Jordi,

Thanks very much. I get it.

Best wishes.

Wei


2015-03-09 12:52 GMT+00:00 Jordi Inglada <[email protected]>:

> [email protected] wrote:
> >
> > Hello,
> >
> > Does anyone could confirms it ?
> >
> > On Friday, March 6, 2015 at 3:35:05 PM UTC, [email protected] wrote:
> >
> >     Thanks, Jordi,
> >
> >     Now I am running the CloudDetectionExample, and my goal is to
> >     adapt this method to detect the cloud automatically, which is mean
> >     that i should combine the classification method and spectral angle
> >     together.
> >
> >     My first problem here is that: what exactly the referencePixel
> >     mean?
> >     I have found one reply from a discussion several months ago which
> >     said:"pixel components parameters are a pixel value in the input
> >     image taken on a cloud which is used as a reference for spectral
> >     angle computation".So whether the "a pixel value" is the DN
> >     (Digital Number) Value of the specified pixel? Just because there
> >     are 4 bands, so the values have 4 components, Is that right?
> >
> >     If the image only have 3 bands (Like RGB colorful images, and such
> >     kind of images are more normal and available that tiff with four
> >     bands), So I only need to provide 3 components of a cloud pixel,
> >     Is that right?
> >
> >     Best wishes!
>
> Hi,
>
> The reference pixel is the one with which every pixel in the image would
> be compared using the spectral angle.
>
> If you use the example in OTB, it assumes that the input image has 4 bands
> and therefore it takes 4 values as spectral bands.
>
>
>   VectorPixelType referencePixel;
>   referencePixel.SetSize(4);
>   referencePixel.Fill(0.);
>   referencePixel[0] = (atof(argv[5]));
>   referencePixel[1] = (atof(argv[6]));
>   referencePixel[2] = (atof(argv[7]));
>   referencePixel[3] = (atof(argv[8]));
>   cloudDetection->SetReferencePixel(referencePixel);
>
> You can modify the example to use as many bands as your input image has.
>
> Jordi
>
> >
> >     On Friday, March 6, 2015 at 12:53:42 PM UTC, Jordi Inglada wrote:
> >
> >         [email protected] wrote:
> >         >
> >         > Thanks very much for your reply,Jordi.
> >         >
> >         > I have read the first paper, it need cloud and cloud shadow
> >         match, and
> >         > the processing was more complicated than OTB.
> >         >
> >         > As refer to OTB cloud detection method,I have another
> >         question:the OTB
> >         > cloud-detect method need specify a pixel which possibly is
> >         cloud, that
> >         > means it can not recognize cloud automatically.If someone
> >         adopt the
> >         > OTB cloud detection method to process large amount of images
> >         or run
> >         > real-time on-board the remote sensing satellite, Obviously,
> >         one can
> >         > not pick a cloud pixel for the OTB program.So my question
> >         is:
> >         >
> >         > Does there any techniques to determine the reference cloud
> >         spectral
> >         > angle automatically? Or could I just preset the reference
> >         cloud
> >         > spectral angle?
> >         >
> >         > I know that some methods such as SVM could train the cloud
> >         spectral
> >         > angle classifier which could act as a reference,Any other
> >         > suggestion??
> >         >
> >
> >         Hi,
> >
> >         I simple approach for which I have seen good results is indeed
> >         supervised classification. If you can generate a set of
> >         training samples (small polygons) for the cloud and for the
> >         non-cloud classes, you can use either SVM or Random Forests to
> >         implement your cloud screening procedure.
> >
> >         In this case, you don't even have to code in C++ and you can
> >         use the OTB applications. Have a look here:
> >
> https://www.orfeo-toolbox.org/CookBook/CookBookse14.html#x66-1010004.4
> >
> >
> >         Good luck.
> >
> >         Jordi
> >
> >         > Thanks again!
> >         >
> >         > On Thursday, March 5, 2015 at 12:37:58 PM UTC, Jordi Inglada
> >         wrote:
> >         >
> >         > [email protected] wrote:
> >         > >
> >         > > Hello guys,
> >         > >
> >         > > Now I am a beginner to use the OTB and I have some
> >         problems when
> >         > using
> >         > > the Cloud Detection Example.
> >         > >
> >         > > As far as I know that there are several techniques to
> >         detect
> >         > cloud
> >         > > from the remote sensing imagery. In the OTB, it detects
> >         the
> >         > cloud
> >         > > based on spectral angle principle and assume that the
> >         image have
> >         > four
> >         > > spectral bands. In my mind the parameter setting should
> >         affect
> >         > the
> >         > > detection result, and the parameters must set according to
> >         > Sensor that
> >         > > the camera adopted.But not every cameras have four
> >         spectral
> >         > bands.
> >         > >
> >         > > So My questions are:
> >         > >
> >         > > Does there any papers about the OTB Cloud Detection
> >         method?
> >         > >
> >         > > Does it work well using only three spectral band?
> >         > >
> >         > > What is the basic principle to set the parameters?
> >         > >
> >         > > Anyone any suggestion is welcome!
> >         > >
> >         > > Thanks very much.
> >         > >
> >         > > --
> >         >
> >         > Hi,
> >         >
> >         > You are right about the fact that there are many methods for
> >         the
> >         > detection of clouds. The spectral method proposed in OTB is
> >         a very
> >         > simple one (spectral angle and low-pass filtering before
> >         > thresholding), but has the advantage of not needing any
> >         particular
> >         > spectral band (SWIR or thermal, for instance).
> >         >
> >         > If you want more sophisticated approaches, you should try to
> >         > implement something inspired from Fmask[1] for single-date
> >         > acquisitions or from MACCS[2] for multi-temporal series.
> >         >
> >         > Jordi
> >         >
> >         > [1] Zhu, Zhe, and Curtis E. Woodcock. "Object-based cloud
> >         and
> >         > cloud shadow detection in Landsat imagery." Remote Sensing
> >         of
> >         > Environment 118 (2012): 83-94.
> >         >
> >         > [2] Hagolle, Olivier, et al. "A multi-temporal method for
> >         cloud
> >         > detection, applied to FORMOSAT-2, VENµS, LANDSAT and
> >         SENTINEL-2
> >         > images." Remote Sensing of Environment 114.8 (2010):
> >         1747-1755.
>

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