Author: Yi-huang TaoPublisher:
ISBN:
Category : Artificial satellites in remote sensing
Languages : en
Pages : 300
Book Description
An Improved Approach to the Classification of Landsat Thematic Mapper Imagery
Author: Yi-huang TaoPublisher:
ISBN:
Category : Artificial satellites in remote sensing
Languages : en
Pages : 300
Book Description
Knowledge-based Classification of Landsat Thematic Mapper Digital Imagery
Author: Daniel Louis CivcoPublisher:
ISBN:
Category : Landsat Thematic Mapper
Languages : en
Pages : 428
Book Description
Land Cover Classification of Landsat Thematic Mapper Images Using Pseudo Invariant Feature Normalization Applied to Change Detection
Author: Tim HawesPublisher:
ISBN:
Category : Artificial satellites in remote sensing
Languages : en
Pages : 202
Book Description
"A radiometric normalization technique for compensating illumination and atmospheric differences between multi-temporal images should allow classification of the images with a single classification algorithm. This allows a simpler approach to land cover change detection. Land cover classification of Landsat Thematic Mapper Imagery with and without Pseudo Invariant Feature Normalization was performed to demonstrate the effect on classification and change detection accuracy. A post-classification change detection method using two separate classification algorithms, one for each date, was performed as a baseline comparison. Land cover classification using one classification algorithm was attempted with and without gain and offset correction to serve as another comparison. Accuracy verification was performed on the classification results by comparing random samples against ground truth."--Abstract.
The Effect of Training Block Size on Unsupervised Classification of Landsat Thematic Mapper Imagery
Author: Paul W. SnookPublisher:
ISBN:
Category : Remote sensing
Languages : en
Pages : 4
Book Description
Exploring Multi-temporal and Transformation Methods for Improved Cropland Classification Using Landsat Thematic Mapper Imagery
Author: Brianna N. MosimanEarth Resources
Author: Publisher:
ISBN:
Category : Astronautics in earth sciences
Languages : en
Pages : 758
Book Description
Advanced Remote Sensing
Author: Shunlin LiangPublisher: Academic Press
ISBN: 0123859557
Category : Science
Languages : en
Pages : 821
Book Description
Advanced Remote Sensing is an application-based reference that provides a single source of mathematical concepts necessary for remote sensing data gathering and assimilation. It presents state-of-the-art techniques for estimating land surface variables from a variety of data types, including optical sensors such as RADAR and LIDAR. Scientists in a number of different fields including geography, geology, atmospheric science, environmental science, planetary science and ecology will have access to critically-important data extraction techniques and their virtually unlimited applications. While rigorous enough for the most experienced of scientists, the techniques are well designed and integrated, making the book’s content intuitive, clearly presented, and practical in its implementation. Comprehensive overview of various practical methods and algorithms Detailed description of the principles and procedures of the state-of-the-art algorithms Real-world case studies open several chapters More than 500 full-color figures and tables Edited by top remote sensing experts with contributions from authors across the geosciences
Development and Evaluation of Advanced Classification Systems Using Remotely Sensed Data for Accurate Land-Use/Land-Cover Mapping
Author: Publisher:
ISBN:
Category :
Languages : en
Pages :
Book Description
Our general objective in this research was to explore, design, develop, implement, and evaluate advanced classification approaches for more accurate land-use/land-cover mapping using remotely sensed data. The overall research consists of three interrelated studies. Simulated Annealing (SA) has been shown to be able to overcome the local minimum problem in many optimization methods. Our hypothesis in the first study was that SA-based classification systems could help overcome the local minimum problem in one of such methods, K-means, and thus improve the classification performance. Our experimental results using Landsat Thematic Mapper (TM) images have shown that SA-based classification systems significantly improved the classification accuracy over that of the K-means algorithm when appropriate parameters were chosen. In the second study, we developed an automated Artificial Neural Network (ANN) classification system and performed a classification of a Landsat TM image. Two hypotheses were tested: 1) the ANN system was suitable for land cover mapping, and 2) the incorporation of SA network could overcome the local minima problem in ANN approaches and improve the resulting classification accuracy. Our study demonstrated that the ANN classification system was a robust and suitable classification system for land cover mapping. Experimental results indicated that the incorporation of SA improved the classification accuracy of an unsupervised ANN network. ANNs have been shown to have great potential to fuse multiple source data sets. Our hypothesis in the third study was that a proposed two-stage ANN-based multisource classification of Landsat TM and SPOT images could increase the classification accuracy of the derived land categories. Our experimental results demonstrated that the two-stage multisource classification had the best classification performance when proper training sets were used. The adequate and reliable training sets were successfully selected by an Autom.
Scientific and Technical Aerospace Reports
Author: Publisher:
ISBN:
Category : Aeronautics
Languages : en
Pages : 538
Book Description
Lists citations with abstracts for aerospace related reports obtained from world wide sources and announces documents that have recently been entered into the NASA Scientific and Technical Information Database.
Author: