Image Interpretability

Visual identification of objects in vertical airphotos normally requires ground sample distance (GSD) 3-5 times smaller than the object itself (Hall, 1997), as noted before (Chapter 2). For example, to positively identify a house (~ 10 m x 10 m), the camera/image system needs to achieve a GSD in the range of 2-3 m. Factors other than spatial resolution often affect the ability to recognize objects, however. Contrasts in color and brightness are important clues as are size, shape, context, shadows, and other factors

Small-Format Aerial Photography

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noted above. Keep in mind that with increasing height above the ground, atmospheric effects begin to degrade image quality regardless of spatial resolution.

With these limitations in mind, a perceptual measure of image quality or interpretability has been developed. This measure is the U.S. National Imagery Interpretability Rating Scale—NIIRS (Leachtenauer et al., 1997). An image rating depends on the most difficult interpretation task that can be performed, which indicates the level of interpret-ability that can be achieved (Table 10-1).

TABLE 10-1 The civilian U.S. National Imagery Interpretability Rating Scale levels (0-9) and examples of interpretation tasks (based on Leachtenauer et al., 1997).

Rating Level

Interpretation tasks

0

Interpretability precluded by obscuration, degradation, or poor resolution

1

Distinguish between major landuse classes-urban, agricultural, forest, etc. Detect medium-sized port facility

Distinguish between runways anct taxiways at a large airport Identify regional drainage patterns-dendritic, trellis, etc.

2

Identity large (800 m diameter) center-pivot irrigated fields

Detect large buildings-hospital, factory, etc.

Identify road pattern-highway interchange, road network, etc.

Detect ice-breaker tracks in sea ice; detect wake of large (100 m) ship

3

Detect large area (800 m square) contour plowing Detect individual houses in residential neighborhoods Detect trains or strings of railroad cars on tracks Identify inland waterways navigable by barges Distinguish between natural forest and orchards

4

Identify farm buildings as barns, silos, or residences Count tracks along railroad right-of-way or in train yards Detect basketball court, tennis court, volleyball court in urban area Identify individual tracks, control towers or switching points in railroad yard Detect jeep trails through grassland

5

Identify Christmas-tree plantation Recognize individual train cars by type-box, flat, tank, etc. Detect open bay doors of vehicle storage buildings Identify large tents within camping areas

Distinguish between deciduous and coniferous forest during leaf-off season Detect large animals (elephant, rhinoceros, bison) in grassland

6

Detect narcotics intercropping based on texture Distinguish between row crops and small grains Identify passenger vehicles as sedans, station wagons, etc. identify individual utility poles in residential neighborhoods Detect foot trails in barren areas

7

Identify individual mature plants in a field of known row crops

Recognize individual railroad ties

Detect individual steps on stairways

Detect stumps and rocks in forest clearings or meadows

8

Count individual baby farm animals-pigs, sheep, etc.

Identify a survey benchmark set in a paved surface

Identify body details or license plate on passenger car or truck

Recognize individual pine seedlings or individual water lilies on a pond

Detect individual bricks in a building

9

Identify individual seed heads on grain crop-wheat, oats, barley, etc.

Recognize individual barbs on a barbed-wire fence

Detect individual spikes in railroad ties

Identify individual leaves on a tree

Identify an ear tag on large animals-deer, elk, cattle, etc.

TABLE 10-1 The civilian U.S. National Imagery Interpretability Rating Scale levels (0-9) and examples of interpretation tasks (based on Leachtenauer et al., 1997).

FIGURE 10-1 Ikonos satellite image showing a portion of Fort Leavenworth military base, Kansas, United States. Vehicles are clearly visible on roads and in parking lots; parking pattern - diagonal or parallel - is evident. The smallest features that can be identified (under enlargement) are individual parking stalls, which give an interpretability rating of 6. The slightly fuzzy appearance demonstrates the spatial limitation of such imagery. Panchromatic band (green + red + near-infrared) with 1 m spatial resolution, August 2000. Image processing by JSA.

FIGURE 10-1 Ikonos satellite image showing a portion of Fort Leavenworth military base, Kansas, United States. Vehicles are clearly visible on roads and in parking lots; parking pattern - diagonal or parallel - is evident. The smallest features that can be identified (under enlargement) are individual parking stalls, which give an interpretability rating of 6. The slightly fuzzy appearance demonstrates the spatial limitation of such imagery. Panchromatic band (green + red + near-infrared) with 1 m spatial resolution, August 2000. Image processing by JSA.

Manned-space small-format photography, as practiced on space-shuttle and space-station missions, provides images of the Earth's surface for rating levels 1-3. Rating levels of 4-6 are typically attained by conventional aerial photography and high-resolution commercial satellite imagery (Fig. 10-1), and sometimes level 7 is possible. However, the highest rating levels (8-9) are generally not available for civilian use. These highest NIIRS rating levels may be acquired, in many cases, with SFAP from low-height platforms (Fig. 10-2).

the basic features noted above are combined for interpretation of the subjects discussed below. This review is not intended to be exhaustive, but rather to introduce the general concepts of interpretation as applied to SFAP. For in-depth discussion of airphoto interpretation, readers should consult Avery and Berlin (1992), Philipson (1997), Jensen (2007), Lillesand et al. (2008) and similar texts on this subject. Classical textbooks with emphasis on geo-scientific subjects are Lueder (1959), Schneider (1974), and Kronberg (1995).

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