Steps in Remote Sensing from Image to Remote Sensing Data (Especially for GATE-Geospatial 2022)

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Digital Image

In a most generalized way, a digital image is an array of numbers depicting spatial distribution of certain field parameters, such as reflectivity of EM radiation, emissivity, temperature or some geophysical or topographical elevation. All computer information is stored as bits, representing a 0 or a 1. We use combinations of bits to store more information, and the information increases exponentially the more bits we use. 1 Byte is 8 bits so we can store 28 or 256 values. Most of the data we use is “8 bits” . Unit of spatial image data is a Pixel and represents the data from all spectral bands at one spatial location. The value for the pixel is called a digital number or “DN” The size of this area affects the reproduction of details within the scene. As the pixel size is reduced more scene detail is preserved in the digital representation. Our eyes can see many different colours. But we can usually only identify 100 different colours on a page or screen and see fewer than 32 gray shades.

Remote Sensing Data

Remote sensing images are recorded in digital forms and then processed by the computers to produce images for interpretation purposes. Images are available in two forms -photographic film form and digital form. Variations in the scene characteristics are represented as variations in brightness on photographic films. A part of the scene reflecting more energy will appear bright while a different part of the same scene that reflecting less energy will appear black.

Relationship between Digital Image Processing to Remotely Sensed Data

To understand the relationship of digital image processing to remotely sensed data, one should have a clear concept of the steps involved in the remote sensing process. These steps are illustrated in below Figure.

- Relationship to Remotely Sensed Data

Steps in Remote Sensing

The first step in remote sensing, as in any scientific study, is the definition of a problem. Due to its multidisciplinary nature, the problems that remote sensing can be applied to are numerous and diverse. In spite of this, the approaches to remote sensing can be categorized as being either scientific in nature or technological in nature. The distinction is primarily a function of the motive behind solving the problem. Scientific approaches are driven primarily by “curiosity or whim” (Curran, 1987) while technological approaches are driven by a human, need. The methodology that is subsequently applied to the problem is usually dependent upon the origin of our problem. There are three basic types of logic that can be applied to a problem; inductive, deductive, and technologic. Scientific approaches use both inductive logic and deductive logic methodologies, while a technological approach uses a technologic logic methodology. The steps in each of these logic methodologies can be seen in below Figure.

- Logic Used in Remote Sensing
  • Inductive Logic & Image Interpretation: Inductive logic could be described as learning logic. The inductive methodology seeks to form tenable theories by making observations of phenomena, classifying these observations and generalizing that form the basis of theories. Most people use inductive logic every day. For example, a person slips and falls on the greasy road which spilled due to leaking tanker passed on the road. They would see that when the road is greasy it becomes slippery and there is a subsequent loss of traction. This observation can then be generalized to a theory that all roads, when greasy, provide less traction than when dry.

This type of logic is at the centre of remote sensing when the focus is image interpretation. Like our everyday learning experiences, a researcher using this logic observes facts about remotely sensed data and seeks to form general theories or principles that can be applied to other remotely sensed data (Curran, 1987) . Theories formed from this inductive approach are often fed directly into a deductive methodology where hypotheses are developed for testing the theories.

  • Deductive Logic: The focus of deductive logic is the formulation of theories and the subsequent testing of hypotheses. Once a problem is identified, a researcher conjectures a theory to solve it. To determine the validity of any such theory, hypotheses are developed and tested. The hypotheses are at the core of the deductive logic. Because of their importance, great care should be taken to formulate a hypothesis that is appropriate to the problem at hand. Two of the most common types of hypotheses are factual and inferential. A factual hypothesis clearly states a position that can be either verified or falsified. (e. g. There is a canal that passes through field X or with field Y.) It is possible to verify this hypothesis as either true or false. An inferential hypothesis is one which can be falsified. ′ Observations that fail to disprove the hypothesis do not necessarily prove its truthfulness. However, a failure to disprove the hypothesis generally results in the acceptance of the theory being tested with the knowledge that future observations may later reverse that decision.
  • Technological Approach: A technological approach differs from both the inductive and deductive in both its origin and its goal. The basis of this approach is a human need rather than scientific inquiry. The goal is the rectification of that need rather than simply an increase in knowledge. The focus of a technological methodology is the design of coherent plan which successfully blends “inputs from science, economics, aesthetics, law, logistics and other areas of human endeavour” (Curran, 1987) . Once a plan of action has been designed it is implemented without a formal hypothesis being stated.

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