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The present study was undertaken with the objective to check effectiveness of spectral similarity measures to develop precise cropspectra from the collected hyperspectral field spectra. In Multispectral and Hyperspectral remote sensing, classification of pixels isobtained by statistical comparison (by means of spectral similarity) of known field or library spectra to unknown image spectra.Though these algorithms are readily used, little emphasis has been placed on use of various spectral similarity measures to selectprecise crop spectra from the set of field spectra. Conventionally crop spectra are developed after rejecting outliers based only onbroad-spectrum analysis. Here a successful attempt has been made to develop precise crop spectra based on spectral similarity. Asunevaluated data usage leads to uncertainty in the image classification, it is very crucial to evaluate the data. Hence, notwithstandingthe conventional method, the data precision has been performed effectively to serve the purpose of the present research work. Theeffectiveness of developed precise field spectra was evaluated by spectral discrimination measures and found higher discriminationvalues compared to spectra developed conventionally. Overall classification accuracy for the image classified by field spectraselected conventionally is 51.89% and 75.47% for the image classified by field spectra selected precisely based on spectralsimilarity. KHAT values are 0.37, 0.62 and Z values are 2.77, 9.59 for image classified using conventional and precise field spectrarespectively. Reasonable higher classification accuracy, KHAT and Z values shows the possibility of a new approach for fieldspectra selection based on spectral similarity measure

Effectiveness of Spectral Similarity Measures to Develop Precise Crop Spectra for Hyperspectral Data Analysis