ALCM: Automatic land cover mapping
- PDF / 321,746 Bytes
- 7 Pages / 612 x 792 pts (letter) Page_size
- 45 Downloads / 252 Views
J. Indian Soc. Remote Sens. (June 2010) 38 : 239–245
RESEARCH ARTICLE
ALCM : Automatic Land Cover Mapping A. Kumar . S.K. Ghosh . V. K. Dadhwal
Received: 12 April 2009 / Accepted: 3 February 2010
Keywords Fuzzy c-means (FCM) . Possibilistic c-means (PCM) . Fuzzy error matrix (FERM)
Abstract It may be quite important for resource management people to extract single land cover class, at sub-pixel level from multi-spectral remote sensing images of different areas in single step processing. It has been observed, that neural network can be trained to extract single land cover class from multi-spectral remote sensing images, but they have problems in setting various parameters and slow during training stage. This paper present single land cover class water,
A. Kumar1( ) . S.K. Ghosh2 . V. K. Dadhwal1 1
Indian Institute of Remote Sensing, Dehradun - 24800, India 2 Indian Institute of technology, Roorkee - 247667, India
extraction from mixed pixels present in multiple multispectral remote sensing data sets of same bands of AWiFS sensor of Resoursesat-1 (IRS-P6) satellite from different areas. In this work fuzzy logic-based algorithm, which is independent of statistical distribution assumption of data, has been studied at sub-pixel level to handle mixed pixels. It has been found; possibilistic c-means (PCM) algorithm takes the possibilistic view, that the membership of a feature vector in a class has nothing to do with its membership in other classes. Due to this, it was observed that PCM can extract only one class, from remote sensing multi-spectral data and it has produced 93.7% and 97.1% overall sub-pixel classification accuracy for two different data sets of different places using LISS-III (IRS-P6) reference data of same dates as of AWiFS data.
Introduction
email: [email protected] email : [email protected]
Digital image classification is a fundamental and quick image processing operation to extract land cover information from remote sensing data and it assigns a
240
class membership for each pixel in an image. Land cover information can be extracted using crisp as well as fuzzy classification approaches. In a crisp classification, each image pixel is assumed to be pure and is classified to one class. Often, particularly in coarse spatial resolution images, the pixels may be mixed containing two or more classes. Fuzzy classifications may be beneficial where a mixed pixel may be assigned multiple class memberships. For this both supervised and unsupervised classification approaches may be followed. To identify a class of interest not only have to classify individual pixels as belonging to a specific class such as soil, vegetation or water but also identify a set of such pixels as a part of the class. In number of applications there may be requirement for extracting only one land cover class from remote sensing multispectral images, at sub-pixel level. In the past many learning algorithms like neural network has also been used for performing sub-pixel classification to extract single land cover class from r
Data Loading...
