<p>Here is a <a href="https://arxiv.org/pdf/1904.10674.pdf" rel="nofollow noreferrer">survey on DL algorithms</a> used to classify hyperspectral datas. </p>

<p>Since you have datas or varying size, you will have to create patches of datas, you won't be able to feed datas of different sizes.</p>

<p>For example you could feed patches of <code>(16, 16, 220)</code> to your network. </p>

<p>I worked on a CNN with images of multispectral bands, I had less bands that you have, the size of patches was obviously important, I used a UNET in image segmentation.</p>

<p>Edit with an example using<code>(None, None, 220)</code> as input :</p>

<pre class="lang-py prettyprint-override"><code>model = Sequential()

# this applies 32 convolution filters of size 3x3 each.
model.add(Conv2D(32, (3, 3), activation='relu', input_shape=(None, None, 220)))
model.add(Conv2D(32, (3, 3), activation='relu'))
model.add(MaxPooling2D(pool_size=(2, 2)))
model.add(Dropout(0.25))

model.add(Conv2D(64, (3, 3), activation='relu'))
model.add(Conv2D(64, (3, 3), activation='relu'))
model.add(MaxPooling2D(pool_size=(2, 2)))
model.add(Dropout(0.25))

# model.add(Flatten())
# Replace flatten by GlobalPooling example :
model.add(GlobalMaxPooling2D()) 
model.add(Dense(256, activation='relu'))
model.add(Dropout(0.5))
model.add(Dense(10, activation='softmax'))

adam = Adam(lr=1e-4)
model.compile(loss='categorical_crossentropy', optimizer=adam)


</code></pre>
Training hyperspectral data using Tensorflow & Keras
<p>I am looking for an approach to train a hyperspectral image data on Tensorflow.
The training sample is encoded in CSV and has an arbitrary x-y dimension but constant depth:
The data looks like this:</p>

<p><em>Sample1.csv</em>: <strong>50x4x220</strong> (Row 1-50 is supposed to be aligned with row 51-100, 101-150, and 151-200)<br/>
<em>Sample2.csv</em>: <strong>18x71x220</strong> (Row 1-18 is supposed to be aligned with row 19-36, etc.)<br/>
<em>Sample3.csv</em>: <strong>33x41x220</strong> (same as above)<br/>
....<br/>
<em>Sample100.csv</em>: <strong>15x8x220</strong> (same as above)<br/></p>

<p>Is there any project example that I can use? Thanks in advance.</p>