A New Generic Method for the Semi-automatic Extraction of River and Road Networks in Low and Mid-resolution Satellite Images PDF Download
Are you looking for read ebook online? Search for your book and save it on your Kindle device, PC, phones or tablets. Download A New Generic Method for the Semi-automatic Extraction of River and Road Networks in Low and Mid-resolution Satellite Images PDF full book. Access full book title A New Generic Method for the Semi-automatic Extraction of River and Road Networks in Low and Mid-resolution Satellite Images by . Download full books in PDF and EPUB format.
Author:
Publisher:
ISBN:
Category :
Languages : en
Pages :
Get Book Here
Book Description
This paper addresses the problem of semi-automatic extraction of road or hydrographic networks in satellite images. For that purpose, we propose an approach combining concepts arising from mathematical morphology and hydrology. The method exploits both geometrical and topological characteristics of rivers/roads and their tributaries in order to reconstruct the complete networks. It assumes that the images satisfy the following two general assumptions, which are the minimum conditions for a road/river network to be identifiable and are usually verified in low- to mid-resolution satellite images: (i) visual constraint: most pixels composing the network have similar spectral signature that is distinguishable from most of the surrounding areas; (ii) geometric constraint: a line is a region that is relatively long and narrow, compared with other objects in the image. While this approach fully exploits local (roads/rivers are modeled as elongated regions with a smooth spectral signature in the image and a maximum width) and global (they are structured like a tree) characteristics of the networks, further directional information about the image structures is incorporated. Namely, an appropriate anisotropic metric is designed by using both the characteristic features of the target network and the eigen-decomposition of the gradient structure tensor of the image. Following, the geodesic propagation from a given network seed with this metric is combined with hydrological operators for overland flow simulation to extract the paths which contain most line evidence and identify them with the target network.
Author:
Publisher:
ISBN:
Category :
Languages : en
Pages :
Get Book Here
Book Description
This paper addresses the problem of semi-automatic extraction of road or hydrographic networks in satellite images. For that purpose, we propose an approach combining concepts arising from mathematical morphology and hydrology. The method exploits both geometrical and topological characteristics of rivers/roads and their tributaries in order to reconstruct the complete networks. It assumes that the images satisfy the following two general assumptions, which are the minimum conditions for a road/river network to be identifiable and are usually verified in low- to mid-resolution satellite images: (i) visual constraint: most pixels composing the network have similar spectral signature that is distinguishable from most of the surrounding areas; (ii) geometric constraint: a line is a region that is relatively long and narrow, compared with other objects in the image. While this approach fully exploits local (roads/rivers are modeled as elongated regions with a smooth spectral signature in the image and a maximum width) and global (they are structured like a tree) characteristics of the networks, further directional information about the image structures is incorporated. Namely, an appropriate anisotropic metric is designed by using both the characteristic features of the target network and the eigen-decomposition of the gradient structure tensor of the image. Following, the geodesic propagation from a given network seed with this metric is combined with hydrological operators for overland flow simulation to extract the paths which contain most line evidence and identify them with the target network.
Author: Ting Peng
Publisher:
ISBN:
Category :
Languages : en
Pages : 156
Get Book Here
Book Description
Author: Jiangye Yuan
Publisher:
ISBN:
Category : Figure-ground perception
Languages : en
Pages : 65
Get Book Here
Book Description
Abstract: This thesis investigates automatic road extraction from satellite imagery based on locally excitatory globally inhibitory oscillator network (LEGION), which is introduced as a general framework for object segmentation. We extend the existing two-dimensional LEGION image segmentation algorithm to process multispectral and hyperspectral images. Road extraction from satellite images has been extensively studied. We develop a new automatic road extraction method using LEGION networks. An image is first segmented by LEGION. Then, medial axis transform is performed on the resulting segments, and the medial axis points corresponding to potential road regions are selected. A LEGION network with alignment-dependent connections is proposed to group well-aligned medial axis points, and generate extracted roads. Experiments on synthetic and real images show that the proposed method can effectively carry out road extraction from different satellite images. Comparisons with the existing methods demonstrate that the proposed method produces more accurate extraction results.
