How to automatically correct the radiometry of optical Earth Observation images?
Multi-temporal airborne and satellite multispectral/hyperspectral images are often used for monitoring the environment and detecting land cover changes. The different atmospheric and illumination conditions, sensor performance and viewing angles result into differences in the reflectance values of the same objects in multi-temporal images.
Relative radiometric normalization (RRN) approaches aim to adjust the radiometry of the images included in a temporal sequence (subject images) to that of a reference image. Temporal invariant objects presented in the multi-temporal set of images should have the same spectral signatures after the normalization.
The concept of RRN is not new and many algorithms have been proposed. These algorithms can be clustered into two basic categories: the manual and the automatic algorithms. Manual algorithms require user’s involvement during the process, making the RRN time consuming and subjective. On the contrary automatic RRN algorithms minimize or even exclude user’s involvement. In both categories two common steps are identified: (a) the selection of Invariant Pixels (IPs) or Pseudo-invariant Features (PIFs) and (b) the regression function calculation. The way that IPs are selected, i.e. manually or automatically, characterizes the category of the algorithm.
Manual RRN category includes several algorithms. For example, seven empirical linear normalization techniques: the Dark Set — Bright Set (DB) normalization, the pseudo-invariant feature (PI) normalization, the no-change set (NC) regression normalization, the Haze Correction (HC), the minimum-maximum (MM) normalization, the mean-standard deviation (MS) normalization, and the simple regression (SR) normalization are tested and compared.
A relative radiometric normalization algorithm can be based on the normalized difference vegetation index (NVDI) is developed. This algorithm is a variation of the PIFs, it is called temporally invariant cluster (TIC). In order to select the IPs or the TIC, a scatter plot is formed with x and y axis containing the values of the NVDI of the subject (x) and reference (y) image. From this scatter plot a density map is calculated. According to their assumptions, the TIC should be distinct areas of the scatter plot. The centres of the TICs are selected as invariant pixels and are used to calculate the regression lines. Lack of automation is mainly due to the final selection of TICs. It is based upon observation of the point density map in conjunction with visual examination of the original images to confirm that those pixels are truly spectrally invariant.
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