TY  - JOUR
TI  - Optimization of Remote Sensing Image Attributes to Improve Classification Accuracy
AB  - Remote sensing technologies provide very important big data to various science areas such as risk identification, damage detectionand prevention studies. However, the classification processes used to create thematic maps to interpret this data can be ineffectivedue to the wide range of properties that these images provide. At this point, there arises a requirement to optimize the data. The firstobjective of this study is to evaluate the performance of the Bat Search Algorithm which has not previously been used for improvingthe classification accuracy of remotely sensed images by optimizing attributes. The second objective is to compare the performanceof the Genetic Algorithm, Bat Search Algorithm, Cuckoo Search Algorithm and Particle Swarm Optimization Algorithm, which areused in many areas of the literature for the optimization of the attributes of remotely sensed images. For these purposes, an imagefrom the Landsat 8 satellite is used. The performance of the algorithms is compared by classifying the image using the K-Meansmethod. The analysis shows a 10-22% increase in overall accuracy with the addition of attribute optimization.
AU  - KÜÇÜK MATCI, DİLEK
AU  - Avdan, Ugur
DO  - 10.30897/ijegeo.466985
PY  - 2019
JO  - International Journal of Environment and Geoinformatics
VL  - 6
IS  - 1
SN  - 2148-9173
SP  - 50
EP  - 56
DB  - TRDizin
UR  - http://search/yayin/detay/334560
ER  -