With the recent development of deep learning, its application in the analysis of aerial and satellite images is becoming increasingly common. However, existing models optimized for certain objects had limitations in recognizing other objects. Additionally, these models often did not take into account the morphological features of objects, leading to erroneous results.
To solve these problems, Professor Jaeyoung Hwang’s team developed a neural network called “DG-Net” that provides much more accurate results than existing models and can be applied in a wide variety of fields. DG-Net uses a test phase adaptive learning method optimized for input images to recognize object density and perform detailed segmentation.
DG-Net neural network has demonstrated high performance in various tasks of object segmentation in aerial and satellite images, achieving accuracy especially in geographical segmentation of spatial objects.
Source: Ferra
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