SEARCH RESULT

Using Synthetic Aperture Radar images become popular in many military or civilian applications such as algorithm design, geo-referencing and Automatic Target Recognition. One of the main reason is SAR images can be obtained in any weather condition like rainy or cloudy weather even without daylight.However, Synthetic Aperture Radar (SAR) images contain multiplicative noise called speckle which makes analyzing images difficult. Therefore, there are many algorithms developed about despeckling SAR images in last decades. Each algorithm has strengths and weaknesses such as some algorithms work great in texture areas and some can work fine about homogeneous regions. To achieve more efficient result in despeckling SAR images, we proposed a method which uses 3 despeckling algorithms (SSD, MSAR_BM3D and FANS) and apply those algorithms in the regions which they are powerful. The proposed method splits a SAR image into smaller images and use Convolutional Neural Networks to categorize the sub images to find which algorithm is the best for that region. Afterwards, sub images despeckled using the algorithm which CNN selected and sub images come together and create the final despeckled image. The proposed method aimed despeckling of noises from the Synthetic Aperture Radar images more effective than the available despeckling algorithms.

International Conference on Advanced Technologies, Computer Engineering and Science
ICATCES

Yusuf Şevki Günaydın Baha Şen