity of pixels and motion estimation is a motion pixel intensity measurement, this research used this relation to develop the pro- posed algorithm. The experimental results reveal the successful performance of the pro- posed MFEA algorithm and the proposed methodology.
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Introducing mapping standards in the quality assessment of buildings extracted from very high resolution satellite imagery

Introducing mapping standards in the quality assessment of buildings extracted from very high resolution satellite imagery

Extraction of building structures (polygons) from the imagery was performed using Feature Analyst 4.2 from Visual Learning Sys- tems (VLS), as an extension for ArcGIS (Esri). Feature Analyst (FA) is a GEOBIA application that conducts an internal ‘hidden’ segmenta- tion of the image that allows to classify and extract only those fea- tures belonging to the class of interest. FA uses an inductive machine learning approach for object recognition, exploring both spectral and spatial (contextual) information, and uses several dif- ferent algorithms depending on the data (Opitz and Blundell, 2008). The classification mode that was used is based on a super- vised approach, so the initial step is the identification of training samples for each class, followed by the definition of parameters such as band data type (e.g., reflectance, elevation), and number of bands to use in the classification, the type of input representa- tion pattern and size, and level of aggregation (i.e., minimum ob- ject size). The input representation pattern consists in a local window of pixels having a specific spatial configuration that en- ables the algorithm to examine the pixels adjacent and/or in the vicinity of the pixel of interest (the central pixel of the pattern), in order to determine if this pixel belongs to the feature class being Fig. 1. Study area in the city of Lisbon and pansharpened QuickBird image.
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Phenotype classification of zebrafish embryos by supervised learning.

Phenotype classification of zebrafish embryos by supervised learning.

Control or treated embryos were placed in a melt of E3 and methylcellulose, in a glass plate with 12 cavities (Hecht, Sondheim, FRG), one fish per well. Images were captured using an Olympus SZX10 stereo dissecting microscope coupled with an Olympus XC50 camera with a transmitted light illumination, the light passing up from the condenser and through the em- bryo. The Olympus XC50 camera allows us to acquire 2575 × 1932 pixel resolution images with a size of 14,2 Mo in TIFF format. We used the same parameters for all acquisition sessions (exposure time = 17ms, contrast = 1.05, maximum luminosity, white balance, magnification = 1.60x). First test runs revealed the ability of the classification algorithm to classify the images according to the acquisition session, therefore we defined a rigorous protocol in order to avoid variations in acquisition adjustments and parameters inducing artifacts that could bias the image analysis algorithms [9, 10]. Besides the primary aspects, such as the control of luminosity and focus, we also paid a particular attention to the position of the fish and to the nature of the plates (e.g.: glass) in order to avoid light refraction problems, causing shadowed parts on the images that can disturb the analysis. Finally, we decided to include images from five indepen- dent acquisition sessions into the learning set (see also below).
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Chlorophyll a spatial inference using artificial neural network from multispectral images and in situ measurements

Chlorophyll a spatial inference using artificial neural network from multispectral images and in situ measurements

After atmospheric correction of the scene was performed, the target of interest was isolated. This made it possible to analyze separately its spectral attributes and later modeling the occurrence of chlorophyll a in the study area. Water body isolation was performed using the result of an image segmentation of spectral band 7 (NIR-1 is the region that the spectral response of the water is almost null, which permit to differentiate it from other targets), whose mask was used to extract the surface refl ectance values just inside the water body in each Worldview-2 spectral band.
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A System to Detect Residential Area in Multispectral Satellite Images

A System to Detect Residential Area in Multispectral Satellite Images

necessary to take into account the notion of spatial relation. In order to take into account this notion, two kinds of method can be found in the literature: The first one is based on co-existing individual objects approach [1]. It is based on the existence of objects or a collection of objects and relations between these objects. The second one uses graphs to represent segmented images [2], RCC8 system is used in order to build a graph based description of the relationships between the regions of an image. This approach gives the possibility to represent in a very convenient way, the spatial relations between different regions or objects in an image. Moreover, the graph theory provides some very interesting algorithms such as graph matching algorithms [3,4,5]. These algorithms were used successfully to compute the similarity degree between two shapes or two patterns [6,7]. We propose in this paper to use the second approach to detect automatically complex patterns in high-resolution images. The spatial characteristics of the data are represented by region adjacency graphs. We also propose a way to represent the organization of the pattern that we look for in the image. This model based on the principle of automaton language generator is provided to the system with the set of images that we want to analyze. Then the system returns the images containing the detection of the desired patterns. This paper is organized as follow: In section 2, we describe in detail the architecture of our system. Trees and forest detection is presented in section 3.In section 4, we present methods used for houses and streets detection. In section 5, we present the used method to enhance the segmentation result. Residential area detection with graph adjacency is presented in section 6. Finally, in Section 7, we draw conclusion from this work and discuss future works.
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A simple semi-automatic approach for land cover classification from multispectral remote sensing imagery.

