Article Sidebar
Article Details
Authors who submit papers to this journal agree to the following terms:
a) Authors retain copyright over their work, while allowing the journal to place this work on the journal website under a Creative Commons Attribution License, which allows others to freely access, use, and share the work, with an acknowledgment of the work's authorship and its initial publication in this journal.
b) Authors are able to waive the terms of the CC license and enter into separate, additional contractual arrangements for the non-exclusive distribution and subsequent publication of this work (e.g., post it to an institutional repository or publish it in a book), with an acknowledgment of its initial publication in this journal.
c) In addition, authors are encouraged to post and share their work online (e.g., in institutional repositories or on their website) at any point after publication on the journal website.
Main Article Content
Comparing Three Spaceborne Optical Sensors via Fine Scale Pixel-based Urban Land Cover Classification Products
Abstract
from a pixel-based classification principle in a suburban area. Following a systematic working procedure, manual segmentation and vegetation indices were applied to generate smaller subsets to in turn develop sets of ISODATA unsupervised classification maps. With the focus on the land cover classification differences detected between the sensors at spectral level, the validation of accuracies and their relevance for fine scale classification in the built-up environment domain were examined. If an overview of an urban area is required, RapidEye will provide an above average (0.69 k) result with the built-up class sufficiently extracted. The higher resolution sensors such as WorldView-2 and Pleiades in comparison delivered finer scale accuracy at pixel and parcel level with high correlation and accuracy levels (0.65-0.71k) achieved from these two independent classifications.
