It is well-known that

because the object scales in remote sensing images change over wide and unpredictable ranges

a problem in selecting adaptive scale filter is existed for extracting different scale objects in remote sensing images. Aiming at object scales in remote sensing image change uncertain

we introduce one class of Gaussian antisymmetric wavelets based on Gaussian kernel

which extends to Mallat Gaussian wavelet （σ=1）. Coefficients of spatial filter related to the class of Gaussian antisymmetric wavelets given in the paper are derived adaptively by selecting appropriate parameter σ values for special scale object extraction in SAR. Five group coefficients of spatial filter related to the antisymmetric wavelets have been given in this paper. It is important that ones develop interesting operators for the object recognition of SAR images and investigate approaches for feature detection in multi-resource remote sensing images. Because `speckle’ in SAR images is a multiplicative noise

we performed firstly logarithm transform over the two SAR images in preprocessing. Then the features in the logarithm images may be detected in the wavelet transform. It is shown by our experiments in two SAR images that the class of Gaussian antisymmetric wavelets is very efficient for feature extraction in remote sensing images

in which object scales change over wide ranges. Because there exist both step and roof edges in remote sensing images

maximum modulus

or zero-crossings of antisymmetric wavelet transforms can be used for edge feature detection

but the results detected exist local position discrepancy. The conclusion is important to explore new edge detectors in remote sensing images and new technology related to all automatic digital photogrammetry in future.