Author: James A. KingPublisher:
ISBN:
Category : Remote sensing
Languages : en
Pages : 250
Book Description
A Landsat Image Classification Accuracy Assessment Program
Author: James A. KingPublisher:
ISBN:
Category : Remote sensing
Languages : en
Pages : 250
Book Description
Accuracy Assessment of Supervised and Unsupervised Classification Using Landsat Imagery of Little Rock, Arkansas
Author: Rex PeacockPublisher:
ISBN:
Category : Electronic dissertations
Languages : en
Pages : 48
Book Description
Remotely sensed data is an important component of land use/land cover (LULC) studies. This research utilized the vegetation-impervious surface-soil (V-I-S) model. Using Enhanced Thematic Mapper Plus (ETM+) imagery, this research compared the accuracy of supervised and unsupervised classification by analyzing three study areas in and near Little Rock, Arkansas. The first study area was a homogeneous region comprised primarily of water features. The second study area was a region of an intermediate mix of land cover classes. The third study area was a region of heterogeneous land cover composition between the four land cover classes of the V-I-S model. Upon the completion of supervised and unsupervised classification, 200 points for each area were randomly generated using a stratified random sampling approach. The land cover data associated with these points were then compared to ground truth data derived from higher-resolution imagery from the National Agriculture Imagery Program (NAIP). Based on error matrices, the homogeneous and intermediate study areas featured higher accuracy values for unsupervised classification over supervised classification. For the heterogeneous study area, supervised classification was more accurate than unsupervised classification by one percent.
A Land Use and Land Cover Accuracy Assessment Based on Landsat 7 Imagery Within the Canandaigua Watershed
Author: Travis MoneyPublisher:
ISBN:
Category : Land use
Languages : en
Pages : 132
Book Description
Analyzes how well the Natural Heritage Classification (NHC) and the Anderson Classification systems classify an area based on Landsat imagery and a modern (circa 2002) land cover map created by the Canandaigua Lake Watershed Council (CLWC). A geographic information system (GIS) is used to reclassify the Landsat imagery into land uses and land covers based on the two classification systems and to produce tables expressing the accuracy of each system as compared to the truth image.
Assessing the Accuracy of Remotely Sensed Data
Author: Russell G. CongaltonPublisher: CRC Press
ISBN: 1420055135
Category : Mathematics
Languages : en
Pages : 210
Book Description
Accuracy assessment of maps derived from remotely sensed data has continued to grow since the first edition of this groundbreaking book. As a result, the much-anticipated new edition is significantly expanded and enhanced to reflect growth in the field. The new edition features three new chapters, including: Fuzzy accuracy assessmentPositional accu
A Land Use and Land Cover Classification System for Use with Remote Sensor Data
Author: James Richard AndersonPublisher:
ISBN:
Category : Land cover
Languages : en
Pages : 36
Book Description
Remote Sensing Image Classification in R
Author: Courage KamusokoPublisher: Springer
ISBN: 9811380120
Category : Technology & Engineering
Languages : en
Pages : 189
Book Description
This book offers an introduction to remotely sensed image processing and classification in R using machine learning algorithms. It also provides a concise and practical reference tutorial, which equips readers to immediately start using the software platform and R packages for image processing and classification. This book is divided into five chapters. Chapter 1 introduces remote sensing digital image processing in R, while chapter 2 covers pre-processing. Chapter 3 focuses on image transformation, and chapter 4 addresses image classification. Lastly, chapter 5 deals with improving image classification. R is advantageous in that it is open source software, available free of charge and includes several useful features that are not available in commercial software packages. This book benefits all undergraduate and graduate students, researchers, university teachers and other remote- sensing practitioners interested in the practical implementation of remote sensing in R.
Digital Classification of Landsat Data for Vegetation and Land-cover Mapping in the Blackfoot River Watershed, Southeastern Idaho
Author: Lawrence R. PettingerPublisher:
ISBN:
Category : Blackfoot River Watershed (Idaho).
Languages : en
Pages : 44
Book Description
A case study, including step-by-step procedures for computer-assisted analysis of Landsat digital data, with emphasis on assessment of classification accuracy and generation of output products.
Classification Accuracy Assessment of Spatially Improved Satellite Images and the Need for Single Photo Resected Images in a GIS Database
Author: Venu G. OddirajuPublisher:
ISBN:
Category : Geodatabases
Languages : en
Pages : 268
Book Description
Determination of Classification Accuracy for Land Use/cover Types Using Landsat-TM, SPOT-MSS and Multi-polarized and Multi-channel Synthetic Aperture Radar (SAR) Data
Author: Mehmet DondururExperimental Design in Psychological Research
Author: Allen Louis EdwardsPublisher: Holt McDougal
ISBN:
Category : Psychology
Languages : en
Pages : 522
Book Description