To　realize　fast　and　effective　synthetic　aperture　radar　(SAR)　deception　jamming,　a　high-quality　SAR　deception　jamming　template　library　can　be　generated　by　performing　sample　augmentation　on　SAR　deception　jamming　templates.　However,　the　current　sample　augmentation　schemes　of　SAR　deception　jamming　templates　face　certain　problems.　First,　the　authenticity　of　the　templates　is　low　due　to　the　lack　of　speckle　noise.　Second,　the　generated　templates　have　a　low　similarity　to　the　target　and　shadow　areas　of　the　input　templates.　To　solve　these　problems,　this　study　proposed　a　sample　augmentation　scheme　based　on　generative　adversarial　networks,　which　can　generate　a　high-quality　library　of　SAR　deception　jamming　templates　with　shadows.　The　proposed　scheme　solved　the　two　aforementioned　problems　from　the　following　aspects.　First,　the　influence　of　the　speckle　noise　was　considered　in　the　network　to　avoid　the　problem　of　reduced　authenticity　in　the　generated　images.　Second,　a　channel　attention　mechanism　module　was　used　to　improve　the　network＇s　learning　ability　of　the　shadow　features,　which　improved　the　similarity　between　the　generated　template　and　the　shadow　area　in　the　input　template.　Finally,　the　single　generative　adversarial　network　(SinGAN)　scheme,　which　is　a　generative　adversarial　network　capable　of　image　sample　augmentation　for　a　single　SAR　image,　and　the　proposed　scheme　were　compared　regarding　the　equivalent　number　of　looks　and　the　structural　similarity　between　the　target　and　shadow　in　the　sample　augmentation　results.　The　comparison　results　demonstrated　that,　compared　to　the　templates　generated　by　the　SinGAN　scheme,　those　generated　by　the　proposed　scheme　had　targets　and　shadow　features　similar　to　those　of　the　original　image　and　could　incorporate　speckle　noise　characteristics,　resulting　in　a　higher　authenticity,　which　helps　to　achieve　fast　and　effective　SAR　deception　jamming.