OBJECT IDENTIFICATION AND FEATURE EXTRACTION TECHNIQUES OF A SATELLITE DATA: A REVIEW

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1 OBJECT IDENTIFICATION AND FEATURE EXTRACTION TECHNIQUES OF A SATELLITE DATA: A REVIEW Navjeet 1, Simarjeet Kaur 2 1 Department of Computer Engineering Sri Guru Granth Sahib World University Fatehgarh Sahib, Punjab, India, 2 Assistant Professor Sri Guru Granth Sahib World University Fatehgarh Sahib, Punjab, India, ABSTRACT: Satellite images are widely used in these days. With the help of high resolution satellite image we can extract the features of an image. There are different types of satellites which provide accurate and high resolution images to individuals and organizations. High resolution images gives detail information about buildings, vegetation, rivers etc. Applications of High resolution images are telecommunications, disaster management and transport network mapping. Object identification is to identify an object which belongs to and feature extraction to extract features from the image like size, shape, texture and spectral information of the image. With the help of feature extraction techniques we can easily find the relevant information from the data and the data can easily be classified. There are several existing techniques for object identification and feature extraction which are discussed in this paper. Keywords: Object Identification, Feature extraction, Spatial features, Gray level features, Edge based type, Patch based type [1]INTRODUCTION The aim of this paper is to represent an approach for extracting the knowledge from the image. Identification means to identify the objects which belong to an image because of noise and shadow objects cannot be identified. Object recognition or identification can be classified in two types based on the feature type they use. The two types are edge based and patch based feature type. Edge based feature type extracts the edge map of an image and identify the features of the object at the edges. It represents the object boundaries and data efficiently. Patch based feature type uses appearance as cues. The combination of these features is more prevalent in future use [1]. Feature extraction means to extract the features of an image of high resolution data. There are some specific characteristics to extract the objects before the classification. Feature extraction is of two types: spatial features describing the shape of the objects. Some features are used in pre-classification and other is used in final statistical classification. The 1

2 spatial properties of a region are area, compactness, angle deviation, Hu moments, Elongation, Rectangularity, height and width and grey level features describing properties related to the pixel values of the objects. The grey level properties are Region mean, Region standard deviation, Region gradient mean, Boundary gradient, local contrast, Smoothness contrast ratio [2]. [2] RELATED WORK Lindi J. Quackenbush[6] describes the overview of the types of the imagery used for linear feature extraction and also describe the methods used for feature extraction and quantitative and qualitative accuracy of these procedures. Feature extraction is important for different applications due to increasing automatically updating GIS databases. Line Eikvil et.al [2] this paper describes the problem of vehicle detection in highresolution satellite images. For vehicle detection he proposed an automatic approach consisting of segmentation steps followed by two stages of object classification. This approach has been tested on quickbird images and results that are obtained have been compared with manual counts. Abhinav gupta et.al [4] this paper gives an overview of existing algorithms for object recognition and detection and their performance under different conditions. The author finally propose a system with detection using Haar filters and recognition using Scale Invariant Transform Features (SIFT). Haar based feature detection performs the edge arrangement based detector and SIFT outweighs the appearance based approach. Shrinivas Khandare et.al [5] describes the automated and semi-automated extraction of street features like roads, buildings, canals etc, from high resolution satellite imagery. This paper gives an overview of various types of imagery used for feature extraction and also describes the methods used for street feature extraction and considers qualitative and quantitative accuracy of procedures. Patricia G. Foschi et.al [7] describes the feature extraction and selection is the preprocessing step in image mining. The approach is to mine images to extract patterns and derive knowledge from large collection of images mainly deals with identification and extraction of features for particular domain. There are various methods available for features, the aim is to identify the features and extract relevant information. The extracted features were evaluated for goodness and tested on images. Er. Tanvi Madaan et.al [3] this paper describe the problem of pre-segmentation for object detection and statistics in remote sensing image processing. It has an important role in reducing computational burden and increasing efficiency for further image processing and analysis. The author follow the paradigm of object detection by Active Contour Method and then it imposes structural constraints for the detection of the entire object and analyzed the performance of the proposed scheme comprehensively and using measured data that carried out and comparisons with the existing algorithms. The result of this show that the proposed scheme could improve the application ability in target detection. Dong ping Tian[8] describes the image feature extraction and image feature represent- ation, which play a crucial role in multimedia processing. This paper provides a 2

