Download or read book Urban Land Cover Classification and Moving Vehicle Extraction Using Very High Resolution Satellite Imagery written by Bahram Salehi and published by . This book was released on 2012 with total page 0 pages. Available in PDF, EPUB and Kindle. Book excerpt:

Download or read book Qualit t in Beratung und Bildungsarbeit written by and published by . This book was released on 1998 with total page pages. Available in PDF, EPUB and Kindle. Book excerpt:

Download or read book A Land Use and Land Cover Classification System for Use with Remote Sensor Data written by James Richard Anderson and published by . This book was released on 1976 with total page 36 pages. Available in PDF, EPUB and Kindle. Book excerpt:

Download or read book A Comparison of Two Common Classification Procedures for Economical Urban Land Cover Mapping Using NAIP Imagery written by Kent Lowell Simons and published by . This book was released on 2009 with total page 102 pages. Available in PDF, EPUB and Kindle. Book excerpt: Detailed urban land cover maps are increasingly useful and important applications of remote sensing. Municipal agencies and others use land cover maps and data for numerous critical local planning and monitoring functions and for urban geographical research studies. Because of this, there is a demand for accurate urban land cover maps that can be produced quickly and economically. The availability of very high resolution multispectral imagery is an important factor in enabling such production, as the judicious selection of source imagery has a large impact on the resulting map products. Likewise, the implementation of appropriate digital image processing methods is crucial for deriving urban land cover maps of acceptable accuracy and cost. This study compared two common image classification algorithms using 2006 NAIP 1-meter GSD CIR images of Orem and Provo, Utah. The two classification procedures - conventional per-pixel supervised classification coupled with post-classification filtering, and object-based feature extraction - were compared for resulting accuracy and, in general terms, for their cost-effectiveness. Results demonstrated that object-based feature extraction has the potential to produce maps with better accuracy, but at a somewhat higher cost than per-pixel supervised classification. Classification errors and their probable causes are discussed; also a number of options for improving the classification accuracy are presented together with considerations of the potential costs involved. Although the ultimate goal of economical production of accurate urban land cover maps was not fully realized, this study nevertheless has established a cost containment baseline upon which methodological improvements can be built.

Download or read book Object Oriented Image Analysis written by Mike Lackner and published by VDM Publishing. This book was released on 2008 with total page 156 pages. Available in PDF, EPUB and Kindle. Book excerpt: Creating land-cover and land-use maps for urban areas has always been challenging due to the complexity of an urban landscape. The greater availability of remotely sensed high-resolution imagery and recent advances in object-oriented analysis allow for more detail than ever before in urban image classification. This book explores object-oriented land-cover and land-use classifications with one-meter resolution Ikonos imagery. First, three different ways of classifying land cover, with varying inclusion of ancillary building and road data, are examined. Second, the spatial relations of the land-cover information are examined to derive land use. Accuracy assessments, as well as statistical and visual evaluations, for the classifications show that the object-oriented approach works well for classifying an urban area. The results confirm the observed trend in the literature to move away from traditional pixel-based techniques for urban image classification in exchange for object-oriented methods. This book will be useful to those who want to develop new ways of efficiently classifying complex urban areas, using object-oriented image classification techniques.

Download or read book Object Based Image Analysis written by Thomas Blaschke and published by Springer Science & Business Media. This book was released on 2008-08-09 with total page 804 pages. Available in PDF, EPUB and Kindle. Book excerpt: This book brings together a collection of invited interdisciplinary persp- tives on the recent topic of Object-based Image Analysis (OBIA). Its c- st tent is based on select papers from the 1 OBIA International Conference held in Salzburg in July 2006, and is enriched by several invited chapters. All submissions have passed through a blind peer-review process resulting in what we believe is a timely volume of the highest scientific, theoretical and technical standards. The concept of OBIA first gained widespread interest within the GIScience (Geographic Information Science) community circa 2000, with the advent of the first commercial software for what was then termed ‘obje- oriented image analysis’. However, it is widely agreed that OBIA builds on older segmentation, edge-detection and classification concepts that have been used in remote sensing image analysis for several decades. Nevert- less, its emergence has provided a new critical bridge to spatial concepts applied in multiscale landscape analysis, Geographic Information Systems (GIS) and the synergy between image-objects and their radiometric char- teristics and analyses in Earth Observation data (EO).

