PJB-2011-267
CLASSIFICATION OF COTTON AND SUGARCANE PLANTS ON THE BASIS OF THEIR SPECTRAL BEHAVIOR
MUHAMMAD SHAHZAD SHIFA1, M. SHAHID NAWEED2, MUHAMMAD OMAR2, M. ZEESHAN JHANDIR2 AND TANVEER AHMED3
Abstract
The study is about the classification of cotton (Gossypium hirsutum L.) and sugarcane (Saccharum officinarum L.) crops based on spectral behavior of their plants. The hand held ground based remote sensing optical Multispectral Radiometer MSR5 has been used for this purpose. MSR5 scans a scene and gives its digital representation in 5 separate spectral bands compatible with Landsat satellite images, so the study is also applicable to Landsat images. To judge the discrimination power of five spectral bands, used as features to represent the scenes, K-means algorithm is used for unsupervised clustering of reflectance sample data set. Computational and visualization results of clustering through K-means show that MSR5 scans are good candidates for classification purpose. Supervised classification is achieved using K-nn algorithm, and 98% accurate results of classification are achieved. Choice of MSR5 for crop classification is good for two reasons: the results are accurate and the technique is an efficient way to represent an image with only five real values covering a 1.5 square meter.
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