Fire image detection based on clustering data mining techniques

The number of forest fires is growing exponentially with globalization negative impacts and industry evolution. The firefighters are unable to attend fire sources in the desired elapse time. Hence a huge number of forests are destroyed yearly. The statics demonstrate horrible prediction in a time interval of less than ten years. Necessary action and evolution plans must be established to save the globe from an invasive destruction due to the disappear of green areas and consequent disequilibrating ecosystem effects. The obvious idea is to take advantage of current evolution in informatic systems and robotic field, to develop a distance controllable device to scan areas classified as high risk in the vulnerable season (hot season). The first step is to design a machine learning accurate approach to detect fire area on pictures acquired by probable drone or intelligent systems, responsible of the scanning task. Through literature, several approaches were developed treating pictures that are more with afront view of the flames. Training a machine learning algorithm with such pictures with huge areas of flames is feasible. Nonetheless, treating aerial images is not a very easy approach. A deep analysis of the chosen feature engineering technique and machine learning model is required. The current paper accesses the performance of wavelet-based feature extraction technique within different traditional clustering techniques and ranking methods. The results were accessed using different metrics, to show the effectiveness of the approach, namely sensitivity specificity, precision, recall, f-measure, and g-mean.