Subset of your image that only includes the areas our are interested in. This saves disk space and processing time. Some software packages refer to this process as subsetting while others use the term clipping. Most raster data can be subset using XY coordinates, vector files or user created Regions of Interest (ROI). This process always creates a new dataset that only contains your data subset.
Minimum noise fraction (MNF) transformation is used to show the variation between bands in an image. This is a statistical method which works out differences in an image based on pixel DNs in various bands. MNF determines the inherent dimensionality of image data, to segregate noise in the data, and to reduce the computational requirements for subsequent processing. This step is often completed as a precursor to other types of analysis. Basically it is a way of simplifying the data. The MNF transform is essentially two principal component transformations. The first transformation, based on an estimated noise covariance matrix, decorrelates and rescales the noise in the data. 
This first step results in transformed data in which the noise has unit variance and no band-to-band correlations. The second step is a standard principal components transformation which creates several new bands containing the majority of the information. By using only the coherent portions, the noise is separated from the data, thus improving spectral processing results. Once applying MNF technique, on the 7 bands images TM (After being calibrated in reflectance mode), we will have like result 7 new bands images MNF. The image pixels are presented by eigenvalues. In examining the eigenvalues it can be seen that the first MNF bands ( 1 and 2) have the highest values while the remaining bands have consistent low values. It is the first two bands with the large values that contain most of the information and it is these bands that correspond to MNF images. The remaining low value bands (3 and under for example) are seen as noise. The images show the information compressed into only a few bands. The redundancy of the data is eliminated and noise is also removed. The result are more interpretable images. You could say that the data has been simplified or the dimensionality has been reduced.
Optimum Index Factor (OIF) is a statistic value that can be used to select the optimum combination of three bands in a satellite image with which you want to create a color composite. The optimum combination of bands out of all possible 3-band combinations is the one with the highest amount of 'information' (= highest sum of standard deviations), with the least amount of duplication (lowest correlation among band pairs). The limitation of the OIF calculation is that, the best combination for conveying the overall information in a large scene may not be the best combination for conveying the specific information desired by the image analysis. This from experience in most cases is reasonable and that also depends on the type of study. The aim of this study is to use OIF technique to rank all the possible three-band combinations with the best favorable for geological mapping of El-Beda Prospect.  Based on the results obtained from OIF, the combination 7, 2 and 1 shows the highest value of OIF with the first rank. This band combination has the most information with the least amount of duplication so that the boundaries between rock units and other geological features are very clear.
 J.W. , Boardman & F.A. , Kruse ; Thematic Coference on Geologic Remote Sensing, Environmetal Research Institute of Michigan, Ann Arbor, MI, I: 407-418; (1994); "Automated spectral analysis: A geologic example using AVIRIS data, noth Grapevine Mountais, Nevada".
 Ali M. Qaid and H.T. Basavarajappa ; American-Eurasian Journal of Scientific Research 3 (1): 84-91, 2008 ISSN 1818-6785"Application of Optimum Index Factor Technique to Landsat-7 Data for Geological Mapping of North East of Hajjah, Yemen".