## 2 Non Destructive Sensing
### Algorithms  
The use of algorithms began in the 1970s with least squares. From \(1981-1985\), the usage of RLS and Entropy began. Maximum Likelihood and Least squares were popular during \(1986-1990\). Maximum Likelihood, Least squares and Entropy were popular during \(1991-1995\)[117]. There was a drastic change in the usage of algorithms from \(1996-2000\) and this might be due to the introduction of hyperspectral imagery. Maximum Likelihood still seems to be popular, followed by Least squares and Entropy. The usage of principle component analysis (PCA) and Genetic algorithms has increased. From 2001 to the present year, the usage of Least squares has decreased compared to previous years, but Maximum Likelihood and Entropy are still widely used. Partial least square regression is a multivariate statistical method for establishing models combining the features of principle component analysis and multiple regressions [118]. The decomposition of data is similar to the principle component analysis with an additional advantage of data reduction on both spectral and concentration data. When the spectral data is processed using the PLS algorithm two eigen-vectors are formed representing variation in spectral data and changes in spectra due to variations in concentration. Linear discriminant analysis (LDA) is a method of predicting a classification based on known values of the variables. LDA is a statistical, supervised method used for dimensionality reduction and feature optimization. The technique is based on how close a set of measurement variables are to the multivariate means of the levels being predicted [119,120]. Based on the discriminant analysis of the training data set, the Mahalanobis distance to each class cluster is computed. Based on this distance a probability can be calculated providing the likelihood that the sample is classified with a class label. The contribution of individual variables to the accuracy of prediction was assessed using the magnitude of the ratio of variances between consecutive stepwise additions to the model (F-ratio statistic). Principle component analysis is a multivariate statistical method to reduce the large number of original variables into some linear combinations of transformed variables [121]. These transformed variables represent the constituents from the spectral information and represent the largest variations among the values of the spectral data. An Artificial Neural network (ANN) is an information processing concept constructed by a large number of individual, locally connected processing elements or units called neurons similar to the biological nervous system or human brain [120,122,123]. These neurons in the network sum up the results of the respective input connections, weight them and transform the weighted sum by a non linear function of variables. ANN is applied in many complex real-world problems like pattern recognition, forecasting and data classification. The use of ANN in the field of image processing, remote sensing and weather forecasting is increasing rapidly because of the ability of ANN to handle large volume of complex data for processing and classification. Remote sensing uses these algorithms but they have to be optimized due to the high quantity of data acquired. Several types of data are available, but most use images which could be visible, NIR, infrared, ultraviolet or radar. Image classification is defined as the process of creating thematic maps from satellite imagery [124]. Image classification classifies each pixel of an image into land cover categories. In the case of crisp or _hard classification_, each pixel is assigned to only one class. However, in fuzzy or _soft classification_, a pixel is associated with many land cover classes. In general, classification techniques may be supervised or unsupervised. Supervised classification procedures tend to require considerable interaction with the analyst, who must guide the classification by identifying areas on the image which are known to belong to each category of interest [125].