Abstract
Abstract. SAR images are different from the optical images in terms of image properties with the values of scattering instead of reflectance. This makes SAR images difficult to apply the traditional object detection methodologies. In recent years, deep learning models are frequently used in segmentation and object detection purposes. In this study, we have investigated the potential of U-Net models for building detection from SAR and optical image fusion. The datasets used are Sentinel 1 SAR and Sentinel-2 multispectral images, provided from ‘SpaceNet 6 Multi Sensor All-Weather Mapping’ challenge. These images cover an area of 120 km2 in Rotterdam, the Netherlands. As training datasets 20 pieces of 900 by 900 pixel sized HV polarized and optical image patches have been used together. The calculated loss value is 0.4 and the accuracy is 81%.
Highlights
Radar systems are one the types of active remote sensing with its capacity to operate in all weather conditions
When the SAR images were examined, the highest reflection value was seen at HV polarization in manmade objects
convolutional neural network (CNN) is the basic architecture of concept of deep learning for image processing
Summary
Radar systems are one the types of active remote sensing with its capacity to operate in all weather conditions. The SAR images are created in a 2-dimensional plane by processing the scattering of the transmitted radar signals. The black and gray scales that make up the composition of the radar image show the strength of the reflected signals. SAR is a type of radar that provides higher resolution than the image normally obtained by a single larger unit by combining the radar images collected by more than one small radar unit electronically. Different polarization of Radar signals provides various scattering from the objects with regard to their geometrical properties. When the SAR images were examined, the highest reflection value was seen at HV polarization in manmade objects. For this reason, only HV polarization band is used in the developed algorithm
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