Deep Learning application for spectral emissivity targets recognition

In this work, we use a simple deep learning model to classify a set of emissivity spectrum from samples into six different classes. We test the network for only one hidden layer, changing the hidden units. The results showed an accuracy of 1.0 after 200 iteration steps (epochs) for 100 hidden units. The novelty of the work resides in the fact that there is no precedent for it to be applied for the classification of emissivity values in the range of thermal infrared, being this range of the electromagnetic spectrum, so important for geological mapping and mineral exploration. Introduction Vibrational spectroscopy provides a means for studying geologic materials both in the laboratory and in remote sensing applications (Ruff et al. 1997). The study of minerals and rocks using this technique is based on the principle that molecules vibrate as they interact with propagating electromagnetic (EM) energy (Lane and Christiansen 1998). The fundamental frequencies of internal molecular vibration for most geologic materials occur in the mid-infrared range of the EM spectrum, and for this reason it is a very useful, non-invasive technique for characterization of materials for planetary exploration. In despite of this, it is not easy for the human eye to identify a material from its emissivity spectrum. In this sense, machine-learning techniques are becoming increasingly important. In particular, deep learning has proven to be both a major breakthrough and an extremely powerful tool in many fields(Zhu et al. 2017) Remote-sensing scientists have exploited the power of deep learning to tackle different challenges and instigated a new wave of promising research. Deep learning involves a class of models which try to hierarchically learn deep features of input data. The network is first layer-wise initialized via unsupervised training and then tuned in a supervised manner. In this scheme, high-level features can be learned from low-level ones, whereas the proper features can be formulated for pattern classification in the end (CHEN et al.,2014). The most commonly-used neural network model for classification in remote sensing is the multi-layer perceptron trained by the backpropagation algorithm (Rumelhart et al. 1986) . Given a dataset of shape (n,m), where n is the number of data samples and m is the length of each data sample. The basic goal of a neural network is to learn a function from a set of fixed-size inputs X = {x1,x2,. ,xi,. ,xn}, xi є Rm, to a fixed-size output y = {y1,y2,. ,ym}, yi є Rm . The network consists of the input layer (Figure 1), one or more intermediate or hidden layers consisting of a set of units or nodes, and an output layer. In a feed-forward neural network, the layers are ordered and each unit of a layer connects to some subset of the units of previous layers. A layer is dense if each of its units connects to all of the units in the previous layer. A network is deep if there are several hidden layers. Each connection between units in adjacent layers has a weight and each unit has a bias. Method During the training, after computing the weighted sum of the inputs, an activation function is calculated. These two steps can be formulated as a =f (wa+b ) (1) where the wl is the weight matrix for each layer, initialized with random values, and bl is the bias vector for each layer, initialized with zeros. The activation function of a node defines the output of that node, This output is then used as input for the next node and so on. Figure 1. Diagram of the proposed neural network for the spectrum recognition (The hidden layer units are reduced for simplicity) The nonlinear functions f(.) are generally chosen to be sigmoidal functions such as the logistic sigmoid or the ‘tanh’ function (Bishop, 2006). Sixteenth International Congress of the Brazilian Geophysical Society Deep Learning application for spectral emissivity targets recognition ______________________________________________________________________________________________________________________________________________________________________________________________________________________________________________________________________________________________________________________________________________________________________________________________________________________________________________________________________________________________________________________