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How to apply Matlab CNN code on an input image with 6 channels

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I have currently applied the Matbal CNN function to train my research data. Unlike, the Matlab 'lettersTrainSet'with a size of 28x28x1x1500 (4-D array), the input train data of my experiment have a size of 7x7x6x30,000. The problem I have encountered is that while running the 'trainNetwork' function, Matlab shows me an error: *Error using trainNetwork>iAssertValidImageArray (line 575) X must be a 4-D array of images.
Error in trainNetwork>iParseInput (line 329) iAssertValidImageArray( X );
Error in trainNetwork (line 68) [layers, opts, X, Y] = iParseInput(varargin{:});*
However, the same training data with 3 channels or 1 channels I can run the CNN code without any error message. It will be a great help if anyone can suggest how to use image data with more than 3 channels in Matlab for CNN classification.

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Armin Eskandari
Armin Eskandari on 27 Oct 2017
Hi, all I have the same problem, Please remove this limitation

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Accepted Answer

Kristen Amaddio
Kristen Amaddio on 27 Jul 2017
Currently, CNN exclusively supports single and RGB channel imagery. Due to this limitation, the ability to use CNNs with image data with more than 3 channels is not available at this time.
I work at MathWorks, so I have forwarded this feedback to the relevant development team.

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Greg Heath
Greg Heath on 13 Jun 2018
The purposes of PCA and PLS are to reduce dimensionality subject to the constraints of minimizing the loss of regression information (PCA) or classification information(PLS).
It may definitely be worth doing before trying to preserve the dimensionality.
Hope this helps
Greg
GAMPA V P CHANDRA SEKHAR YADAV
I have currently applied the Matbal CNN function to train my research data. Unlike, the Matlab 'lettersTrainSet'with a size of 28x28x1x1500 (4-D array), the input train data of my experiment have a size of 7x7x2500. The problem I have encountered is that while running the 'trainNetwork' function, Matlab shows me an error: *Error using trainNetwork>iAssertValidImageArray (line 575) X must be a 4-D array of images.
Error in trainNetwork>iParseInput (line 329) iAssertValidImageArray( X );
Error in trainNetwork (line 68) [layers, opts, X, Y] = iParseInput(varargin{:});*
please help me in this regard

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More Answers (4)

jim peyton
jim peyton on 1 Nov 2017
Edited: jim peyton on 1 Nov 2017
If the development team is prioritizing by market need, this is a deal-breaker for a few of our applications too:
Using XYZRGB (6ch), or XYZ+Gray(4ch), or XYZ+normals+gray(7ch), or two stereo channels with multiple exposures/textures each (up to 24ch)...

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Chandrama Sarker
Chandrama Sarker on 1 Nov 2017
Yes, I agree with you, Jim, that is why I have to shift from Matlab to Python in order to utilize the information from all the 6 channels of the image. In some cases specifically in the field of remote sensing, the number of channels would never be limited to 3 channel data and it may be higher than 6 channels too. Regards

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Carole
Carole on 21 Feb 2018
This is the same for me. I wanted to implement a deconvolutional neural network and thus meed to have an input layer with more than 3 channels (to input the feature map and also to modify them as all needed layers for this are not yet implemented). Is there any workaround, or will this fixed in the next release? I will have to switch to Python otherwise. Is it in the plans of the development team? Cheers.

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Hang-Rai Kim
Hang-Rai Kim on 17 Apr 2018
I want to apply CNN in 3D images (MRI data). I am planning to use 3D images as 2D x z stacks thus need to work in 2D CNN with multi channels. Please let me know what should i do.. Thank you.

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