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3.3.1. Image Preprocessing
In the context of digital analysis of remotely sensed data, preprocessing refer to those operations that are preliminary to the main analysis. Typically
preprocessing operations could include 1 radiometric preprocessing to adjust digital values for the effect of hazy atmosphere andor 2 geometric
preprocessing to bring an image into registration with a map or another image. In this study, operation subset image that cover only study area, and masking land
objects that will not be classified was included as part of preprocessing process.
3.3.2. Image Classification
ALOS digital image processing was carried out using Environment for Visualizing Images ENVI version 4.4. In this research, ANN supervised
classification was guided by variety of input data sets including field survey, satellite imagery and expert knowledge to produce seagrass condition map.
Twenty two scenario processes would be done to compare the result of accuracy as shown in Table 3.1.
The starting point of image classification is the different spectral characteristics of the different materials on the Earth’s surface. Digital image
classification is the process of assigning pixels to classes. Usually each pixel is treated as individual unit composed of values in several spectral bands. By
comparing pixels to one another and to those of known identity, it is possible to assemble group of similar pixels into classes that match the informational
categories of interest to users of remotely sensed data. Field data were used to determine training sites for three levels of seagrass cover classes
≤ 29,9, 30- 59,9,
≥ 60 in the ANN supervised image classification process. Reflectance
signatures for each of the three different seagrass cover classes were extracted from the ALOS AVNIR-2 scene of Pari Island for the calibration field sites,
which served as training sites for the image classification process. Characteristic spectral reflectance signatures were defined for each of the target levels of
seagrass cover to be mapped.
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