Author: Haihong Li
Publisher:
ISBN: 9783906513966
Category :
Languages : en
Pages : 164
Get Book Here
Book Description
Author: Selassie David Mayunga
Publisher:
ISBN:
Category : Buildings
Languages : en
Pages : 596
Get Book Here
Book Description
Author: Manami Barthakur
Publisher:
ISBN: 9787193905015
Category :
Languages : en
Pages : 0
Get Book Here
Book Description
Machine and deep learning techniques for content extraction of satellite images utilize artificial intelligence and neural networks to analyze and extract information from satellite imagery. These techniques can be used for a variety of applications, such as image classification, object detection, optical character recognition (OCR), and semantic segmentation. Convolutional Neural Networks (CNNs) are commonly used for image classification and object detection tasks. These networks are designed to process and understand images by analyzing the spatial relationship between pixels. They are composed of multiple layers, with each layer analyzing a different level of detail in the image. CNNs are particularly effective at identifying patterns and features in satellite images, such as roads, buildings, and vegetation. Recurrent Neural Networks (RNNs) and Long Short-term Memory (LSTM) networks are particularly useful for tasks that require the analysis of sequential data, like time series data. They are particularly useful in land cover change detection, change detection and time series analysis of satellite images. Semantic segmentation is the process of classifying each pixel in an image to a particular class, and it can be achieved using Fully Convolutional Networks (FCN) and U-Net architecture. This technique is particularly useful for identifying different land cover classes in satellite images, such as urban, agricultural, and natural areas. Generative Adversarial Networks (GANs) are used for creating synthetic images or super resolution of images. These are particularly useful for creating synthetic data for training and testing deep learning models for satellite images. Transfer learning is a technique that allows a pre-trained model to be fine-tuned for a specific task. This can be used to improve the accuracy of image classification and object detection tasks by using a pre-trained model as a starting point. In summary, machine and deep learning techniques for content extraction of satellite images involve using neural networks and computer vision techniques to analyze and extract information from satellite imagery. These techniques can be used for a variety of applications, such as image classification, object detection, and semantic segmentation, and can improve the accuracy and efficiency of extracting information from satellite images. to process and understand images by analyzing the spatial relationship between pixels. They are composed of multiple layers, with each layer analyzing a different level of detail in the image. CNNs are particularly effective at identifying patterns and features in satellite images, such as roads, buildings, and vegetation. Recurrent Neural Networks (RNNs) and Long Short-term Memory (LSTM) networks are particularly useful for tasks that require the analysis of sequential data, like time series data. They are particularly useful in land cover change detection, change detection and time series analysis of satellite images. Semantic segmentation is the process of classifying each pixel in an image to a particular class, and it can be achieved using Fully Convolutional Networks (FCN) and U-Net architecture. This technique is particularly useful for identifying different land cover classes in satellite images, such as urban, agricultural, and natural areas. Generative Adversarial Networks (GANs) are used for creating synthetic images or super resolution of images. These are particularly useful for creating synthetic data for training and testing deep learning models for satellite images. Transfer learning is a technique that allows a pre-trained model to be fine-tuned for a specific task. This can be used to improve the accuracy of image classification and object detection.
Author: Ethan Brewer
Publisher:
ISBN:
Category : Data integrity
Languages : en
Pages : 0
Get Book Here
Book Description
Satellite imagery analysis using deep learning methods, specifically convolutional neural networks (CNNs), has grown in popularity since 2012, with uses extending into the estimation of population, wealth, poverty, conflict, migration, education, and infrastructure, among other applications. This dissertation contributes to this body of literature in three parts. First, I explore the use of deep learning to overcome the sparsity, or complete lack, of accurate information regarding existing road infrastructure across much of the world. Using a novel labeled dataset generated by a custom-coded Android application, I show that a transfer learning approach can estimate road quality based on high-resolution satellite imagery with an accuracy of up to 80%. In the second chapter, I illustrate the vulnerability of this and related models to cyber intrusions (data poisoning), and propose a new technique to mitigate these vulnerabilities. The third chapter applies the lessons learned to propose a novel model architecture for spatiotemporal monitoring of industrial sites in inaccessible regions around the world, integrating high-resolution satellite imagery, a segmentation algorithm, and a pretrained deep learning framework to automatically detect and monitor individual industrial sites within the People's Republic of China. These three chapters advance our understanding of many of the challenges unique to computer vision in the context of satellite data, and provide some guidance on fruitful future directions.
