Bag of Visual Words Model

Bag of Visual Words ModelAbstractAutomatic interpretation of Remote sensing enters is a very important labor movement in several practical fields. There be several approaches to accomplish this task, one of the or so powerful and effective approach is the use of local anaesthetic anaesthetic features and machine eruditeness techniques to detect object lenss and break upify it. In such an approach, first, the mental image is scanned for local features and coded in a mathematically manipulatable form, then these local features are injected to a classifier to get the class of the object which contains these local features. In this thesis, bag of visual words model for detecting and recognizing of objects in high resolution satellite images is constructed and tested using blot local features. Scale Invariant Feature Transform (SIFT) and Speedup Robust Features (SURF) algorithms are use as blob local feature detector and descriptor. The take outed features are coded mathemat ically with Bag of Visual Words algorithm in order to represent an image by the histograms of visual words. Dimension reduction technique is used to eliminate non-relevant and non-distinctive data using Principle Component Analysis (PCA). Finally, a single class Support Vector Machine (SVM) classifier is used to classify the object image as a positive or negative match. We extend the typical use of BOVW by using an object proposals technique to pull up regions that ordain be classified by the SVM depends on keypoints location clustering instead of sliding window approach. Besides enhance the resolution independency by using geospatial info extracted from the remote sensing images meta-data to extract real dimensions of objects during training and spotting. The whole approach will be tested practically in the experiment land to prove that this approach is capable to detecting a number of geo-spatial objects, such as airplane, airports and cars.IntroductionThe remote sensing, imag es has been developed in quantity and quality and its applications. The image itself is not useful without analysis. The analysis is to generate information from the image. One of the image analysis tasks is the detection of objects from the images, either man-made objects or natural objects. The automation of this task is very useful in real world applications, but it is very challenging. This can be one of the computer vision field chores. The methods that, use local features in object, recognition from visual data is very favored in recent researches. The benefits of using local features is immunity, to occlusion, and clutter, and with greatest significantly, no pre-step of segmentation, is required before local feature extraction. The accessibility of diverse feature extraction and descriptors algorithms lets local feature methods efficient. Furthermore, the large number of features, generated from images of objects is crucial advantage, of local features. While the benefits o f local features are useful, a feature has to cover some factors like invariance to scaling, rotation, illumination, viewing direction slight change, noise and cluttering.MotivationThe revolutionary technology used in recent generation satellite systems is driving the development of new large scale data handling approaches in remote sensing related applications. Furthermore, the large image archives captured over the previous missions are now being used to produce innovative global products. In particular, the development of large-scale analytics tools to efficiently extract information and apply the achieved results towards answering scientific questions represents a big challenge for the research community working in the Remote Sensing field. One of the most useful analytic tools in remote sensing images is the object detection and recognition, either the man-made objects or natural ones as shown in Figure 11Figure 11 Object detection as a Remote sensing image interpretation anal ysisThere are a lot of challenges faced by the researchers like, but not limited to, enhancing the efficiency to process large data, developing the suitable techniques to detect and recognize diverse object types and develop tools and platforms needed to store, analyze, interpret and represent data and results. These challenges united experts of data science, algorithm development and computer science, as well as environmental experts and geoscientists, to present state of the art algorithms, tools, and applications for processing and exploitation of a huge amount of remotely sensed data. The scope of these researches can be generalized as followersStudies describing advanced approaches to process large volume of multi-temporal optical, SAR (Synthetic Aperture Radar) and radiometric data.Studies discussing innovative techniques, and associated data processing methods for very large-scale data exploitation.Critical analyses of existing and innovative tools, methods and techniques f or large-scale analytics to extract and represent informationResults of case studies executed at divers(prenominal) large spatial and temporal scales, also by using GRID and/or haze over Computing platforms.Results of on-going national/international initiatives and solutions for managing, processing, and disseminating huge archives of Remote Sensing data and relevant results.Problem StatementThis thesis addresses the problem of geospatial object detection and recognition from high resolution satellite images. The problem we are trying to solve is to decide if a given aerial, or satellite image, contains one or more objects, belonging to the class of interest, and locate the position, of each predicted object, in the image. The expression object stated in this thesis is any type of object whitethorn appear in the remote sensing images, including man-made objects which have sharp edges and are distinct from the background, for example a building, a ship, a vehicle. Our solution mus t be consider the challenges and difficulties of object detection in optical remote sensing images like visual appearance variations which caused by occlusion, viewpoint variation, clutter, illumination variation, fag end variation, etc.A general statement of the problem can be formulated as followsGiven a remote sensing image contains different objects, it is required to decide if one or more occurrences of a specific object class is existing in this image, and if so, detect locations of these objects, this needs to be successful in case of variation of viewpoint, occlusion, background clutterObjectivesModel a methodology to solve the problem stated above that can features the following baffle training data of unlimited object classes.Read high resolution remote sensing images and able to analyze its data.Detect occurrences of trained object classes in the remote sensing imagesDemonstrate results as a geo-referenced data type.In this thesis, we will demonstrate a model to achieve these objectives, and assess its results compared to other state-of-the-art models presented in the recent researches.Thesis LayoutThe thesis is composed of five chapters, the first chapter presenting an introduction stating the motivation, problem definition and objectives, second chapter is discussing the literature survey about the problem and researches in the field, third chapter presenting a detailed explanation of the methodology proposed to solve the problem. Fourth chapter contains the experimental results of the model. Fifth chapter discusses and concludes the methodology represented in this thesis, then a few points is suggested as a future work.