you can't remove the cloud from satellite image. maybe, you can choose other one without cloud

Removing cloud is not easy. Atmospheric haze of any sort influences or blocks radiation signal. You can use any one of these, 

• use modules from commercial (in Erdas Imagine you have ATCOR, ENVI has ACM) or non-commercial (fmask) softwares to mask out the cloud
• try spectral mixing/ ratioing to optimize shadow effects over other features
• try secondary sources for those areas. 

You may try fmask available from Google Code. It can mask not only clouds but cloud shadows too. It does not work perfectly, but it is easy to use.

There are several threads on researchgate discussing this topic in detail.

Best to get another image to replace cloudy one if possible. Masking may remove cloud but you still don't have clean data...

you can't remove the cloud from satellite image. maybe, you can choose other one without cloud

Fmask does a fair job masking out cloud and cloud shadows

you can use Radiometric correction tools in ERDAS software. 

As a first approach you can identify clouds, e.g. the Landsat TM/ETM+ Automatic Cloud Cover Assessment (ACCA). An implementation you find here: http://grass.osgeo.org/grass70/manuals/i.landsat.acca.html i.landsat.acca

Hello, Imrana Aliyu!

As was told, you can try FMASK (not the best option, but). YOu can download it here

https://code.google.com/p/fmask/

Info about it here http://www.sciencedirect.com/science/article/pii/S0034425711003853

If you aren't interested in aditional postprocessing the data, you just can download Landsat surface reflectance product from http://earthexplorer.usgs.gov/ (registration needed, but it's free). Almost all the Landsat data (Landsat 4/5, 7 ETM+ slc -on/-off, Landsat 8 OLI/TIRS) can be requested and downloaded as sr product in *.hdf format. it includes *TIFF bands with clouds, cloud shadows and some buffer called adjusted clouds. More about this you can find here http://landsat.usgs.gov/CDR_LSR.php

Best regards,

Ruslan

Thanks to all for your contribution, I am glad indeed.

Dear Imrana Aliyu,

You can try to use FMask softweare ( https://code.google.com/p/fmask )

Be sure, it's not ideal, but it can provide acceptable results for most cases.

 possible to remove cloud from Landsat 8 OLI images by band 9

Here a Landsat 8 example using the QA band and the gdal utilities.

# create a mask of the clouds (cloud.tif) where 0 is No-Cloud and 1 is Cloud

gdal_calc.py -A LC08_L1GT_233067_20170322_20170329_01_T2_pixel_qa.tif --type=Int16 --outfile=cloud.tif --calc="((numpy.bitwise_and(A, 40) != 0) * 1).astype(int16)"

# then use the mask to filter the Landsat bands

gdal_calc.py -A LC08_L1GT_233067_20170322_20170329_01_T2_sr_band4.tif -B cloud.tif --type=Int16 --outfile=masked.tif --calc="(numpy.where(B, 0, A)).astype(int16)" --NoDataValue=0

Thanks to Rolf Simoes

I do agree with @Alber_Sanchez, he is right about the method he recommends. I've used it sometime and it has worked for me.

I could recommend you some example where I worked with MSG SEVIRI (Meteosat Second Generation Spinning Enhanced Visible and InfraRed Imager) data and processed it with Bilko (UNESCO-ESA open source software, available at http://www.learn-eo.org/software.php).

In order to understand how clouds are detected and interpreted in satellite imagery, have a look at Alonso et al. (2013) "Prediction of cloudiness in short time periods using techniques of remote sensing and image processing" (see attached), as well as J. Schmid's "The SEVIRI instrument".

Article The SEVIRI instrument

First, need to calculate bright temperature , build mask according to temperature of the cloud area and then mask out using the cloud area, Also can read my paper.

Has anyone once used envi（v5.3.1） the calculate cloud mask using fmask algorithm to recognize the cloud area of landsat8? I have done some tests, but the output mask was very strange. It makes the whole image range into one cloud mask. It really confused me, because even if I have set the cloud threshold 90%,the output mask still covers the whole image.

Dear Alyiu

You can use QGIS SCP plugin from Congedo, Luca for this task:

"Before the land cover change assessment, we need to remove cloud cover pixels in the image acquired on 2017-02-10. Of course we could perform the same process for all the other images."

See more details at this link: https://semiautomaticclassificationmanual.readthedocs.io/en/latest/tutorial_2.html#id20

Regards

You can used ENVI Software to Correction the image (FLAASH Tool)

If the percentage of clouds is high you can choose other one without cloud. Or can used ENVI Software to Correction the image (FLAASH Tool)

Is there anyone found this message when running FMASK: "Error: Index exceeds matrix dimensions."

we can't remove the clouds from satellite images, so its better to skip the image and choose another one. neither radiometric correction and nor atmospheric correction could remove the clouds. I had the same problem but simply I skipped and choose another image.

you can not remove the cloud from satellite image. maybe, you can choose other one without cloud, or within an acceptable percentage

While downloading data from usgs earth explorer we have option to choose for minimum cloud cover.