Download or read book Detailed Urban Land cover Mapping Using a Data Fusion Procedure written by Amit A. Kokje and published by . This book was released on 2015 with total page 205 pages. Available in PDF, EPUB and Kindle. Book excerpt: The significance of urban land-cover information has increased greatly in the past decade following the rapid growth of geo-location services. Efforts to automate existing labour-intensive and time-consuming thematic data acquisition and updating procedures gained considerable momentum once very high-resolution images were provided by a fleet of satellites. However, the procedures for extracting land-cover information from such images failed to produce acceptable results for spectrally and spatially-complex urban land features. Attempts have been made to integrate information that is complementary to the spectral and spatial data in order to successfully overcome the typical problems faced by reflectance-based classification methods. Most such data fusion endeavours are specially customised and feature specific procedures, but fail to adapt to different urban scenarios. This study tackles the problems commonly associated with urban land-cover classification through fusing multi-sensory data. A multistage segmentation classification workflow using LiDAR and WorldView-2 data based on existing data fusion methods is developed to identify the land-covers that are normally found in core urban areas. The results show that integration of the height products derived from the 3D LiDAR data brought many benefits. Elevation (nDSM), used as a main data source during image segmentation, produced sharper image object boundaries, thus reducing the impact of shadows. A novel elevation-based feature differentiation step was implemented prior to thematic classification effectively separated a range of spectrally-identical impervious land-covers into two intermediate sub-classes. As a result, it was possible to derive and integrate two separate classification processes targeting a particular group of land-covers. Collectively, the integration of multi-stage procedures and multi-sensory data products significantly improved the thematic classification accuracy, from the 75% obtained using the normal method of spectral classification, to 93%. As a secondary outcome of this study, a spectral index called ZABUD is successfully modified for WorldView-2 multispectral channels. This index is found to be beneficial in identifying impervious asphalt features. The standard nearest neighbour classifier was later replaced with other methods such as the K-nearest neighbour, the support vector machine and the DC-CART, with the obtained results being comparatively assessed at the end of the study. A comparison of the classification accuracies obtained further confirmed the flexibility of the proposed workflow in adapting various procedures to the different scenarios of land-covers.

Download or read book Land Cover Classification of Remotely Sensed Images written by S. Jenicka and published by Springer Nature. This book was released on 2021-03-10 with total page 176 pages. Available in PDF, EPUB and Kindle. Book excerpt: The book introduces two domains namely Remote Sensing and Digital Image Processing. It discusses remote sensing, texture, classifiers, and procedures for performing the texture-based segmentation and land cover classification. The first chapter discusses the important terminologies in remote sensing, basics of land cover classification, types of remotely sensed images and their characteristics. The second chapter introduces the texture and a detailed literature survey citing papers related to texture analysis and image processing. The third chapter describes basic texture models for gray level images and multivariate texture models for color or remotely sensed images with relevant Matlab source codes. The fourth chapter focuses on texture-based classification and texture-based segmentation. The Matlab source codes for performing supervised texture based segmentation using basic texture models and minimum distance classifier are listed. The fifth chapter describes supervised and unsupervised classifiers. The experimental results obtained using a basic texture model (Uniform Local Binary Pattern) with the classifiers described earlier are discussed through the relevant Matlab source codes. The sixth chapter describes land cover classification procedure using multivariate (statistical and spectral) texture models and minimum distance classifier with Matlab source codes. A few performance metrics are also explained. The seventh chapter explains how texture based segmentation and land cover classification are performed using the hidden Markov model with relevant Matlab source codes. The eighth chapter gives an overview of spatial data analysis and other existing land cover classification methods. The ninth chapter addresses the research issues and challenges associated with land cover classification using textural approaches. This book is useful for undergraduates in Computer Science and Civil Engineering and postgraduates who plan to do research or project work in digital image processing. The book can serve as a guide to those who narrow down their research to processing remotely sensed images. It addresses a wide range of texture models and classifiers. The book not only guides but aids the reader in implementing the concepts through the Matlab source codes listed. In short, the book will be a valuable resource for growing academicians to gain expertise in their area of specialization and students who aim at gaining in-depth knowledge through practical implementations. The exercises given under texture based segmentation (excluding land cover classification exercises) can serve as lab exercises for the undergraduate students who learn texture based image processing.

