Optimization of Remote Sensing Image Attributes to Improve Classification Accuracy

Year 2019, Volume: 6 Issue: 1, 50 - 56, 12.04.2019

Dilek Küçük Matcı , Uğur Avdan

https://doi.org/10.30897/ijegeo.466985

Cited By: 1

Abstract

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.

Keywords

Remote Sensing, Classification, Optimization, Unsupervised Classification

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