A new sub-pixel mapping method based on spatial autocorrelation and landscape indexes
نویسنده
چکیده
Geospatial Technology is one of the three emerging technologies in the 21st century. Remote sensing images typically contain a combination of pure and mixed pixels. Mixed pixels result when the sensor's instantaneous field-of-view (IFOV) includes more than one land cover class on the ground. The phenomenon of this "mixed pixels" caused great difficulties to remote sensing image classification. Moreover, it serious impacts on the accuracy and effectiveness of the results of remote sensing image classification. It has become to the major issue which to obstruct the quantitative of remote sensing technology in-depth development. These mixed pixels pose a difficult problem for RS classification, as their spectral characteristics are not representative of any single land cover class. In fact, the value of each pixel is the composite spectral signature of the land cover types present. For these mixed pixels, fuzzy classifiers can be used, which assign a pixel to several land cover classes in proportion to the area of the pixel that each class covers. Fuzzy techniques aim to estimate the proportions of specific classes that occur within each pixel. The result is a number of fraction images, one for each land cover class concerned. While this information describes the class composition, it does not provide any indication as to how the classes are spatially distributed within the pixel. A limited number of methods for solving this sub-pixel mapping problem have been proposed. Schneider (1993) introduced a knowledge-based analysis technique for automatic localization of field boundaries in scenes of agricultural areas. It is applicable to homogeneous fields with straight boundaries. Gavin and Jennison (1997) adopted a Bayesian approach and incorporated prior information about the true image in a stochastic model that attaches higher probability to images with shorter total edge length. Atkinson (1997)
منابع مشابه
Super-resolution Mapping of Landscape Objects from Coarse Spatial Resolution Imagery
The landscape patches that are fundamental to landscape ecology may be considered as objects to be extracted from remotely sensed imagery. The accuracy with which objects may be characterised varies as a function of the spatial resolution of the imagery used. In general terms, a coarsening of the spatial resolution degrades the characterization of objects, notably through an increase in the pro...
متن کاملLand Cover Mapping at Sub-Pixel Scales: Unraveling the Mixed Pixel
This study investigates the “pixel-swapping” optimization algorithm for predicting sub-pixel land cover distribution. Two limitations of this method, the arbitrary spatial range value and the arbitrary exponential model of spatial autocorrelation are assessed. Various weighting functions, as alternatives to the exponential model, are evaluated in order to derive the optimum weighting function. ...
متن کاملAdaptive Multi-objective Sub-pixel Mapping Framework Based on Memetic Algorithm for Hyperspectral Remote Sensing Imagery
Sub-pixel mapping technique can specify the location of each class within the pixels based on the assumption of spatial dependence. Traditional sub-pixel mapping algorithms only consider the spatial dependence at the pixel level. The spatial dependence of each sub-pixel is ignored and sub-pixel spatial relation is lost. In this paper, a novel multi-objective sub-pixel mapping framework based on...
متن کاملAssessing Alternatives for Modeling the Spatial Distribution of Multiple Land-cover Classes at Sub-pixel Scales
We introduce and evaluate three methods for modeling the spatial distribution of multiple land-cover classes at subpixel scales: (a) sequential categorical swapping, (b) simultaneous categorical swapping, and (c) simulated annealing. Method 1, a modification of a binary pixel-swapping algorithm, allocates each class in turn to maximize internal spatial autocorrelation. Method 2 simultaneously e...
متن کاملSpatial-Temporal Sub-Pixel Mapping Based on Swarm Intelligence Theory
In the past decades, sub-pixel mapping algorithms have been extensively developed due to the large number of different applications. However, most of the sub-pixel mapping algorithms are based on single-temporal images, and the results are usually compromised without auxiliary information due to the ill-posed problem of sub-pixel mapping. In this paper, a novel spatial-temporal sub-pixel mappin...
متن کامل