Land use mapping is one of the major applications of remote sensing. While most studies focus on the advanced remote
sensing thematic classification algorithms for land use mapping, the scale factor in remote sensing data classification was
less recognized. Previous studies showed that while the multi-scale characteristics exist in the remotely sensed data for
land use classification, some classes are mostly accurately classified at finer resolution, and others at coarser ones. Thus,
it is helpful to improve the overall classification accuracy by mapping different land use classes at different scales. In this
paper, a framework for improving the land use classification accuracy by exploiting the multi-scale properties of
remotely sensed data is presented. Firstly, the remotely sensed data at original fine resolution was up-scaled to different
coarser resolutions; Secondly, the up-scaled data were classified by independently trained Maximum Likelihood
Classifier at every resolution, and the corresponding a Posteriori Probability of MLC classification was saved; Thirdly,
the classification results at different resolutions were integrated by comparing the a Posteriori Probability of
classification at every resolution. The final class of pixel was labeled as the class that has the maximum a Posteriori
Probability. A case study on the land use mapping using Landsat TM data using this framework was conducted in the
Dianchi Watershed in Yunnan Province of China. The land use was categorized into 6 classes. The classification accuracy
was assessed using the Confusion Matrix. Comparison between the classification accuracy at multi-scale and that at
original resolution showed an improvement of overall classification accuracy by about 10%. The study showed that by
exploiting the multi-scale properties in the remotely sensed data, the accuracy the land use mapping can be improved
significantly.
Speckle noise is a common phenomenon in SAR images. The reduction of Speckle is necessary for any further processing of SAR image such as segmentation, classification and other procedures for information extraction. In this paper, after a brief review of conventional filters for SAR speckle reduction, a wavelet-based soft-thresholding filter for SAR speckle reduction is presented. To evaluate the performance of this filter, the adaptive local statistics filters, which include Lee, Frost, Enhanced Lee, Enhanced Frost, Kuan, and the Gamma-Map filter are applied to the speckle reduction for a same typical SAR image. The performances are compared in several aspects including Radiometric preservation, feature preservation, speckle reduction in extended uniform regions and the absence of artifacts. The results show that the wavelet-based speckle reduction filter performs better in every aspect in evaluation than the conventional filters do.
Access to the requested content is limited to institutions that have purchased or subscribe to SPIE eBooks.
You are receiving this notice because your organization may not have SPIE eBooks access.*
*Shibboleth/Open Athens users─please
sign in
to access your institution's subscriptions.
To obtain this item, you may purchase the complete book in print or electronic format on
SPIE.org.
INSTITUTIONAL Select your institution to access the SPIE Digital Library.
PERSONAL Sign in with your SPIE account to access your personal subscriptions or to use specific features such as save to my library, sign up for alerts, save searches, etc.