Keywords: Supervised Classification, Change Detection
Abstract Natural healthy grass and varnished grass all appear green under a visible band, and generally not to be distinguishable. But this does not apply to the near infrared wave testing which is useful in determining the health conditions of plants. Experiments have been designed and executed, therefore, using both normal color slides and color infrared slides to take both natural health and varnished grass at a close distance. Then, the slides were scanned and transformed to obtain digital images for comparison. Observing the changes on the brightness value, determining the differences in healthy plants and varnished grass, and then studying all the above results for reference in order to improve the credibility on plant testing in the future. Variables are controlled and fixed in the whole process to provide an ideal remote sensing environment and to increase the accuracy of the experiment.
1. Introduction
When investigating the characteristic of the ground area with remote sensed images, one should keep in mind that to minimize unwanted spectral variability as well as to maximize this variability when the specific application requires it (Lillesand, 2000). According to this, in order to test the accuracy of change detection using postclassification comparison, such as temporal effects and spatial effects are minimized in this research. The methods, experimental design, results and their analyses are discussed respectively in the following sections.
2. Methodology
2.1. Image Classification
In general, image classification involves three procedures, supervised classification, unsupervised classification, and hybrid classification. Supervised classification with Gaussian maximum likelihood classifier has been used to simplify the study and improve the accuracy.
Base on the maps, images, pictures that could represent the particle category are taken as samples for training sites. To calculate the statistics of the mean, variance, variance-covariance matrix on every training sites, and then come up with the classification according to the produced equation and appropriate wave band formula.
Gaussian maximum likelihood classifier is one of the most used formulas in supervised classification. First, the brightness value of every category, wave band from the image is set as norm and then the operator determine the numbers of categories in the picture. And training sites are picked and selected in each picture. Using the brightness value of the picture in training sites, calculating the mean and covariance matrix within each wave band in each category. And then apply the result to the equation of possibility, and calculus then figure out the possibilities of the item to each category. Finally, classify according to the maximum likelihood.
2.2. Change Detection
To detect the changes on the ground area or gather the changes in a short period of time can only rely on the remote sensing image data. Change detection is to compare and contrast the two images with symmetrical positions, and use image-handling technique to analyze the reformed area. There are many methods to detect the reformation, such as Image Differencing Method, Multi-Date Composite Image Change Detection, and Post-Classification Comparison Change Detection…etc. The Post-Classification Comparison Change Detection is to classify the rectified images separately from two periods of time, giving appropriate marks to different particles on the surface of the ground. Then, compare and analyze the classified images from the two periods to figure out the change-detecting matrix, and finally construct the change map.
3. Experimental Design
This research is to investigate the light wave reaction on the real green grass. Normal color slides and color infrared slides have been used to closely take the same area. Flow chart of the experiment is shown in Fig.1.
Figure 1 Flow chart of the experiment
