Currently, in the application scenario of generative adversarial networks, determining how to improve the quality of the generated signals and ensure the modulation recognition accuracy of convolutional neural networks are important problems. In this paper, a generative sample quality screening method for the problem of low-quality samples generated by generative adversarial networks under few-shot conditions has been proposed, which innovatively establishes a sample expansion mode without fixing the network parameters, realizes the learning of the real data distribution by constantly updating the network weights, and enhances the quality of the expanded samples by adopting the quality screening method with two quality screenings. A generative adversarial network has been designed for this method, which reduces the time investment required for generating samples by extracting different features of few-shots of signals. The experimental results show the few-shot conditions, under the signal-to-noise ratio of −8∼12 dB and three expansion ratios of 1:1, 1:2 and 1:3. Compared with the general method expansion, the average modulation mode recognition accuracy of the QCO-CFGAN method expanded with the quality screening method is improved by 2.65%, 2.46% and 2.73%, respectively, which proves the effectiveness under this condition.

The complexity of land types and the limited spatial resolution of Synthetic Aperture Radar (SAR) imagery have led to the widespread presence of mixed pixels in radar backscatter images. The signal mixture of Endmembers within mixed pixels hinders the establishment of accurate relationships between pure Endmember’s parameters and the corresponding backscatter coefficient, thereby significantly reducing the accuracy of surface parameter inversion. However, few studies focused on decomposing and estimating the pure backscatter signals within mixed pixels. This paper proposes a novel approach based on hyperspectral unmixing techniques and the microwave backscatter contribution decomposition model to estimate the pure backscatter coefficients of all Endmembers within mixed pixels. Experimental results demonstrate that the proposed method can relatively accurately estimate the backscatter coefficients of all end members of mixed pixels. For mixed pixels composed of grassland and soil in the test area, the proportion of pixels with a relative error within 20% in estimating soil and grass endmember backscattering coefficients was close to 80% of the total number of tested pixels. Furthermore, the spatial distribution of the estimated Endmember backscattering coefficients exhibited a high degree of consistency with the endmember types, structures, and conditions that influence their backscattering behavior.