###### Abstract
The above-mentioned requirements can be addressed using hyperspectral imaging system (HSI) which is relatively new technique based on hyperspectral imaging and visible near infrared (VSNIR) spectroscopy. Red Green Blue (RGB) devices give a valuable insight into the limitations of colour discrimination with a low number of photoreceptors, as the principles involved in the interpretation of photoreceptor signals in humans also apply to RGB camera responses. Whereas, the hyperspectral camera architecture provides means to explore other important aspects of colour vision like the perception of certain types of camouflage and colour constancy where multiple, narrow-band sensors increase resolution [12]. HSI has been used in many fields, such as food, agriculture, medical and target detection because of its unique combination of spectroscopy and imaging properties and characteristics like non-destructive, robust nature. HSI generates a hyperspectral cube in which XY represents the spatial information and Z represents the wavelength. Scanning the samples and recording data for a wide area in less time is a major advantage of HSI over other vibrational spectroscopy technique. This technique gives the ability to remotely identify the composition of each pixel of the generated image [13]. The fast, non-invasive, non-destructive and contactless characteristic of HSI makes it a promising standoff trace detection tool as it provides remote and real-time measurements. A key issue for identifying traces of chemical in the hyperspectral image from remotely sensed data is the identification of the minimum mapping size of the pure chemical. The accuracy of the model depends on several factors of like altitude of the sensor, velocity of the vehicle and the minimum size of the traces of the target chemical to be identified. A classified image, created for trace detection of chemical from classification model data, are presented using a minimum size of the chemical that can be mapped. It is defined as \"the smallest size of the trace of the chemical that cab be mapped as a discrete entity\". Determining the minimum size of the pure chemical for trace detection of chemical in hyperspectral image is an important consideration due to the mix pixel problem in which a pixel can be dominated by the background material. Due to this limitation, it is often noticed that the performance of the algorithms used to process the hyperspectral data gets affected. The size of the chemical and resolution of the sensor can lead to challenging issue like mixed pixel [14]. If the quantity of the trace of the chemical is less than the background material in the pixel in that case mix pixel problem would arise. Irrespective of the spatial resolution of the sensor, in natural environmental conditions the spectral signature for a nominal pixel is invariably a mixture of the signatures of the background or clutter materials present in the field of view of the sensor [15]. This results in identification of pixels which contain mixture of chemical and background material, especially on the edges of the chemical, which reduces the overall accuracy. Very few articles have been published on determining minimum size of the trace of chemical to be identified and mixed pixel problem at a spatial resolution less than a centimeter. An inappropriate size of minimum mapping size of chemical results into \"salt and pepper\" effect where many single pixels of a target chemical that are interspersed with contiguous areas of other background material. Individual pixels are not spectrally pure because diffuse directional reflectance from neighboring pixels contribute to a given pixel's spectral signature. Such problem can be resolved by using appropriate size of minimum mapping size of chemical. Whereas, if the minimum mapping size of chemical is small then problem of mixed pixel problem is reduced as only pure pixels containing chemicals will be identified.