## 1 Introduction  
It is estimated that half of the world's population is under the risk of dengue fever, spread by mosquitoes [1]. It is also identified that approximately 390 million dengue infections are identified every year around the globe [2]. There is also evidence of the outbreak of combined infections of dengue and chikungunya pathogens in human beings [3]. Even a typical dengue vaccine such as Sanofi Pasteur CYD-TDV shows an efficiency of only 65.5% among people in the age group of above nine years and 44.6% in children younger than nine years [4]. The major transmission vectors of zika, dengue, yellow fever and chikungunya include mosquitoes such as _Aedes_, _Culex_ and _Anopheles_[5, 6]. The inference from the analysis of research works related to mosquito-transmitted diseases demands the development of an efficient automated system for detection, surveillance and classification of mosquitoes. Several works have reported the prediction and mapping of geographical regions that are vulnerable to mosquito transmitted diseases, by analyzing and manipulating the satellite images. [PERSON], [PERSON] and [PERSON] presented an aerial photographic identification of mosquito larva inhabitants through of remote sensing [7]. In this study, the authors indicated the possibility of predicting spatial and temporal distribution of malaria, by analyzing the mosquito larval density and data through remote sensing. A similar technique was used by [PERSON], [PERSON] and [PERSON], where the authors used Global Information System (GIS), and integrated remote sensing methods for estimating the potential larval inhabitants of _Culex_ mosquitoes that transmit the West Nile virus [8]. In addition to the mosquito larva inhabitant analysis on a macroscopic level, there are also works that reported on the analysis of mosquito activities and flight trajectory analysis [9]. In the above-mentioned work, the authors used multiple cameras to extract the flight trajectory of mosquitoes to understand the characteristics of mosquito colonies. Even though several works have reported the geographical distribution of mosquito larva and disease outbreak, minimal focus is given to species-wise classification of mosquitoes, which lights the path for mosquito density mapping and classification. The mosquito identification method includes DNA analysis, which required a very undeveloped mosquito sample collection method. Moreover, the method requires several laboratory examinations to be performed with an expert supervision [10; 11]. Hence, an automated method could be more useful for mosquito classification. [PERSON] et al. presented a mosquito classification by spectral analysis of wing beat waveforms followed by an artificial neural network classification system [12]. Results of this study indicated an average accuracy of 72.67% in classifying the mosquito species. Similarly, the work carried out by [PERSON], [PERSON], et al. proposed a method for classifying flying insects including the _Aedes_ mosquito by analyzing their wing-beat frequencies [13]. The study indicated success in classifying species and sex of individual insects with an accuracy of 84%. Even though the method of spectral analysis of wing beat waveforms, followed by a trained classifier method, yields success, it is very difficult to acquire the wing-beat waveform from a mosquito inhabitant. The typical method for vision-based object classification starts with feature extraction, followed by using a trained classifier for classification. [PERSON] et al. presents a new feature extraction approach that uses genetic algorithm for feature selection, feature extraction, and training classifiers [14]. A method for the order level classification of insects from the images using support vector machine (SVM) and artificial neural network (ANN) has been mentioned by [PERSON] et al. [15], where the authors introduced a novel feature extraction method from the acquired images of insects.