3 comprehensive survey on the latest development in image feature extraction and image feature representation. The author analyzes the effectiveness of the fusion of global and local features in image processing. Ian Dowman[8] describes that there are an increasing number of sensors capable of delivering high resolution image data in a variety of spectral bands and polarisations. New data source such as LIDAR are available. The availability of this multi sensor data offers great potential for new techniques and new products but also makes it necessary that automatic methods are used to register the data, to extract features and for change detection. [3] PREVIOUS TECHNIQUES FOR OBJECT IDENTIFICATION AND FEATURE EXTRACTION The techniques for object identification are: M Kass et.al[2012][3]active Contour method is due to imaging noise and partial volume effects, the similarity in texture between neighboring structures complicates the task of identifying distinct boundaries between the structures. Active contour method was introduced which developed the concept of shape contours.when evolving shape contours, the interaction consists of m forces of attraction, repulsion, and competition by taking it into the relationship between object contours and their shape estimates. Hough Transformation [2012][3] is a technique which can be used to isolate features of a particular shape of an image. Because it requires the desired features be specified in parametric form, Hough transform is most commonly used for the detection of regular curves such as lines, circles, etc. A generalized Hough transform can be employed in applications where a simple analytic description of a feature is not possible. Because of the computational complexity of the generalized Hough algorithm, we restrict the main focus of this to the Hough transform. Despite its domain restrictions, the Hough transform retains many of the applications, as manufactured part contains feature boundaries which can be described by regular curves. The advantage of the Hough transform technique is that it is tolerant of gaps in feature boundary descriptions and is relatively unaffected by image noise. Viola et.al [2001][4]Haar feature based detection this algorithm is based on the work done by Viola and Jones. Features: The detection procedure classifies images based on the value of simple features. The haar feature uses the feature like edge features, line features and center-surrounded features. The value of two rectangle feature is the difference between the sums of the pixels within two rectangular regions. The regions have same size and shape and are horizontally or vertically adjacent. A three-rectangle frame computes the sum within two outside rectangles subtracted from the sum in the center triangle. Learning: Given a feature set and a training set of positive and negative images, number of machine learning approaches could be used to learn classification function. As there are large numbers of rectangular features we tend to reduce the number of features by selecting features appropriate for classification. In order to select the features we select 3

4 the single rectangular feature that separates the positive and negative examples and for each of the feature, weak learner determines the optimal threshold classification function such that the minimum numbers of examples are misclassified. The Attentional Cascade: We use a cascade of classifiers which achieves increased detection and also saves computational time. We make smaller and more efficient boosted classifiers can be constructed which reject many negative subwindowswhile detecting all positive instances. Amit et.al[2001][4]eigen-objects based Recognition the eigenspace approach requires an off-line learning phase during which images of all objects from many different views are used to construct the eigenspace. In subsequent recognition runs the test images are projected into the learned eigenspace and the closest model point is determined. In this the preprocessing step is ensured that all images of all objects are of the same size and that they are normalized with regard to overall brightness changes due to variations in the ambient illumination or aperture setting of the imaging system. Techniques for feature extraction There are many techniques for feature extraction in an image. Many papers reviewed extraction features for specific class such as roads while others have linear features such as streams, railroads and buildings etc. Techniques for feature extraction are divided into three steps :edge detection or road findings, road tracking and road linking. Yucheng et.al [2014][5] Multi Resolution Techniques many classifiers based on the spectral analysis of individual pixels have their limitations, usually they produce salt and pepper noisy results. First, segmentation based on mean shift was employed to gain the initial segments from remote sensing images. Vectorization method to generate polygons from the segmented image and feature attributions such as spectral, shape, texture etc. were extracted by zonal analysis based on original raster and polygons. Yucheng et.al [2014][5] Mathematical Morphology method introduced new fuzzy mathematical morphology in which open-closing algorithm is adopted to smooth the image and then to compute its gradient operators based on basic morphology. The method segmented the gradient image by fuzzy mathematical morphology for to give the result. This method has removed the over-segmentation that occurred due to noise and exiguity catchment basin of image. The techniques of mathematical morphology have proven useful in automating feature extraction. The two basic operations of mathematical morphology were dilation and erosion. F.Masulli et.al [2014][5] Model Template Matching method this approach, a template describing the general characteristics of the feature of interest is defined. Templates were often fixed in terms of attributes such as size, shape, and intensity. Features were extracted by moving the template through the image and evaluating the match at each location using a similarity measure (e.g., correlation) to find optimal locations. The measure can include shape (e.g., rectilinearity, parallelism or radial symmetry), image constraints (e.g., forcing consideration of only homogenous regions) or external constraints. 4