Author:
Publisher:
ISBN:
Category :
Languages : en
Pages :
Get Book Here
Book Description
The discontinuities play an important role both in design and development stages of many geotechnical engineering projects. Because of that considerable time and capital should be spent to determine discontinuity sets by conventional methods. This thesis present the results of the studies associated with the application of the Remote Sensing (RS) and the development of a methodology in accurately and automatically detecting the discontinuity sets. For detection of the discontinuities, automatic lineament analysis is performed by using high resolution satellite imagery for identification of rock discontinuities. The study area is selected as an Andesite quarry area in Gölbaþý, Ankara, Turkey. For the high resolution data 8-bit Ikonos Precision Plus with 1 meter resolution orthorectified image is used. The automatic lineament extraction process is carried out with LINE module of PCI Geomatica v8.2. In order to determine the most accurate parameters of LINE, an accuracy assessment is carried out. To be the reference of the output, manual lineament extraction with directional filtering in four principal directions (N-S, E-W, NE-SW, NW-SE) is found to be the most suitable method. For the comparison of automatic lineament extraction and manual lineament extraction processes, LINECOMP program is coded in java environment. With the written code, a location and length based accuracy assessment is carried out. After the accuracy assesssment, final parameters of automatically extracted lineaments for rock discontinuity mapping for the study area are determined. Besides these, field studies carried out in the study area are also taken into consideration.
Author:
Publisher:
ISBN:
Category :
Languages : en
Pages : 0
Get Book Here
Book Description
This report describes an approach for automatic feature detection from fusion of remote sensing imagery using a combination of neural network architecture and the Dempster-Shafer (DS) theory of evidence. Deterministic or idealized shapes are used to characterize surface signatures of oceanic and atmospheric fronts manifested in satellite remote sensing imagery. Raw satellite images are processed by a bank of radial basis function (RBF) neural networks trained on idealized shapes. The final classification results from the fusion of the outputs of the separate RBF. The fusion mechanism is based on DS evidential reasoning theory. The approach is initially tested for detecting different features on a single sensor and extended to classifying features observed by multiple sensors.
Author: Yuanming Shu
Publisher:
ISBN:
Category :
Languages : en
Pages : 119
Get Book Here
Book Description
Developing methods to automatically extract objects from high spatial resolution (HSR) remotely sensed imagery on a large scale is crucial for supporting land user and land cover (LULC) mapping with HSR imagery. However, this task is notoriously challenging. Deep learning, a recent breakthrough in machine learning, has shed light on this problem. The goal of this thesis is to develop a deep insight into the use of deep learning to develop reliable automated object extraction methods for applications with HSR imagery. The thesis starts by re-examining the knowledge the remote sensing community has achieved on the problem, but in the context of deep learning. Attention is given to object-based image analysis (OBIA) methods, which are currently considered to be the prevailing framework for this problem and have had a far-reaching impact on the history of remote sensing. In contrast to common beliefs, experiments show that object-based methods suffer seriously from ill-defined image segmentation. They are less effective at leveraging the power of the features learned by deep convolutional neural networks (CNNs) than conventionally patch-based methods. This thesis then studies ways to further improve the accuracy of object extraction with deep CNNs. Given that vector maps are required as the final format in many applications, the focus is on addressing the issues of generating high-quality vector maps with deep CNNs. A method combining bottom-up deep CNN prediction with top-down object modeling is proposed for building extraction. This method also exhibits the potential to extend to other objects of interest. Experiments show that implementing the proposed method on a single GPU results in the capability of processing 756 km2 of 12 cm aerial images in about 30 hours. By post-editing on top of the resulting automated extraction, high-quality building vector maps can be produced about 4-times faster than conventional manual digitization methods.
