DNN-Based, Semantic Extraction: Fast Learning from Multispectral Signatures

Abstract

In this paper, we present three methods that reduce the computational time of training Deep Neural Networks with multispectral images, optimize the resource occupation of the dataset, and obtain high performance for reduced datasets. In the first two methods, we reduce the dimension of the input data with either histograms of pixel intensity or Bag-of-Words. Then we train a Convolutional Neural Network with either histograms or Bag-of-Words and we achieve an accelerated training. Moreover, storing the image patches from the dataset in the form of histograms or Bagof-Words reduced the memory storage significantly. In the last method, we subsample the training dataset randomly to 50%, 20% and 10% of the original dataset, thus training a Convolutional Neural Network on a smaller number of samples (in the form of histograms or Bag-of-Words), and the classification performance is almost unaffected. This is an important achievement, as there are few labelled datasets for Earth Observation and the number of images in these datasets is small. Our results show that the training time is reduced by a maximum of 387 times and the datasets with histograms or Bag-of-Words occupy 633 times less space.