Image interpretation I and II
Looking at satellite image, identifying different objects, according to scale and associated information and to communicate this information to others is what we call as IMAGE INTERPRETATION (Lillesand & Kiefer, 2004)
Fundamentals of Image Interpretation Level of Interpretation may vary from simple to complex Interpretation complexity varies with the type of subjected landcovers Interpretation success depends upon the quality of satellite data (Spatial, Spectral and Radiometric Resolutions) Success of Image interpretation depends on the experience of interpreter
Key to Visually Interpret a Satellite Image Name of satellite (LANDSAT, SPOT, IKONOS etc) Type (Spectral Mode) of satellite data (Panchromatic, Multispectral etc) Band combination used (432, 742, 541 etc) Satellite image acquisition date and ancillary information about the area
Colour Tone Texture Pattern Shape Shadow Association Resolution Scale Elements of Image Interpretation
Colour Color display of remote-sensing data is of importance for effective visual interpretation. There are two color display methods: color composite, to generate color with multi-band data, and pseudo-color display, to assign different colors to the grey scale of a single image.
A Color Composite image can be generated by composing three selected single-band images with the use of three primary colors i.e. RGB Different color images may be obtained depending upon the selection of three band images and the assignment of the three primary colors.
True Colour Composites Colour Display Types Natural colour composites render features similarly as the human eyes sees them. They can be prepared by using Landsat TM bands 3 (red), 2 (green), and 1 (blue) for the RGB primary colours. The advantage is that they are easy to understand also for laymen, the disadvantage is that the blue band is strongly affected by atmospheric haze and is not available from most sensors.
False Colour Composites In addition, invisible regions, such as infrared, are often used, which need to be displayed in colour. As a colour composite within an infrared band is no longer natural colour, it is called a false colour composite (FCC). In particular, the colour composite with the assignment of blue to the green band, green to the red band, and red to the near infrared band is very popular.
Forming a False Color Composite Image (FCC) TM Band-2 (Green) Blue TM Band-3 Red Green TM Band-4 (IR) Landsat-5, False Color Composite (FCC) Red
Tone Relative brightness / Darkness in the image
Texture Rate of change of tonal variation per unit area on the satellite Image. For example, homogeneous grassland exhibits a smooth texture, dense and tall forest usually show a coarse texture. Depends upon the scale of the photograph or image
Pattern Spatial arrangement of the objects in a satellite image data Pattern is a regular, usually repeated, shape in respect to an object. For example, rows of houses or apartments, regularly-spaced rice fields, interchanges of highways, orchards, and so on, can provide information from their unique patterns.
Shape The specific shape of an object, as it is viewed from above, will be imaged as a vertical photograph. For example, the crown of a conifer tree looks like a circle, while that of a deciduous tree has an irregular shape. Airports, factories, and so on can also be identified by their shapes.
Size A proper photo-scale (image resolution) should be selected depending on the purpose of the interpretation. Shadow Shadow is usually a visual obstacle for image interpretation. However, shadow can also give height information about a tower, tall building, mountain ranges, and others, as well as shape information from the non vertical perspective-such as the shape of a bridge.
Association A specific combination of elements, geographic characteristics,and configuration of the surroundings, or the context, of an object can provide the user with specific information for image interpretation.