Умови та обмеження методів інтелектуальної обробки космічних зображень для подальшого 3D моделювання

Розглянуто та проаналізовано існуючі методи, способи і засоби розпізнавання космічних знімків, визначено їх особливості і недоліки з точки зору подальшого тривимірного моделювання територій забудови. На підставі результатів аналізу виявлено необхідність створення нових методів і алгоритмів виділення складних архітектурних об'єктів. Представлено математичний опис основних характерних ознак будівель та їх елементів (дахів).Reviewed and analyzed existing methods, ways and means of recognition of satellite images, defined by their features and drawbacks in terms of three-dimensional modeling of further development areas. Based on the results of the analysis revealed the need for new methods and algorithms for complex extraction of architectural objects. A mathematical description of the main characteristic features of the buildings and their elements (roof).Рассмотрены и проанализированы существующие методы, способы и средства распознавания космических снимков, определены их особенности и недостатки с точки зрения дальнейшего трехмерного моделирования территорий застройки. На основании результатов анализа выявлена необходимость создания новых методов и алгоритмов выделения сложных архитектурных объектов. Представлено математическое описание основных характерных признаков зданий и их элементов (крыш)

Fusion of monocular cues to detect man-made structures in aerial imagery

The extraction of buildings from aerial imagery is a complex problem for automated computer vision. It requires locating regions in a scene that possess properties distinguishing them as man-made objects as opposed to naturally occurring terrain features. It is reasonable to assume that no single detection method can correctly delineate or verify buildings in every scene. A cooperative-methods paradigm is useful in approaching the building extraction problem. Using this paradigm, each extraction technique provides information which can be added or assimilated into an overall interpretation of the scene. Thus, the main objective is to explore the development of computer vision system that integrates the results of various scene analysis techniques into an accurate and robust interpretation of the underlying three dimensional scene. The problem of building hypothesis fusion in aerial imagery is discussed. Building extraction techniques are briefly surveyed, including four building extraction, verification, and clustering systems. A method for fusing the symbolic data generated by these systems is described, and applied to monocular image and stereo image data sets. Evaluation methods for the fusion results are described, and the fusion results are analyzed using these methods

Shadow Detection from VHR Images using Clustering and Classification

This project mainly focus to get the high resolution color remote sensing image, and also undertaken to remove the shaded region in the both urban and rural area. Some of the existing projects are involved to detect the shaded region and then eliminate that region, but it has some drawbacks. The detection of the edges will be affected mostly by the application of the external parameters. The edge detection process can be more helpful in the detection of the objects so that the objects can be used for further processing. In this process we have implement the Scale Space algorithm is used to detect the shadow region and extract the feature from the shadow region. Scale Space is simplest in region-base image segmentation methods. The concept of Scale Space algorithm is check the neighboring pixels of the initial seed points. Then determine whether those neighboring pixels are added to the seed points or not. In the Scale Space threshold algorithm Pixels are placed in the region based on their properties or the properties of the nearby pixel values. Then the pixel containing the similar properties is grouped together and then the large numbers of pixels are distributed throughout the image

Scale Space Based Object-Oriented Shadow Detection and Removal from Urban High-Resolution Remote Sensing Images

This task mostly center to get the high resolution color remote sensing image, and furthermore attempted to eliminate the concealed district in the both metropolitan and country region. A portion of the current activities are included to recognize the concealed district and afterward dispense with that area, yet it has a few disadvantages. The discovery of the edges will be influenced generally by the utilization of the outside boundaries. The edge location cycle can be more useful in the recognition of the articles with the goal that the items can be utilized for additional handling. In this cycle we have execute the Scale Space algorithm is utilized to identify the shadow area and concentrate the component from the shadow district. Scale Space is least complex in area base image segmentation strategies. The idea of Scale Space algorithm is check the neighboring pixels of the underlying seed focuses. At that point decide if those neighboring pixels are added to the seed focuses or not. In the Scale Space limit algorithm Pixels are set in the area dependent on their properties or the properties of the close by pixel esteems. At that point the pixel containing the comparable properties is gathered and afterward the enormous quantities of pixels are circulated all through the image

Automatic Building Change Detection in Wide Area Surveillance

We present an automated mechanism that can detect and characterize the building changes by analyzing airborne or satellite imagery. The proposed framework can be categorized into three stages: building detection, boundary extraction and change identification. To detect the buildings, we utilize local phase and local amplitude from monogenic signal to extract building features for addressing issues of varying illumination. Then a support vector machine with Radial basis kernel is used for classification. In the boundary extraction stage, a level-set function with self-organizing map based segmentation method is used to find the building boundary and compute physical area of the building segments. In the last stage, the change of the detected building is identified by computing the area differences of the same building that captured at different times. The experiments are conducted on a set of real-life aerial imagery to show the effectiveness of the proposed method

Separation and contrast enhancement of overlapping cast shadow components using polarization

Shadow is an inseparable aspect of all natural scenes. When there are multiple light sources or multiple reflections several different shadows may overlap at the same location and create complicated patterns. Shadows are a potentially good source of information about a scene if the shadow regions can be properly identified and segmented. However, shadow region identification and segmentation is a difficult task and improperly identified shadows often interfere with machine vision tasks like object recognition and tracking. We propose here a new shadow separation and contrast enhancement method based on the polarization of light. Polarization information of the scene captured by our polarization-sensitive camera is shown to separate shadows from different light sources effectively. Such shadow separation is almost impossible to realize with conventional, polarization-insensitive imaging

Graph Search and its Application in Building Extraction from High Resolution Remote Sensing Imagery

Building extraction using Hough transformation and cycle detection

Automatic building detection in aerial and satellite images

Abstract—Automatic creation of 3D urban city maps could be an innovative way for providing geometric data for varieties of applications such as civilian emergency situations, natural disaster management, military situations, and urban planning. Reliable and consistent extraction of quantitative information from remotely sensed imagery is crucial to the success of any of the above applications. This paper describes the development of an automated roof detection system from single monocular electro-optic satellite imagery. The system employs a fresh ap-proach in which each input image is segmented at several levels. The border line definition of such segments combined with line segments detected on the original image are used to generate a set of quadrilateral rooftop hypotheses. For each hypothesis a probability score is computed that represents the evidence of true building according to the image gradient field and line segment definitions. The presented results demonstrate that the system is capable of detecting small gabled residential rooftops with variant light reflection properties with high positional accuracies. Index Terms—Building extraction, satellite image processing, aerial image processing, photogrammetry, computer vision, geo-metrical shape extraction. I