A simple semi-automatic approach for land cover classification from multispectral remote sensing imagery.

The results in Fig. 4 show that the spectral DN values of the five land cover types mostly cluster in three-dimensional ellipsoidal spaces. As the sampling regions were retrieved automatically, there are some outliers for each land cover type, which will affect the accuracy of the final classification. In the study area, most of the forest samples were outside of the ellipsoid (Fig. 4.2), accounting for up to 27.6% of the total samples. This may be because the change in forest cover did not follow the ecological rule, due to the high rate of urbanization in Shanghai, causing some large forest patches also to be less ecologically stable. Other land cover types present similar outliers but with fewer samples outside the ellipsoid, which may reduce the accuracy of the classification of forest land. Farmland is the dominant land cover type in the Qingpu District with sufficient samples for our classification method. As shown in Fig. 4.1, there are nearly 14.3% of samples are outside the automatically retrieved three-dimensional ellipsoid space. The area of grassland in the Qingpu District is quite small and spread among the forest and farmland; therefore, there are few grassland samples extracted from the images (Fig. 4.3), which also affects the final classification result. Water, such as rivers and lakes, is the third largest land cover type in the study area. Due to its low reflectivity, water areas are the easiest type to identify from remote sensing images by various other methods. As shown in Fig. 4.4, the DN values of water samples are quite low, and 91.7% pixels of samples in the regions of interest are inside the ellipsoid, ensuring the precision of its classification result. The second major land cover type of residential and construction area (urban land) of study area are of great three-dimensional spatial clustering (Fig. 4.5). Only 4.6% of sample pixels in the region of interest are outliers. In the Qingpu District, urban land is one of the most rapidly expanding land cover types, due to the rapid urbanization of Shanghai City. So within a period of time, the center of a large urban patch is unlikely to be transformed to a different land cover type, and the automatically retrieved samples in this area are well- represented spectrally.
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Design of a Conceptual Reference Framework for Reusable Software Components based on Context Level

Design of a Conceptual Reference Framework for Reusable Software Components based on Context Level

In this paper, we described a framework for reusing the software components in context level. This framework also provides diversity in the execution environment leading to a higher level of reliability of the system. By this new approach we can includes the reuse benefits as reduction in development effort and maintenance effort. This approach is feasible for building reliable software systems using the reusable components. Finally, the complete cycle of phases can enable the reuser to determine which components have high reuse potential with regards to specific functional requirements, minimum extraction time and measures of reusability metric. This research also shows how metrics can be used to find the quality attributes of a software component. The long term goal of this approach is to provide a distributed software component repository to support the system development though internet and cloud services.
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AN EFFICIENT APPROACH TO IMPROVE ARABIC DOCUMENTS CLUSTERING BASED ON A NEW KEYPHRASES EXTRACTION ALGORITHM

AN EFFICIENT APPROACH TO IMPROVE ARABIC DOCUMENTS CLUSTERING BASED ON A NEW KEYPHRASES EXTRACTION ALGORITHM

In this paper, we propose a new method to solve the problem cited above especially for Arabic language documents, which is one of the most complex languages, by using a new Keyphrases extraction algorithm based on the Suffix Tree data structure (KpST). To evaluate our approach, we conduct an experimental study on Arabic Documents Clustering using the most popular approach of Hierarchical algorithms: Agglomerative Hierarchical algorithm with seven linkage techniques and a variety of distance functions and similarity measures to perform Arabic Document Clustering task. The obtained results show that our approach for extracting Keyphrases improves the clustering results.
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An approach for Minutia Extraction in Latent Fingerprint Matching