Download or read book A Random Forest Based Method for Urban Land Cover Classification Using LiDAR Data and Aerial Imagery written by Jiao Jin and published by . This book was released on 2012 with total page 134 pages. Available in PDF, EPUB and Kindle. Book excerpt: Urban land cover classification has always been crucial due to its ability to link many elements of human and physical environments. Timely, accurate, and detailed knowledge of the urban land cover information derived from remote sensing data is increasingly required among a wide variety of communities. This surge of interest has been predominately driven by the recent innovations in data, technologies, and theories in urban remote sensing. The development of light detection and ranging (LiDAR) systems, especially incorporated with high-resolution camera component, has shown great potential for urban classification. However, the performance of traditional and widely used classification methods is limited in this context, due to image interpretation complexity. On the other hand, random forests (RF), a newly developed machine learning algorithm, is receiving considerable attention in the field of image classification and pattern recognition. Several studies have shown the advantages of RF in land cover classification. However, few have focused on urban areas by fusion of LiDAR data and aerial images. The performance of the RF based feature selection and classification methods for urban areas was explored and compared to other popular feature selection approach and classifiers.

Download or read book Land Cover Mapping and Change Detection in Urban Watersheds Using QuickBird High Spatial Resolution Satellite Imagery written by David Barry Hester and published by . This book was released on 2008 with total page 133 pages. Available in PDF, EPUB and Kindle. Book excerpt: Keywords: impervious surface, water quality, remote sensing, land use.

Download or read book Urban Land Use Extraction from Very High Resolution Remote Sensing Images written by Mengmeng Li and published by . This book was released on 2017 with total page 126 pages. Available in PDF, EPUB and Kindle. Book excerpt:

Download or read book Land Cover Mapping and Change Detection in Urban Watersheds Using QuickBird High Spatial Resolution Satellite Imagery written by and published by . This book was released on 2004 with total page pages. Available in PDF, EPUB and Kindle. Book excerpt:

Download or read book Selected papers from the 2019 IEEE International Workshop on Metrology for AeroSpace written by Pasquale Daponte and published by MDPI. This book was released on 2021-04-21 with total page 446 pages. Available in PDF, EPUB and Kindle. Book excerpt: This book is devoted to recent developments of instrumentation and measurement techniques applied to the aerospace field. It includes 23 selected papers from the 2019 IEEE International Workshop on Metrology for AeroSpace. Measurements are essential for obtaining a deeper knowledge of a phenomenon or an asset, as well as for making proper decisions and proposing new and efficient solutions, and this is especially true in environments as complex as aerospace. The research contributions included in the book can raise the interest of a wide group of researchers, operators and decision-makers from metrology and aerospace fields by presenting the most innovative solutions in this field from the scientific and technological points of view.

Download or read book Digital and Visual Classification of Land Use land Cover Using Landsat MSS and High Altitude Photography Data written by Ramiro Salcedo and published by . This book was released on 1984 with total page 188 pages. Available in PDF, EPUB and Kindle. Book excerpt:

Download or read book Data driven Feature Learning for High Resolution Urban Land Cover Classification written by Piotr Andrzej Tokarczyk and published by . This book was released on 2015 with total page pages. Available in PDF, EPUB and Kindle. Book excerpt:

Download or read book Prospects for Mapping Urban Land Cover from Three metre Resolution Satellite Imagery written by Valdis Janis Kalnins and published by . This book was released on 1997 with total page 216 pages. Available in PDF, EPUB and Kindle. Book excerpt:

Download or read book Historical Land Use Land Cover Classification Using Remote Sensing written by Wafi Al-Fares and published by Springer Science & Business Media. This book was released on 2013-06-25 with total page 216 pages. Available in PDF, EPUB and Kindle. Book excerpt: Although the development of remote sensing techniques focuses greatly on construction of new sensors with higher spatial and spectral resolution, it is advisable to also use data of older sensors (especially, the LANDSAT-mission) when the historical mapping of land use/land cover and monitoring of their dynamics are needed. Using data from LANDSAT missions as well as from Terra (ASTER) Sensors, the authors shows in his book maps of historical land cover changes with a focus on agricultural irrigation projects. The kernel of this study was whether, how and to what extent applying the various remotely sensed data that were used here, would be an effective approach to classify the historical and current land use/land cover, to monitor the dynamics of land use/land cover during the last four decades, to map the development of the irrigation areas, and to classify the major strategic winter- and summer-irrigated agricultural crops in the study area of the Euphrates River Basin.