Abstract
Synthetic aperture radar (SAR) provides rich information about the Earth’s surface under all-weather and day-and-night conditions, and is applied in many relevant fields. SAR imagery semantic segmentation, which can be a final product for end users and a fundamental procedure to support other applications, is one of the most difficult challenges. This paper proposes an encoding-decoding network based on Deeplabv3+ to semantically segment SAR imagery. A new potential energy loss function based on the Gibbs distribution is proposed here to establish the semantic dependence among different categories through the relationship among different cliques in the neighborhood system. This paper introduces an improved channel and spatial attention module to the Mobilenetv2 backbone to improve the recognition accuracy of small object categories in SAR imagery. The experimental results show that the proposed method achieves the highest mean intersection over union (mIoU) and global accuracy (GA) with the least running time, which verifies the effectiveness of our method.
Highlights
With the development of the synthetic aperture radar (SAR) imaging system, large volumes of SAR imagery have become available to support a wide range of applications, such as environment monitoring and geology
The Deeplabv3+ network with an efficient Mobilenetv2 backbone was introduced in this paper to semantically segment SAR imagery
This method uses the proposed potential energy loss function based on the Gibbs distribution to model the dependencies among different categories efficiently
Summary
With the development of the synthetic aperture radar (SAR) imaging system, large volumes of SAR imagery have become available to support a wide range of applications, such as environment monitoring and geology. Traditional methods for SAR imagery semantic segmentation mainly include the threshold method [3] and clustering algorithm [4]. These methods produce segmentation results by using the pixel’s amplitude value and do not consider the characteristics of SAR imagery, such as speckle noise and complex structure, which result in the inevitable segmentation errors. There are some popular feature extraction methods [5] in SAR image segmentation that can produce promising results only if the feature selection is carefully designed. These methods do not consider the contextual information of SAR imagery and are susceptible to speckle noise, which adversely impacts
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