An approach for Minutia Extraction in Latent Fingerprint Matching

Abstract: Biometrics is the most widely used area which helps in identifying a person. It is true that each person has unique fingerprint. A fingerprint has different type of minutiae and it is one of the most popular categories used in fingerprint verification. The science of fingerprinting has been used for many years as a tool for identification. It is the process used to determine whether two sets of fingerprint detail come from the same finger. Automatic and reliable extraction of minutiae from fingerprint images is a critical step in fingerprint matching. The quality of input fingerprint images plays an important role in the performance of automatic identification and verification algorithms. This paper presents a robust alignment algorithm to align fingerprints and measures similarity between fingerprints by taking both minutiae extraction and oriented field information.
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Image Deblurring Using Sparse-Land Model

Image Deblurring Using Sparse-Land Model

Abstract- In this paper, the problem of deblurring and poor image resolution is addressed and extended the task by removing blur and noise in the image to form a restored image as the output. Haar Wavelet transform based Wiener filtering is used in this algorithm. Soft Thresholding and Parallel Coordinate Descent (PCD) iterative shrinkage algorithm are used for removal of noise and deblurring. Sequential Subspace Optimization (SESOP) method or Line search method provides speed-up to this process. Sparse-land model is an emerging and powerful method to describe signals based on the sparsity and redundancy of their representations. Sparse coding is a key principle that underlies wavelet representation of images. Coefficient obtained after removal of noise and blur are not truly Sparse and so Minimum Mean Squared Error estimator (MMSE) estimates the Sparse vectors. Sparse representation of signals have drawn considerable interest in recent years. Sparse coding is a key principle that underlies wavelet representation of images. In this paper, we explain the effort of seeking a common wavelet sparse coding of images from same object category leads to an active basis model called Sparse-land model, where the images share the same set of selected wavelet elements, which forms a linear basis for restoring the blurred image.
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Automatic extraction of road seeds from high-resolution aerial images

Automatic extraction of road seeds from high-resolution aerial images

6. Fragmentation rule: as roads are usually smooth curves, polygons composed by short straight lines are not usually related to roads. For examples, image noise can generate short and isolate polygons. However, parts of poly- gons with very short straight-line segments can be extracted from road crossing where the cur- vature is much more accentuated. Another case is related to very perturbed road edges (by shadow or obstruction, for example), which may give rise to many short straight-line seg- ments connected to form a polygon. In these places, road objects are difficult to be formed, as the two first rules are hardly satisfied at all. Thus, cases involving short straight-line seg- ments are not considered and possible extrac- tion fails (for examples, road crossings not ex- tracted) are left to be handled by other strate- gies, which are based on previously extracted road seeds and other road knowledge, as e.g. context – relation between roads and other ob- jects like trees and building.
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An object-oriented approach for agrivultural land classification using rapideye imagery

An object-oriented approach for agrivultural land classification using rapideye imagery

The accuracy of land cover classification is determined by the characteristics of different land covers and the classification algorithm. The characteristics of land covers in remote sensing imagery mainly include spectral features and related vegetation/water/soil indexes from spectral bands. With the widely applications of high resolution remote sensing imagery in terrestrial monitoring, the structural and texture features are utilized in land cover classification, especially in object-oriented classification algorithms. However, excessive features not only slow down the learning process, but cause the classifier to over-fit the training data, as irrelevant or redundant features may confuse the learning algorithm. In this study, the input features are preliminarily selected according to the contribution of each input feature in constructing AdaTree. WL decision tree. The contribution of each input feature is calculated from the number it occurs and its importance in the The International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences, Volume XL-7/W4, 2015
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Braz. J. Chem. Eng.  vol.23 número2

Braz. J. Chem. Eng. vol.23 número2

The behaviour of the two solvent systems (carbon dioxide, pure or with ethanol) in extracting the pigments (namely bixin) from annatto seeds is different in the recoveries obtained and in the mass transfer mechanisms. But a similar plug flow model (Sovovà, 1994b) can be applied to both situations. Important developments of models to represent supercritical fluid extraction have recently been published. These models deal with the extraction of

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Morphological operation based dense houses extraction from DSM

