Despite being impactful on a variety of problems and applications, the generative adversarial nets (GANs) are remarkably difficult to train. This issue is formally analyzed by , who also propose an alternative direction to avoid the caveats in the minmax two-player training of GANs. The corresponding algorithm, called Wasserstein GAN (WGAN), hinges on the 1-Lipschitz continuity of the discriminator. In this paper, we propose a novel approach to enforcing the Lipschitz continuity in the training procedure of WGANs. Our approach seamlessly connects WGAN with one of the recent semi-supervised learning methods. As a result, it gives rise to not only better photo-realistic samples than the previous methods but also state-of-the-art semi-supervised learning results. In particular, our approach gives rise to the inception score of more than 5.0 with only 1,000 CIFAR-10 images and is the first that exceeds the accuracy of 90% on the CIFAR-10 dataset using only 4,000 labeled images, to the best of our knowledge.

Glacial lakes (GLs), a vital link between the hydrosphere and the cryosphere, participate in the local hydrological process, and their interannual dynamic evolution is an objective reflection and an indicator of regional climate change. The complex terrain and climatic conditions in mountainous areas where GLs are located make it difficult to employ conventional remote sensing observation means to obtain stable, accurate, and comprehensive observation data. In view of this situation, this study presents an algorithm with a high generalization ability established by optimizing and improving a deep learning (DL) semantic segmentation network model for extracting GL contours from combined synthetic-aperture radar (SAR) amplitude and multispectral imagery data. The aim is to use the high penetrability and all-weather advantages of SAR to reduce the effects of cloud cover as well as to integrate the multiscale and detail-oriented advantages of multispectral data to facilitate accurate, quantitative extraction of GL contours. The accuracy and reliability of the model and algorithm were examined by employing them to extract the contours of GLs in a large region of south-eastern Tibet from Landsat 8 optical remote sensing images and Sentinel-1A amplitude images. In this study, the contours of a total 8262 GLs in south-eastern Tibet were extracted. These GLs were distributed predominantly at altitudes of 4000–5500 m. Only 17.4% of these GLs were greater than 0.1 km2 in size, while a large number of small GLs made up the majority. Through analysis and validation, the proposed method was found highly capable of distinguishing rivers and lakes and able to effectively reduce the misidentification and extraction of rivers. With the DL model based on combined optical and SAR images, the intersection-over-union (IoU) score increased by 0.0212 (to 0.6207) on the validation set and by 0.038 (to 0.6397) on the prediction set. These validation data sufficiently demonstrate the efficacy of the model and algorithm. The technical means employed in this study as well as the results and data obtained can provide a reference for research and application expansion in related fields.

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Significant seasonal fluctuations could occur in the regional scattering characteristics and surface deformation of saline soil, and cause decorrelation, which limits the application of the conventional time-series InSAR (TS-InSAR). For extending the saline-soil deformation monitoring capability, this paper presents an improved TS-InSAR approach, based on the interferometric coherence statistics and high-coherence interferogram refinement. By constructing a network of the refined interferograms, high-accuracy ground deformation can be extracted through the weighted least square estimation and the coherent target refinement. To extract the high-accuracy deformation of a representative saline soil area in the Qarhan Salt Lake, 119 C-band Sentinel-1A images collected between May 2015 and May 2020 are selected as the data source. Subsequently, 845 refined interferograms are selected from all possible interferograms to conduct the network inversion, based on the related thresholds (the temporal baseline < 49 days, the average spatial coherences > 0.5, respectively). Compared with the conventional TS-InSAR measurements, both the accuracy and reliability of the extracted deformation results of the saline soil increased dramatically. Furthermore, the testing results indicate that the improved TS-InSAR method has advantages on the deformation extraction in the saline soil region, and is adaptive to reflecting the typical seasonal variations of the saline soil.

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