To address the problem that the existing dehazing methods are difficult to suppress artifact generation and insufficient texture detail recovery caused by non-uniform haze distribution in real remote sensing images effectively， an unsupervised dehazing method for remote sensing images with non-uniform haze was proposed. First， based on Cycle-consistent Generative Adversarial Network （CycleGAN）， a Residual Multi-scale Attention Mechanism （RMAM） was designed in the generator to expand the receptive field and enhance the extraction ability of multi-scale texture and structural information， thereby effectively restoring realistic texture details. Second， a Haze Distribution Enhancement Module （HDEM） was designed to explicitly enhance the expression of haze features， and combined with a dual-branch fusion strategy， the network was guided to accurately identify and process non-uniform haze， thereby alleviating the artifact issue caused by insufficient haze distribution perception. Finally， a Feature Attention （FA） mechanism was embedded in the discriminator to strengthen its ability to distinguish the authenticity of local structures and textures in the image， thereby improving its ability to restore dehazed images. Experimental results on the synthetic remote sensing datasets SateHaze 1k and RICE showed that the proposed method outperformed the best-performing baseline method DedustGAN by 4.20% and 2.44%， respectively， in Peak Signal-to-Noise Ratio （PSNR）， and by 0.96% and 0.51%， respectively， in Structural Similarity Index Measure （SSIM）. Experimental results on the real-world dataset RRSD300 showed that the proposed method outperformed the best-performing baseline method Cycle-SNSPGAN by 2.04% and 0.45% in Natural Image Quality Evaluator （NIQE） and Integrated Local NIQE （IL-NIQE）， respectively. The proposed method effectively removes non-uniform haze， suppresses artifact generation， and restores texture details in remote sensing images.

Quality of intrapulse feature about radar signals has proved to be a significant foundation to decide whether the signals can be differentiated effectively. For evaluating the quality quantitatively, a method adopting fuzziness and close-degree to evaluate intrapulse feature aggregation and discreteness of signals was proposed in this paper. Space distribution of intrapluse feature about radar signals was analyzed with this method, while intrapulse feature aggregation was evaluated by fuzziness and intrapulse feature discreteness was evaluated by close-degree. And for the overlapping states of feature space distribution, a linear separable measure of intrapluse feature about radar signals was put forward by within-class distance, between-class distance and linear discriminated criterion. The simulation results, based on the experiments of two intrapulse features extracted via time-frequency atom approach about five kinds of radar signal, show that the method and measure proposed in the paper are effective and feasible. It provides a new idea and approach for quantitatively evaluating features of the radar emitter signal.