Morphological operation based dense houses extraction from DSM

Mathematical morphology (MM) has been demonstrated to be an efficient tool for DSM data. Recently published papers have recognized the effectiveness of mathematical morphology when extracting objects of given shapes (Valero et al., 2010; Sagar et al., 2000; Soille et al., 2007; Sathymoorthy et al., 2007). More studies have investigated building extraction over the last two decades (Rottensteiner et al., 2002; Brunn et al., 1997; Zhan etal., 2002; Sportouche et al., 2011). Zhan et al. (2002) proposed a volumetric approach, where the DSM is considered in multiple layers, i.e., slices. The connected component in each slice is labeled. Such a component is classified as belonging to a building if the components in the vertically neighbored slices have similar sizes and centers of gravity. Rottensteiner et al. (2002) extracted initial building masks using a binary segmentation based on a height threshold. The masks still contained vegetation and other objects, so they used the binary morphological opening to separate and filter out small thin objects. They then analyzed the DSM textures to eliminate vegetation and other areas. The final individual building regions were determined using a connected component analysis. A decreasing-sized structural element for morphological opening helped to split the regions that corresponded to more than one roof plane. Brunn et al. (1997) used a binary classification to distinguish vegetation from buildings, by applying morphology to derive closed areas on the surface normal variance and select valid vegetation segments. Li et al (2014) used a modified white top-hat transform with directional edge constraints to filter airborne LiDAR data and extract the ground points. They proposed a novel algorithm that used the modified white top-hat (MWTH) transform with directional edge constraints to automatically extract the ground points from airborne LiDAR data. Other studies used MM on a multi-spectral image instead of DSM, and produced good results. Lefevre (2007) used very The International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences, Volume XL-3, 2014
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Fatigue Feature Extraction Analysis based on a K-Means Clustering Approach

Fatigue Feature Extraction Analysis based on a K-Means Clustering Approach

A time series contains a set of explanations of variables taken at equally spaced time intervals. Statistical analysis is commonly used to determine random signals and monitor the pattern of an analyzed signal. In fatigue signals, the calculation of the root- mean-square (r.m.s.) and kurtosis are very important; in order to retain a certain amount of the signal’s amplitude range characteristics. Both values are defined respectively as Nuawi et al. [9].

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Inundation mapping – a study based on December 2004 Tsunami Hazard along Chennai coast, Southeast India

Inundation mapping – a study based on December 2004 Tsunami Hazard along Chennai coast, Southeast India

A tsunami inundation map representing a source- and community-specific “credible worst case scenario” is a pow- erful planning and hazard mitigation tool (Gonzalez et al., 2002). They also reveal that the Mapping Program and increasing the usefulness of inundation mapping products to emergency managers. Tsunami inundation modeling is needed to assess the threat represented by such an event (Groat, 2005). As the population of coastal areas increases the need for, and value of, scientific understanding of earth- quake and tsunami hazards also increases. Simple maps of expected tsunami travel paths and times based on crude bathymetry have been of important operational value in de- veloping a tsunami-warning infrastructure for some time (Mofjeld et al, 2004). Global deep seafloor topography in sufficient detail allows the mapping of a Tsunami Scattering Index (Smith et al., 1997; Mofjeld et al., 2004). The Inun- dation limit in meters with latitude and Longitude are clearly shown Table 1 and also the inundation limit is compared with High Tide Line data which is provided by the Government of Tamil Nadu in the table. The inundation limits which have been transferred over the study area base map prepared us- ing Survey of India (SOI) toposheets. Figure 2 clearly shows that the inundation limits as well as inundation area has been blocked to identify the inundation along the study area from Cooum River to Adyar River.
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Depth extraction in 3D holoscopic images

Depth extraction in 3D holoscopic images

The light field camera is the device that allows to capture holoscopic images and is very similar to a traditional digital camera. However, unlike a traditional camera that only records the light rays entering in each pixel of the scene, the light field camera stores the direction of light rays in the sensor through its microlenses allowing it to collect all the information from the scene. These microlenses are tiny cameras that view the scene from all over the main lens, in other words, they can take a picture of the back of the main lens from different perspectives [7]. The light field cameras store the micro-images taken by microlens encoded in 2D format. Each micro-image has a reduced size due to the microlenses low resolution. The set of images captured by the array of microlenses need to be decoded and processed. One of the processing methods possible to apply to light field images is the depth map estimation. The depth map information may be useful for feature extraction, coding, interactive content manipulation and other applications in areas related to computer vision. For the collection of depth information, the light field images can be an alternative to laser scanning, stereo vision or structured-light techniques. The depth extraction based on holoscopic images can circumvent the high price of the laser, and it is also a passive system that does not need any interaction with the environment to construct the 3D model, unlike IR structured light pattern projection or laser scanning. The light field images do not require external calibration processes and are more likely to avoid occlusions due to camera’s microlenses when compared with stereo vision systems. However, using light field images to estimate the depth shows some disadvantages like the price of the camera (although cheaper than a good quality laser) and the processing time [8].
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