NDLI: Texture estimation in SAR images of forests

Content Provider	IEEE Xplore Digital Library
Author	Pierce, L. Pan Liang Dobson, M.C.
Copyright Year	2003
Description	Author affiliation: Radiat. Lab., Michigan Univ., Ann Arbor, MI, USA (Pierce, L.; Pan Liang; Dobson, M.C.)
Abstract	Texture information plays an important role in interpreting remote sensing images, especially high spatial resolution images. Image texture measures the relationships between pixels and their neighbors. For SAR images, it is apparent that there are fluctuations in addition to speckle. Physically, these appear to be caused by variations in the scene structure. Texture reveals the intrinsic spatial variability of natural scenes and is a valuable feature to discriminate different land types. Many methods and models to describe and estimate texture have been developed. A major problem with texture models is the efficiency and computational complexity. We are faced with a compromise between choosing some simple parameters that fail to reproduce the observed image characteristics accurately and more sophisticated models (with many degrees of freedom) that better fit the observation but are slow to compute. In this paper, we investigate two texture models based on the two types of measurements: correlation length and Gaussian Markov random field(GMRF). The particular application of this study is to use the texture from JERS and RADARSAT data from the Amazon to better distinguish different classes within the forest. The texture information can be used by the classification and segmentation processes to improve the accuracy of the results. The estimated texture helps improve the estimation accuracy of forest parameters such as biomass and tree height.
Starting Page	4010
Ending Page	4012
File Size	1385994
Page Count	3
File Format	PDF
ISBN	0780379292
DOI	10.1109/IGARSS.2003.1295345
Language	English
Publisher	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Publisher Date	2003-07-21
Publisher Place	France
Access Restriction	Subscribed
Rights Holder	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Subject Keyword	Layout Remote sensing Spatial resolution Image texture Pixel Fluctuations Speckle Computational complexity Length measurement Biomass
Content Type	Text
Resource Type	Article

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