Visual Interpretation of EO Images

A few days back, some of my friends were having a discussion on responses recorded by remote sensing system and their visual interpretation for the extraction of meaningful information which gave me an idea to write on a very basic and fundamental concept of element of visual image interpretation.

Changing our Perspective

People are enough experts to elucidate images of objects. What they perceive in the image, they translate the things in the same way. After all, they have been doing this all their lives but with some set of instruction or direction can shape them an excellent image analyst or we can say image reading expert. They can sense and translate the information from the images whether it is aerial photograph or digital satellite image and extract the surface feature available in the image.

Image interpretation is defined as

The examination of images for the purpose of identifying objects and judging their significance (Philipson, 1997: McGlone, 2004: John R Jensen, 2007)

This article is aimed to introduce the fundamental concept of visual image interpretation acquired from remote sensing systems which may be obtained in different portions of the electromagnetic spectrum e.g. optical blue, green, red, and reflective near-infrared. These images may be acquired using different kinds of sensors comprising of traditional analog cameras, digital cameras and multispectral scanners (Landsat Series) and linear area array sensors systems (SPOT, IKONOS, QuickBird, OrbView-3, MODIS, IRS-1C, etc.). While interpreting such images one needs to understand key elements for visual interpretation of images so that the information associated with the image can be fully extracted and utilized for many purposes.

The purpose of image interpretation of remotely sensed images is to draw out qualitative and quantitative information from the photograph and satellite imagery consisting of various features/objects of the surface which may be artificial and natural. It depends on different responses (reflectance or emittance) the incident EMR radiation of their recording by a camera or sensors. Earlier, when digital images and computerized classification were not readily available, images were mostly examined by the visual interpretation, the precision level for the collected information rely on multiple things, such as:

  • Training
  • Experiences
  • Scale of Photograph
  • The geographic location of an area
  • Associated Maps
  • Ground Observation Data

The key elements of image interpretation are routinely used when visually interpreting satellite images and photographs. These elements and associated adjectives are shown in table 1:

Table1: Key Element of Image Interpretation

Sr.No.ElementCommon Adjectives (quantitative and qualitative)
1x,y location• x,y image coordinates: column (x) and row (y) coordinates in an unrectified image • x,y image map coordinates: silver halide crystals or pixels in photograph or image are rectified to a map projection (e.g., UTM)
2Tone/Color• gray tone: light (bright), intermediate (gray), dark (black) • color: IHS = intensity, hue (color), saturation; RGB = red, green, and blue; Munsell
3Size• length, width, perimeter, area (m2)
• small, medium (intermediate), large
4Shape• an object’s geometric characteristics: linear, curvilinear, circular, elliptical, radial, square, rectangular, triangular, hexagonal, pentagonal, star, amorphous, etc.
5Texture• characteristic placement and arrangement of repetitions of tone or color
• smooth, intermediate (medium), rough (coarse), mottled, stippled
6Pattern• the spatial arrangement of objects on the ground: systematic, unsystematic or random, linear, curvilinear, rectangular, circular, elliptical, parallel, centripetal, serrated, striated, braided
7Shadow• a silhouette caused by solar illumination from the side
8Height/Depth
Volume/Slope/
Aspect
• z-elevation (height), z-bathymetry (depth), volume (m3), slope °, aspect °
9Site
Situation
Association
• Site: elevation, slope, aspect, exposure, adjacency to water, transportation, utilities
• Situation: objects are placed in a particular order or orientation relative to one another
• Association: related phenomena are usually present

(Source: John R Jenson 2007)

The digital images containing a set of pixel elements having unique properties based on their responses in different channels of EMS (Electromagnetic Spectrum) at a certain geographic space. The interpretation of such properties based on the key elements of visual interpretation gives meaningful insights of the surface features. These elements have been arranged on the basis of their spatial arrangement and degree of complexity.

Degree of Complexity

Degree of Complexity

Figure 1: Key Elements

(Source: John R Jenson 2007)

Tone & Color: Earth Surface feature/object often reflects or emits different proportions of energy referred to as the spectral signature of the object in the electromagnetic spectrum. It is the measurement of the intensity of the reflected or emitted radiation of the surface object. The lower reflected object appears relatively dark and higher reflected objects appear bright. Responses recorded by remote sensing system display between pure black and pure white (Shades of gray) known as tone. One can differentiate approx. 40-50 shades of gray but it requires practice and skillsets to extract useful information from the images recorded in any multispectral channel. For example, rivers or waterbodies do not reflect in the NIR region, thus appear black and the vegetation reflects largely in NIR, thus appearing bright. In Multispectral images, optimal three bands are used to generate color composite images. Vegetation reflects much in NIR, therefore, in slandered FCC vegetation appears red, which is more suitable in vegetation identification.

