Optimization of Remote Sensing Image Attributes to Improve Classification Accuracy

Remote sensing technologies provide very important big data to various science areas such as risk identification, damage detection and prevention studies. However, the classification processes used to create thematic maps to interpret this data can be ineffective due to the wide range of properties that these images provide. At this point, there arises a requirement to optimize the data. The first objective of this study is to evaluate the performance of the Bat Search Algorithm which has not previously been used for improving the classification accuracy of remotely sensed images by optimizing attributes. The second objective is to compare the performance of the Genetic Algorithm, Bat Search Algorithm, Cuckoo Search Algorithm and Particle Swarm Optimization Algorithm, which are used in many areas of the literature for the optimization of the attributes of remotely sensed images. For these purposes, an image from the Landsat 8 satellite is used. The performance of the algorithms is compared by classifying the image using the K-Means method. The analysis shows a 10-22% increase in overall accuracy with the addition of attribute optimization.