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! On Friday, March 6, 2015 at 12:53:42 PM UTC, Jordi Inglada wrote: > > [email protected] <javascript:> 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. > -- -- Check the OTB FAQ at http://www.orfeo-toolbox.org/FAQ.html You received this message because you are subscribed to the Google Groups "otb-users" group. To post to this group, send email to [email protected] To unsubscribe from this group, send email to [email protected] For more options, visit this group at http://groups.google.com/group/otb-users?hl=en --- You received this message because you are subscribed to the Google Groups "otb-users" group. To unsubscribe from this group and stop receiving emails from it, send an email to [email protected]. For more options, visit https://groups.google.com/d/optout.
