Exploring Subpixel Learning Algorithms for Estimating Global Land Cover Fractions from Satellite Data Using High Performance Computing

Abstract

Land cover (LC) refers to the physical and biological cover present over the Earth's surface in terms of the natural environment such as vegetation, water, bare soil, etc. Most LC features occur at finer spatial scales compared to the resolution of primary remote sensing satellites. Therefore, observed data are a mixture of spectral signatures of two or more LC features resulting in mixed pixels. One solution to the mixed pixel problem is the use of subpixel learning algorithms to disintegrate the pixel spectrum into its constituent spectra. Despite the popularity and existing research conducted on the topic, the most appropriate approach is still under debate. As an attempt to address this question, we compared the performance of several subpixel learning algorithms based on least squares, sparse regression, signal-subspace and geometrical methods. Analysis of the results obtained through computer-simulated and Landsat data indicated that fully constrained least squares (FCLS) outperformed the other techniques. Further, FCLS was used to unmix global Web-Enabled Landsat Data to obtain abundances of substrate (S), vegetation (V) and dark object (D) classes. Due to the sheer nature of data and computational needs, we leveraged the NASA Earth Exchange (NEX) high-performance computing architecture to optimize and scale our algorithm for large-scale processing. Subsequently, the S-V-D abundance maps were characterized into four classes, namely forest, farmland, water and urban areas (in conjunction with nighttime lights data) over California, USA using a random forest classifier. Validation of these LC maps with the National Land Cover Database 2011 products and North American Forest Dynamics static forest map shows a 6% improvement in unmixing-based classification relative to per-pixel classification. As such, abundance maps continue to offer a useful alternative to high-spatial-resolution classified maps for forest inventory analysis, multi-class mapping, multi-temporal trend analysis, etc.

Classification of Paddy Rice Using a Stacked Generalization Approach and the Spectral Mixture Method Based on MODIS Time Series

“…The specific process and implement steps can be found in reference [62]. Subsequently, we applied the FCLS [63], [64] to get the unmixing results, and the expression of FCLS is as follows:…”

Assessing the Fractional Abundance of Highly Mixed Salt-Marsh Vegetation Using Random Forest Soft Classification

“…Classification methods applied to salt marshes have been developed for and applied to multiand hyperspectral remote sensing data in a diverse set of biomes worldwide [76][77][78][79][80][81][82]. The large majority of previous approaches to halophytic vegetation mapping determined vegetation abundance by identifying the dominant species in each pixel, using traditional supervised and unsupervised classification algorithms [2,44,76,[83][84][85][86].…”

Development of a Novel Burned-Area Subpixel Mapping (BASM) Workflow for Fire Scar Detection at Subpixel Level

“…Then, we downloaded the pre-and post-forest fire Sentinel 2 data sets and processed them. Second, burned area fraction was derived from the four selected Multispectral Instrument (MSI)/Sentinel 2 bands listed in Table 2 using the FCLS method [44,59,60]. Taking burned area fraction as input for the Modified Pixel Swapping Algorithm (MPSA) in Visual Studio 2012 platform edited by C# language, we obtained the burned area at subpixel level as output.…”

“…Zhang et al [41] presented SPA that can provide a general guideline to constrain the total number of endmembers. Kumar et al [44] estimated global land cover fractions from satellite data based on unconstrained lease squares (UCLS), fully constrained least squares (FCLS), modified fully constrained least squares (MFCLS), simplex projection (SP), sparse unmixing via variable splitting and augmented Lagrangian (SUnSAL), SUnSAL and total variation (SUnSAL TV), and Collaborateve SUnSAL (CL SUnSAL), and their results indicated that FCLS outperformed the other techniques. Thus, in this paper, we used FCLS for spectral unmixing of burned area in mixed pixels.…”

“…To solve this problem, Atkinson [45,46] proposed the subpixel concept for precise spatial locations of endmembers in a mixed pixel based on unmixing analysis [47]. On the basis of Atkinson's study, many algorithms have been developed [48], such as the spatial attraction model (SAM), double-calculated spatial attraction model (DSAM) [32], subpixel learning algorithms [44], subpixel edge detection method [49], etc. These subpixel techniques have found many applications [50][51][52][53].…”