Decomposition of mixed pixels in MODIS data using Bernstein basis functions

Yi Qin,Yupeng Ren,Feng Guo,Juan Gu,Lejun Zou,Xin Wang,Xiaohua Shen,Jingyu Ma

doi:10.1117/1.jrs.13.046509

Abstract

The decomposition of mixed pixels in Moderate Resolution Imaging Spectroradiometer (MODIS) images is essential for the application of MODIS data in many fields. Many existing methods for unmixing mixed pixels use principal component analysis to reduce the dimensionality of the image data and require the extraction of endmember spectra. We propose the pixel spectral unmixing index (PSUI) method for unmixing mixed pixels in MODIS images. In this method, a set of third-order Bernstein basis functions is applied to reduce the dimensionality of the image data and characterize the spectral curves of the mixed pixels in a MODIS image, and then the derived PSUIs (i.e., the coefficients of the basis functions) are calibrated by means of the abundance values of the ground features from the Landsat Enhanced Thematic Mapper Plus (ETM+)/Operational Land Imager (OLI) classification images corresponding to the date and region of the MODIS image. The proposed method was tested on MODIS and ETM+/OLI images, and it obtained satisfying unmixing results. We compared the PSUI method with conventional methods, including the pixel purity index, the N-finder algorithm, the sequential maximum angle convex cone, and vertex component analysis and found that the PSUI method outperformed the other four methods.

Highlights

Moderate Resolution Imaging Spectroradiometer (MODIS), as well as later-developed hyperspectral sensors have made great breakthroughs in spectral channel settings compared with earlier remote sensors
This method does not need to resort to extracting endmember spectra from MODIS data, which provides a new way of decomposing mixed pixels to assure the unmixing accuracy
In the pixel spectral unmixing index (PSUI) method, the spectral integral area that is in the range enclosed by the spectral reflectance curves of ground features and the x-axis and a set of third-order Bernstein basis functions are applied to characterize the spectral curves of mixed pixels in a MODIS image, and the derived PSUIs are used for representing the spectral characteristics of the mixed pixels

Summary

Introduction

Moderate Resolution Imaging Spectroradiometer (MODIS), as well as later-developed hyperspectral sensors have made great breakthroughs in spectral channel settings compared with earlier remote sensors. There are 36 discrete channels, including 20 reflective spectral channels, in a MODIS image, and each pixel of the image acquires many bands of light intensity data from the spectrum, instead of just the three bands of the RGB color model, which makes it possible to accurately depict the spectrum characteristics of typical ground features using the wavelengths, ranges, and intensities of the peaks and valleys and the integral area that is in the range enclosed by the spectral reflectance curves of the ground features and the x-axis (in Cartesian coordinates). The MODIS visits the globe once or twice per day with coarse resolution of 250 to 1000 m. The spatial resolution of MODIS images is not high enough to clearly distinguish different ground features. A MODIS pixel is a mixed pixel that is covered by multiple land cover types, which has a significant influence on the information that can be derived.[1,2] the decomposition of mixed pixels in MODIS images is critically important for the application of MODIS data in many fields, such as mapping land cover distributions,[3] evaluating vegetation/soil fractional cover,[4,5,6] monitoring and evaluating karst rocky desertification,[7] flood mapping,[8,9] and retrieving fire temperature and area.[10]

Methods

Findings

Discussion

Conclusion