Enhanced Subpixel Mapping With Spatial Distribution Patterns of Geographical Objects

Abstract

This paper proposes spatial distribution patternbased subpixel mapping (SPM S ) as a novel subpixel mapping (SPM) strategy. It separately considers spatial distribution patterns of different types of geographical objects. Initially, it classifies geographical objects into areal, linear, and point patterns according to their spatially geometric characteristics. For the different patterns, SPM S uses the vectorial boundary-based SPM algorithm with the spatial dependence assumption to deal with areal objects, the linear template matching-based SPM algorithm for linear objects, and the spatial pattern consistency matching-based SPM algorithm for point objects. The three patterns are integrated to generate a subpixel map. An artificially created image and two remotely sensed images were used to evaluate the performance of SPM S . The results were compared with a traditional hard classifier and seven existing SPM methods. The experimental results demonstrated that SPM S performed better than the hard classification and traditional SPM methods, particularly when dealing with linear and point objects.

Object-Based Superresolution Land-Cover Mapping From Remotely Sensed Imagery

“…Deconvolution is an effective way to obtain point support semivariograms for ATPK because prior training data for deriving the point support semivariogram is often unavailable [8,28]. Deconvolution aims to iteratively seek a point support semivariogram that minimizes the difference between the theoretically regularized semivariogram and the areal support semivariogram fitted to areal data [47].…”

“…Fortunately, super resolution mapping (SRM) (also termed as subpixel mapping) technique is proposed as a promising solution to mixed pixels [4]. SRM fist disaggregates each coarse pixel in fractional images (i.e., the output of pixel-based soft classification) into fine subpixels and then determines where the subpixels of each land-cover class spatially distribute within a pixel [6][7][8][9][10][11][12]. Over the past decades, many SRM methods have been proposed.…”

Designing an Experiment to Investigate Subpixel Mapping as an Alternative Method to Obtain Land Use/Land Cover Maps

“…Another way to extract fraction images is to aggregate existing classification data to simulate the output of soft classification, such as the Resources and Environmental Scientific Data Center of the Chinese Academy of Sciences [34], National Land Cover Data 1992 [35], and Global Land Cover datasets [36]. Usually, a mean filter (such as 3ˆ3 pixels) or a regular polygon are used to calculate the class proportions within the mean filter or the polygon from the existing classification data to produce fraction images [7,13,17,18,33].…”

“…where A area is the area of the areal features, A linear is the area of features with linear pattern, and A point is the area of features with point pattern [33]. The sum of A area , A linear and A point is the total area.…”

Assessing a Temporal Change Strategy for Sub-Pixel Land Cover Change Mapping from Multi-Scale Remote Sensing Imagery

“…Sub-pixel mapping can be considered to be a post-processing stage of soft classification, in which the fraction images produced by soft classification are used as input to estimate a hard land cover map with fine spatial resolution [18]. A variety of sub-pixel mapping algorithms have been proposed, such as Hopfield neural networks [19][20][21], pixel-swapping algorithm [22], Markov random field [23], spatial attraction algorithms [24][25][26][27][28], vectorial boundary based algorithms [29,30], computational intelligence algorithms [31][32][33], and spatial regularization algorithm [34][35][36][37]. Sub-pixel mapping has been successfully used in many applications, such as the mapping urban trees [38], lakes [39], burned area [40] as well as in the refinement of ground control point location [41] and in the calculation of landscape pattern indices [42].…”