A method implemented by one or more processors, comprising: identifying a first group of training examples, wherein each of the training examples of the first group includes: regular image training example input that is based on a corresponding regular image; and spectral image training example output that is based on a corresponding spectral image that corresponds to the regular image, wherein the spectral image training example output includes one or more non-visible channels that are not included in the regular image; training a first neural network portion based on the first group of training examples, the first neural network portion having a first portion input layer with a first shape that conforms to the regular image training example input and a first portion output layer with a second shape that conforms to the spectral training example output; identifying a second group of training examples, wherein each of the training examples of the second group includes: spectral image training example input that is based on a corresponding spectral image; and training example output that indicates one or more values for one or more features derivable from the corresponding spectral image, wherein the features indicate a plurality of classes and wherein the values for each of the training example outputs each indicate whether a corresponding one of the classes is present in a corresponding spectral training example input; training a second neural network portion based on the second group of training examples, the second neural network portion having a second portion input layer with the second shape that conforms to the first portion output layer and a second portion output layer with a third shape that conforms to the training example output; joining the first portion output layer of the first neural network portion with the second portion input layer of the second neural network portion to create a combined neural network model; applying a regular image as input to the first portion input layer of the combined neural network model; and generating an output based on applying the input, the generating being over both the first neural network portion and the second neural network portion of the combined neural network model. identifying a first group of training examples, wherein each of the training examples of the first group includes: regular image training example input that is based on a corresponding regular image; and spectral image training example output that is based on a corresponding spectral image that corresponds to the regular image, wherein the spectral image training example output includes one or more non-visible channels that are not included in the regular image; regular image training example input that is based on a corresponding regular image; and spectral image training example output that is based on a corresponding spectral image that corresponds to the regular image, wherein the spectral image training example output includes one or more non-visible channels that are not included in the regular image; training a first neural network portion based on the first group of training examples, the first neural network portion having a first portion input layer with a first shape that conforms to the regular image training example input and a first portion output layer with a second shape that conforms to the spectral training example output; identifying a second group of training examples, wherein each of the training examples of the second group includes: spectral image training example input that is based on a corresponding spectral image; and training example output that indicates one or more values for one or more features derivable from the corresponding spectral image, wherein the features indicate a plurality of classes and wherein the values for each of the training example outputs each indicate whether a corresponding one of the classes is present in a corresponding spectral training example input; spectral image training example input that is based on a corresponding spectral image; and training example output that indicates one or more values for one or more features derivable from the corresponding spectral image, wherein the features indicate a plurality of classes and wherein the values for each of the training example outputs each indicate whether a corresponding one of the classes is present in a corresponding spectral training example input; training a second neural network portion based on the second group of training examples, the second neural network portion having a second portion input layer with the second shape that conforms to the first portion output layer and a second portion output layer with a third shape that conforms to the training example output; joining the first portion output layer of the first neural network portion with the second portion input layer of the second neural network portion to create a combined neural network model; applying a regular image as input to the first portion input layer of the combined neural network model; and generating an output based on applying the input, the generating being over both the first neural network portion and the second neural network portion of the combined neural network model.