Defining Subtle Features That Are Not Confined To Reflection Domains

Physical features such as tectonic structures, outcrop­ ing formations, surficial features and geochemical anomalies that are not confined to identifiable reflec­ tion domains often have their presence obscured by more prominent reflectance patterns. Many of the processing methods that tend to delegate closely related reflec­ tances to specific classifications, or to smooth out minor variations in the image, may discriminate against subtle outlines that indicate the presence of coherent but poorly defined physical features. The,,! e subtle anomalies, which may be apparent only through con­ tinuity of shape or texture, often are of major importance to the geologic interpretation of the area. However, because they may not respond to the usual enhancement techniques, special processing methods that improve the display of subtle features are necessary for their analysis. Spatial filtering, using operators that are designed to display features according to characteristics such as size and shape while rejecting conflicting features, can be an effective aid to interpretation. Edge detectors that display the boundaries of features have been used effectively in automatic pattern recogniza­ tion techniques and for the locating of subtle outlines and lineations. However edge detectors respond only to changes in slop,e and may not indicate continuity of the features within the outlines. Unless the body has a recognizable shape, it may be wrongly interpreted. Conversely, band pass filters display features based on their continuity of form or texture. Also, they can eliminate many of the conflicting larger or smaller features and bring out a singular expression of in­ dividual anomalies. A series of band pass filters, each designed to display features of a specific size, can be used to analyze an image feature-by-feature until all hidden anomalies have been discovered. Digital Fourier transforms provide an effective approach for the design and application of band-pass spatial filters. The portion of the scene that is to be analyzed is first transformed to the frequency domain by a two-dimensional fast Fourier transform program. The amplitude spectra permits the interpreter to note any dominate trends in the image and to select the frequency components for detailed examination. Filtering is accomplished by setting all unwanted components to. an amplitude of zero then returning the scene to the spatial domain by the inverse Fourier transform. If the retained frequency components are confined to a band with radial symmetry about the zero frequency origin, the filtered output will have zero phase characteristics and the output features will be in their correct position. In the same manner, any number of frequency bands can be transformed to display a wide range of distinctive features. Unfortunately, filtering does require a large number of mathematical operations; however, it can be performed quickly on modern high speed work stations and is ideally suited to parallel processing systems. Spatial filters can be applied to any digital image including those that have undergone considerable preliminary processing. Enhanced data such as the output from deconvolution or principal components ,analysis respond well to band pass filtering. Examples illustrate how secondary features that are hard to recognize in the original scene can be displayed prominently and correctly in the filtered output. Because the filters only retain features from the original data, all displayed anomalies can be located in the original scene.