2.1.1 STAGES INVOLVE IN PATTERN RECOGNITION
Preprocessing: One of the most common preprocessing steps done in field of pattern recognition are normalization to zero mean and unit variance, especially for 1-D datasets. In the field of remote sensing most common preprocessing step required is re-gridding, which is basically assigning a spatio-temporally uniform grid to raw data. In many image processing applications, it is desirable to have a uniform spatial grid for the pattern recognition process. However, satellite datasets usually have non-uniform grid, this problem can be rectified by re-sampling the spatial data by either interpolation or averaging to an uniform grid. Another common method used is spatial interpolation, as most of the datasets acquired are usually full of missing data points. The problem of missing data points is well known in statistics and this problem can be overcome by using a slew of techniques from simple averaging to advanced spectral analysis methods. (Wikipedia, 2015).
Feature Extraction: The main goal of feature extraction is to reduce the data dimensionality and properly represent the original data in feature space. Features useful for classification process can be simple features like RGB values in color images, or complex features like energies from the Fourier Transform or Wavelet Transform of a time series. The feature extraction process usually consists of three steps.
1) Feature construction is the step in which features are constructed from linear or non-linear combination of raw features.
2) Feature selection process is done using techniques like relevancy ranking of individual features and
3) feature reduction process is used to reduce the no. of features especially when too many features are selected compared to the no. of feature vectors. These three steps are not mandatory in the feature extraction process (Wikipedia, 2015).
2.2 DATA ACQUISITION
Data acquisition is the process of sampling signals that measure real world physical conditions and converting the resulting samples into digital numeric values that can be manipulated by a computer. Data acquisition systems, abbreviated by the acronyms DAS or DAQ, typically convert analog waveforms into digital values for processing. The components of data acquisition systems include:
• Sensors, to convert physical parameters to electrical signals.
• Signal conditioning circuitry, to convert sensor signals into a form that can be converted to digital values.
• Analog-to-digital converters, to convert conditioned sensor signals to digital values.
Digital Data Acquisition System Block Diagram
Data acquisition applications are usually controlled by software programs developed using various general purpose programming languages such as Assembly, BASIC, C, C++, C#, Fortran, Java, LabVIEW, Lisp, Pascal, etc. Stand-alone data acquisition systems are often called data loggers. (Wikipedia, 2018)
There are also open-source software packages providing all the necessary tools to acquire data from different hardware equipment. These tools come from the scientific community where complex experiment requires fast, flexible and adaptable software. Those packages are usually custom fit but more general DAQ package like the Maximum Integrated Data Acquisition System can be easily tailored and is used in several physics experiments. (Wikipedia, 2018)
2.3 DATA PROCESSING
Data processing is the conversion of data into usable and desired form. This conversion or “processing†is carried out using a predefined sequence of operations either manually or automatically. Most of the data processing is done by using computers and thus done automatically. The output or “processed†data can be obtained in different forms like image, graph, table, vector file, audio, charts or any other desired format depending on the software or method of data processing used. (Planning Tank, 2017)
