Convolving Across Channels in Keras CNN: Conv1D, Depthwise Separable Conv, CCCP?

I am developing a CNN in keras to classify satellite imagery that has 10 spectral bands. I'm getting decent accuracy with the network below (~60% val accuracy across 15 classes) but I want to better incorporate the relationships between spectral bands at a single pixel which can yield a lot of information on the pixel's class. I see a lot of papers doing this but it is often called different things. For example:

• Cascaded cross-channel parametric pooling
• Conv1D
• Depthwise Separable Convolution
• Conv2D(num_filters, (1, 1))

And I'm not certain about the differences between these approaches (if there are any) and how I should implement this in my simple CNN below. I'm also not clear if I should do this at the very beginning or towards the end. I'm inclined to do it right at the start when the channels are still the raw spectral data rather than the feature maps.

input_shape = (32,32,10)
num_classes = 15

model = Sequential()
model.add(Conv2D(32, (3, 3), padding='same', input_shape=input_shape))
model.add(Activation('relu'))

model.add(Conv2D(32, (3, 3)))
model.add(Activation('relu'))
model.add(MaxPooling2D(pool_size=(2, 2)))
model.add(Dropout(0.25))

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

model.add(Flatten())
model.add(Dense(256))
model.add(Activation('relu'))
model.add(Dropout(0.5))
model.add(Dense(num_classes))
model.add(Activation('softmax'))

from Convolving Across Channels in Keras CNN: Conv1D, Depthwise Separable Conv, CCCP?