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! > > > 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. >> > -- -- 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.
