mosaicked applying “Global Tilting” radiometric adjustment
and “Adaptive Feathering” mosaic adjustment. Global Tilting is
an image group adjustment that independently compares each
channel of an image to the corresponding adjacent images and
applies the best radiometric parameters. It allowed compensat-
ing any intensity/color/contrast variation within neighboring
images (Trimble, 2011). The Adaptive Feathering mosaic ad-
justment generated seamless mosaics by computing automatic
“blending function” that controlled the individual images to be
stitched into output mosaic (ibid). The computation of radiance
and ground reflectance could only be possible with a fully cali-
brated camera and/or with color targets placed in the flight area
with additional measurements of the targets during imagery
acquisition. This data was not available; therefore, final output
pixels were contented with radiometrically corrected (dark cur-
rent, vignetting, color balanced) digital numbers (
DN
).
Workflow
The workflow primarily consists of three main parts, i.e.,
segmentation, analysis (spectral signatures) and classification.
Segmentation is dividing an image into different homogeneous
regions or “image objects” (Baatz and Schäpe, 2000). Spectral
signature or spectral response curve of an object is the unique
reflected intensity value plotted over a range of wavelength.
Objects could be identified or separated from one another oth-
er based on spectral signatures and their variations. Classifica-
tion is the process of categorizing an image into meaningful
land cover classes such as forest, sand, snow, vegetation, and
water (Tso and Mather, 2009). Figure 3 shows the complete
workflow of the methodology adopted in the present study.
Segmentation
In the object-based approach, image objects (segments) serve
as a basis for classification. Different types of segmentations
were tested using Trimble eCognition
®
8.64. In the prelimi-
nary investigation, multi-resolution segmentation (Baatz
and Schäpe, 2000) revealed the most suitable segmentation
technique for the test area. Implementing multi-resolution
segmentation to the whole study area resulted in workstation
memory problems and software crashes. Thus, an alterna-
tive optimized segmentation strategy was devised, which
comprised combination of quadtree segmentation, multi-
resolution region grow segmentation, and spectral difference
segmentation. The segmentation was implemented on two
hierarchical levels (Figure 4).
Chessboard segmentation was used to rasterizing the field
boundaries resulting in level 1 image objects. The optimized
segmentation strategy produced level 2 image objects without
compromising much on their quality and took about 60 hours
to segment the whole study area. Level 2 image objects were
used as the smallest classification unit.
Figure 3.Workflow of classification. The scheme represents all the steps involved in bioenergy crop classification.
Figure 4. Segmentation hierarchical levels; level 1 involved raster-
izing parcel boundaries whereas any variations within a parcel
were detected at level 2.
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