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Object based image analysis for building extraction?

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Object-based image analysis (OBIA) is a powerful technique for building extraction from satellite/aerial images. Various methods like K-means clustering, texture segmentation, and morphological operations are employed for accurate building extraction . Bayesian networks are utilized to model relationships between architectural targets and scene context, achieving over 90% accuracy in building extraction . OBIA, coupled with eCognition software, leverages spectral and shape information for urban land cover detection, with a focus on building height using LIDAR data . An object-based classification method integrating Quickbird imagery and LiDAR data enhances urban area extraction, with Artificial Neural Networks (ANN) outperforming other methods in accurately delineating building boundaries . Additionally, an improved GGVF snake model optimizes building contours post-extraction, enhancing accuracy and completeness in complex building delineation .

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Open access•Journal Article•DOI Object-Oriented Building Contour Optimization Methodology for Image Classification Results via Generalized Gradient Vector Flow Snake Model Chang Jingxin, Xianjun Gao, Yuanwei Yang, Nan Wang - Show less +3 more 19 Jun 2021-Remote Sensing 2 Citations	Object-oriented building contour optimization method enhances building extraction accuracy via a GGVF snake model, improving post-processing results for image classification.
Open access•Journal Article•DOI Object-Based Method for Urban Extraction through Using Quick Bird Satellite Imagery, LiDAR Data and Digital Urban Geomatics Techniques 04 Mar 2022-Tikrit Journal of Engineering Science	The study utilized object-based classification integrating QuickBird imagery and LiDAR data to extract urban features, with Artificial Neural Networks achieving the highest accuracy for building extraction.
Open access•Journal Article•DOI Object-based approach for urban land cover mapping using high spatial resolution data Malgorzata Verone Wojtaszek, Levente Ronczyk, Zokhid Mamatkulov, Mamanbek Reimov - Show less +3 more 01 Jan 2021 10 Citations	Object-based image analysis, utilizing high-resolution data and elevation information, is effective for building extraction in urban areas, enhancing classification accuracy by considering spectral and geometric features.
Open access•Journal Article•DOI Computer Image Scene and Object Information Extraction based on Bayesian Network Model Huijun Zhao, Guo Chen - Show less +1 more 06 Jun 2023-Applied mathematics and nonlinear sciences	The paper proposes a Bayesian network model for effective building extraction from aerial images, demonstrating successful object-based image analysis for building extraction.
Book Chapter•DOI Extraction of Buildings from Images Based on Segmentation V. S. Shankar Sriram 01 Jan 2022	The paper utilizes segmentation techniques like K-means clustering, texture segmentation, thresholding, and morphological operations for accurate building extraction from images, enhancing object-based image analysis.

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What are the most common techniques used in image processing for object recognition and classification?4 answersThe most common techniques used in image processing for object recognition and classification include the maximum likelihood method, minimum Bayes risk classifier, principal component analysis (PCA), linear discriminant analysis (LDA), nearest neighbor classifiers, and neural network classifiers. Evolutionary approaches, such as sliding window algorithms and the use of spatial information, are also employed for object detection. Convolutional Neural Networks (CNNs) are dominant technologies in object detection due to their speed and accuracy. Other techniques like R-CNN, Fast RCNN, and Faster RCNN are also used for object detection and have been compared in various studies. Additionally, the Mask Region-based Convolutional Neural Network (Mask R-CNN) is an efficient approach for object localization and recognition, allowing for precise identification and segmentation of objects in images.

What are the most promising visual object recognition methods for automated digital modeling of existing buildings?5 answersAutomated object recognition methods using computer vision and deep learning techniques show promise for the automated digital modeling of existing buildings. These methods leverage 2D drawings and utilize image classification and object detection algorithms to reduce the time and effort required for 3D modeling. Convolutional Neural Networks (CNN) have been used for 3D object recognition in indoor environments, addressing challenges such as occlusion and incomplete data sets. Additionally, integrating multi-scale data and utilizing knowledge graphs can improve the efficiency and effectiveness of building pattern recognition, such as recognizing C-shaped building patterns. These advancements in visual object recognition methods offer potential for automating the creation and maintenance of digital building representations, enabling new forms of simulation, automation, and information sharing.

How I analyze an object?5 answersTo analyze an object, there are several methods and techniques available. One approach is to use spectrometric measurements of electromagnetic radiation emitted by the object. Another method involves determining the interest degree grade of objects based on visitor orientation direction and movement track trend. Data representing the object can be processed using algorithms to specify an action for the object, which can then be analyzed and assessed based on the relationships and function-related elementary units. In the case of nanometric scale objects, an evaluation can be done by acquiring an image using a charged particle beam system, determining attributes from the image, reducing the attributes, generating guidelines, and evaluating the actual object based on the guidelines. Additionally, gas-phase ion spectroscopic methods can be used to separate and analyze living-body molecules in a sample.

What are articles that used spatial attention for building extraction?5 answersSpatial attention has been used in several articles for building extraction. Ding et al. proposed a deeply supervised attention-guided network (DSA-Net) that incorporates a spatial attention mechanism-guided cross-layer addition and skip-connection module to aggregate contextual information and direct attention to changed regions. Wang et al. developed a multi-stage attention U-Net (MSAU-Net) architecture that utilizes a spatial attention mechanism to extract building footprints from multi-spectral images. Li et al. introduced an attention-enhanced U-Net for building extraction from farmland, which incorporates a spatial and channel attention mechanism module to improve the U-Net network. These articles demonstrate the effectiveness of spatial attention in improving the accuracy of building extraction from different types of remote sensing images.

How can spatial attention be used for building extraction?5 answersSpatial attention can be used for building extraction by guiding the network's attention to changed regions and aggregating multi-level contextual information. This can be achieved through the use of attention mechanisms, such as spatial attention-guided cross-layer addition and skip-connection modules, as well as the incorporation of attention-enhanced U-Net networks. These attention mechanisms help to weaken the heterogeneity between raw image features and difference features, extract multi-scale features, and enhance the ability of middle layers to extract more distinctive features. By implementing these spatial attention mechanisms, building extraction methods have shown improved accuracy and performance in various datasets, including high-resolution images, remote sensing images, and aerial and satellite images..

How to detect objects in images using Opencv?7 answers

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