How to remove 'silk braid' noise in satellite data?
You ask, How to remove these abnormal value for more precise data?

The short answer is that you might be able to make your data look more representative of the natural 'truth', but you will probably have to pay for this with some precision. The problem is that the measurement itself is imperfect: this looks like an artifact of the data collection method. The general name for unwanted energy that interferes with a signal you are interested in is noise.

In your case, there is some random noise, and there is this coherent (i.e. spatially organized) noise. It is likely impossible to substantially attack either type of noise without touching the signal. In other words, you will probably have to pay for the noise reduction with some loss of precision. This may be a trade-off you are willing to make.

Reducing noise

There are many, many ways to reduce noise in data. A very common way to reduce random noise is to apply a smoothing filter such as a simple mean in some small, moving window (often expressed as a convolution operation). The problem with this is that it just mixes the noise into the image.

You can also think of a smoothing high-cut filter in the spatial frequency (aka wavenumber) domain. A common approach to removing the sort of spatially organized noise you have is to use a 'notch' (narrow) cut filter in this domain, which has the effect of removing noise with that spatial frequency. In other words, you can remove stripes.

Filters that remove stripes can be found in various image processing software, or could be implemented with various scientific computing languages (e.g. MATLAB's Image Processing Toolbox or scikit-image for Python). Here are some options:


FFT in ImageJ/FIJI — I would start here, unless you're already into MATLAB or Python.
Destripe in GIMP — another good option.
Destriping in ISIS — an image processing package from USGS.