5 F.Masulli et.al [2014][5] Dynamic Programming model is a means of optimization through a recursive search, for example to find a global optimum. This approach is applicable only if a function can be expressed in terms of relationships between neighboring pixels alone and involves a sequential decision making process. Dynamic programming was used to optimize road extraction in SPOT imagery. Chanussot et.al [2014][5] Hough Transformation method the application of the Hough transform as an automated detection of linear features has the potential of detecting lines and linear features on images, and allows automated mapping of lineaments for further interpretation of structural geology. An application of the Hough transform for the interpretation of digitally enhanced satellite imageries covering an area of central Norway. The image processing includes directional filtering and a maximum filter prior to segmentation of structural features. An algorithm based on the Hough transform combined with a moving window algorithm has been implemented and applied for the classification of lineaments according to directions and length. The results of the automatic analysis were compared with published interpretations of the structural geology, and stated that the relevant geological features can be detected by this method. Saha et.al [2014][5] Fuzzy Clustering method using XB (Xei-Bei) index with Euclidean distance measurement. Automatic fuzzy clustering using modified differential evolution for image classification. In this method the number of clusters partitions automatically the data set. According to XB index the points were allocated to clusters. It is applied on two numeric remote sensing data with their feature vectors. Than the identification of land cover features is performed. Also, I- index is used to find the cluster classes and points allocation. [4] CONCLUSION Feature extraction is slow and time consuming process. When the size of the image increases the system performance degrades. This paper describes the several methods used for feature extraction and object identification. There is a use of remotely sensed data for the mapping of the streets, creation of land use, urban planning etc. It can also be used for the extraction of various features pertaining to the development of the city for e.g., roads, railways, play grounds, rivers, water bodies, vegetation, agriculture land, wasteland, etc. The data extracted from the remotely sensed data does not show accuracy measurements that it has the accuracy of + 4m in planimetric and +5 m in altimetry. Most current research works are highlighted and discussed. Based on the study presented here, researchers can choose a framework suitable for their own specific object detection and feature extraction problems and further optimize the chosen framework for better accuracy. REFERENCES 5

6 [1] Dilip.k Prasad, Survey o The Problem of Object Detection In Real Images International Journal of Image Processing (IJIP), Volume 6 : Issue 6 : [2] Line Ekvil et.al, Classification-based vehicle detection in high resolution satellite images ISPRS Journal of Photogrammetry and Remote Sensing, Volume 64, Issue 1, January 2009, Pages [3] Er. Tanvi Madaan et.al, Object Detection in Remote Sensing Images: A Review International Journal of Scientific and Research Publications, Volume 2, Issue 6, June [4] Abhinav Gupta et.al, Computational Models for Object Detection and Recognition, Journal of Optical Society America A, pp , [5] Shrinivas Khandare et.al, Review Paper On Techniques Of Street Mapping Using Fuzzy Approach With Remote Sensing Images International Journal of Remote Sensing & Geoscience (IJRSG), Volume 3, Issue 1, Jan [6] Lindi J. Quackenbush, A Review of Techniques for Extracting Linear Features from Imagery Photogrammetric Engineering & Remote Sensing Vol. 70, No. 12, December 2004, pp [7] Patricia G. Foschi, Feature Extraction for Image Mining, Multimedia Information Systems, page Arizona State University, [8] Ian Dowman, Automatic feature extraction for urban landscape models,automated extraction of Man-Made objects from Aerial and Space Images. Balkema Publishers,