Texture:  This element can be defined as the frequency of tonal variation in an image and mainly depends on the scale and resolution of the image. It is the characteristics placement and arrangement of repetition of tone and color in an image. The same reflected object may have a difference in texture helps in their identification. For example, in high-resolution image grasslands and tree crowns have a similar tone, but grassland will have smooth texture compared to a tree, smooth texture refers to less tonal variation and rough texture refers to an abrupt tonal variation in imagery.

Pattern:  This can be observed as a spatial arrangement of the features or objects which may be natural and artificial or arranged randomly and systematically on the landscape. Every Feature or objects has some kind of pattern which helps in their recognition. For example, if we want to analyze the temporal urban expansion pattern in a given geographical space. One can observe from the image that the city grows in radial or elongated form, similarly making for a classical example of the pattern of the fluvial system.

Size: It is one of the important elements and has the most distinguishable characteristics of an object that is size. Size has also dependency on scale or resolution of the image. A quick guess of object size can make a direct translation of feature. Measuring the size of an object allows the interpreter to draw many possible alternatives. This Measurement could be the length, width, perimeter, area, and volume (occasionally). For example, if one needs to distinguish based on their land-use types in geographical space. The size may be distinguishable characteristics between them e.g. large buildings such as factory or warehouse can be identified as a commercial property whereas smaller ones indicate residential properties.

Shape: This is an important consideration while interpretation of vertical or oblique imagery. The shape in property of object refers to the general form, its configuration, outline of feature which helps for recognizing features from an image. There are some examples of shape that can be seen in the remotely sensed images. Generally, regular shapes, square rectangles, circles are a sign of artificial object whereas irregular shapes are the sign of natural environment.

Shadow: This Element is helpful in image interpretation and give the real clue of an object’s identity and sometimes also create difficulties in identification. The element can be very useful to get the height of the object without any stereoscopic imagery. For example, the shadow of the building provides valuable information about the relative height of the building on the ground that may be a single-story building, etc. In Radar imagery, the shadow can be very useful for enhancing or identifying topography and landforms.

Height and Depth:  This is one of the most diagnostic keys for measuring the height and depth (bathymetry) of an object or landforms for visual image interpretation. For example, the stereoscopic instrument is helpful for visually translating the 3-D dimensionality of the surface and extracting meaningful information x, y, z (elevation/bathymetry) from two overlapping images sensed from two vintage point along the flight line.

Site, Situation, and Association: These are higher-order complex key elements and very important to identify the object of activity in the given image for visual image interpretation.

The site has unique characteristics of the landscape. These characteristics can be divided into physical and socio-economic aspects. For example,

  • Physical Characteristics include elevation, slope, direction(aspect), and surface cover.
  • Socioeconomic includes the value of the land, adjacency of particular surface features or social aspects.

Situation and association refer to the occurrence of the object in the geographic space that how a particular object in the image is structured and situated or located relative to one another. For example, in urban settings, smooth green space generally refers to grassland or playground, not agriculture land. These three elements are utilized with the combination of each other to draw a meaningful and logical conclusion and rarely used independently. For example, sewage disposal plants are mostly situated on flat sites adjacent to water sources so that the treated water can be disposed of and exist relatively producing community, etc.

Some Example or a glimpse of digital images related to the key elements of image interpretation:

Tone & Color

Tone & Color

Size

Size

Shape

Shape

Texture

Texture

Pattern

Pattern

Shadow

Shadow

Height & Depth

Height & Depth

Site, Situation and Association

Site, Situation and Association

(Source: John R Jenson, 2007)

Here, we got some examples of interpretation keys for forestry mapping. How some species appealing with respect to certain interpretation keys:

SpeciesCrown shapeEdge of CrownTonePatternTexture
 

Cedar

Conical with a sharp spearCircular and sharpDarkSpotted grainHard and coarse
CypressConical with a round crownCircular but not sharpDark but lighter than cedarSpottedHard and fine
PineCylindrical with shapeless crownCircular but unclearLight and unclearIrregularly spottedSoft but coarse
LarchConical with unclear crownCircular with the unclear edgeLighter than cypressSpottedSoft and fine
Fir/spruceConical with wide crownCircular with a zig-zag edgeDark and clearIrregularCoarse
DeciduousIrregular shapesUnclearLighterIrregularCoarse

(Source: Bhatta, 2008)

A good image interpreter or translator utilizes numerous elements while extracting information from the images without really thinking about them. Beginners need to understand all key elements for visual interpretation systematically and consciously with respect to digital image and evaluate the object and extract all meaningful information available in the scene effectively.

References:

John R. Jenson, 2007, Remote sensing of the environment, Second Edition, Pearson Prentice Hall.

Bhatta, B., 2008, Remote sensing and GIS, Oxford University Press, New Delhi

Lillesand, T.M. Kiefer, R.W., 2002, Remote sensing and image interpretation, Fourth edition, pp.192-193.

https://nature.berkeley.edu/~penggong/textbook/chapter7/html/sect71.htm

https://www.geospatialworld.net/article/image-interpretation-of-remote-sensing-data/

 

Leave A Comment

All fields marked with an asterisk (*) are required