A New Spectral Unmixing Method Based on Derivative of Ratio Spectroscopy

نویسندگان

  • Zhao Hengqian
  • Zhang Lifu
  • Cen Yi
چکیده

The precise analysis of mineral abundance is a key content of hyperspectral technology research. In the present paper, a new spectral unmixing method based on derivative of ratio spectroscopy (DRS) was employed for visible to short-wave infrared (VIS–SWIR; 0.4–2.5 μm) reflectance data. The mixtures of plaster and allochite with different proportions were analyzed by DRS, Fully Constrained Least Squares (FCLS) and Non-Negative Least Squares (NNLS). A high precision was observed for DRS: for plaster, RMSE at the four bands are all less than 1.5%, and PCC are all higher than 0.999; for allochite, the precision is a bit lower, but the highest RMSE is still no more than 4.5%. Comparatively, the results for NNLS and FCLS are much worse than DRS. The result shows that this new spectral unmixing method is simple, of rigorous mathematical proof, and highly precise. It has a great potential in high-precision quantitative analysis of spectral mixture with fixed endmembers.

برای دانلود متن کامل این مقاله و بیش از 32 میلیون مقاله دیگر ابتدا ثبت نام کنید

ثبت نام

اگر عضو سایت هستید لطفا وارد حساب کاربری خود شوید

منابع مشابه

Land Cover Subpixel Change Detection using Hyperspectral Images Based on Spectral Unmixing and Post-processing

  The earth is continually being influenced by some actions such as flood, tornado and human artificial activities. This process causes the changes in land cover type. Thus, for optimal management of the use of resources, it is necessary to be aware of these changes. Today’s remote sensing plays key role in geology and environmental monitoring by its high resolution, wide covering and low cost...

متن کامل

تجزیه‌ ی تُنُک تصاویر ابرطیفی با استفاده از یک کتابخانه‌ ی طیفی هرس شده

Spectral unmixing of hyperspectral images is one of the most important research fields  in remote sensing. Recently, the direct use of spectral libraries in spectral unmixing is on increase. In this way  which is called sparse unmixing, we do not need an endmember extraction algorithm and the number determination of endmembers priori. Since spectral libraries usually contain highly correlated s...

متن کامل

جداسازی طیفی و مکانی تصاویر ابرطیفی با استفاده از Semi-NMF و تبدیل PCA

Unmixing of remote-sensing data using nonnegative matrix factorization has been considered recently. To improve performance, additional constraints are added to the cost function. The main challenge is to introduce constraints that lead to better results for unmixing. Correlation between bands of Hyperspectral images is the problem that is paid less attention to it in the unmixing algorithms. I...

متن کامل

New operational matrix for solving a class of optimal control problems with Jumarie’s modified Riemann-Liouville fractional derivative

In this paper, we apply spectral method based on the Bernstein polynomials for solving a class of optimal control problems with Jumarie’s modified Riemann-Liouville fractional derivative. In the first step, we introduce the dual basis and operational matrix of product based on the Bernstein basis. Then, we get the Bernstein operational matrix for the Jumarie’s modified Riemann-Liouville fractio...

متن کامل

An Overview of Nonlinear Spectral Unmixing Methods in the Processing of Hyperspectral Data

The hyperspectral imagery provides images in hundreds of spectral bands within different wavelength regions. This technology has increasingly applied in different fields of earth sciences, such as minerals exploration, environmental monitoring, agriculture, urban science, and planetary remote sensing. However, despite the ability of these data to detect surface features, the measured spectrum i...

متن کامل

ذخیره در منابع من


  با ذخیره ی این منبع در منابع من، دسترسی به آن را برای استفاده های بعدی آسان تر کنید

برای دانلود متن کامل این مقاله و بیش از 32 میلیون مقاله دیگر ابتدا ثبت نام کنید

ثبت نام

اگر عضو سایت هستید لطفا وارد حساب کاربری خود شوید

عنوان ژورنال:

دوره   شماره 

صفحات  -

تاریخ انتشار 2012