Synthetic Aperture Radar (SAR) remote sensing images with all-day and all-weather advantages are attracting ever-increasing attention in various areas. However, the interpretation of SAR images is quite challenging and not adapted to the non-expert people. To improve the interpretation of SAR images, a SAR-to-optical image translation method based on a modified conditional generation adversarial network (cGAN) is proposed. In the proposed method, the characteristics of the SAR and optical images are comprehensively considered. To generate better results, a modified strongly constrained cGAN, with the generator and the discriminator networks, based on structure similarity index measure (SSIM) and L1 norm was constructed. The generator network aims to generate an artificial optical image by SAR, and the discriminator network aims to force the generated image to be close to the real optical image. The proposed method was verified and compared with several state-of-the-art methods, and the experimental results show the superiority of the proposed method.

Automatic cloud detection and classification using satellite cloud imagery have various meteorological applications such as weather forecasting and climate monitoring. Cloud pattern analysis is one of the research hotspots recently. Since satellites sense the clouds remotely from space, and different cloud types often overlap and convert into each other, there must be some fuzziness and uncertainty in satellite cloud imagery. Satellite observation is susceptible to noises, while traditional cloud classification methods are sensitive to noises and outliers; it is hard for traditional cloud classification methods to achieve reliable results. To deal with these problems, a satellite cloud classification method using adaptive fuzzy sparse representation-based classification (AFSRC) is proposed. Firstly, by defining adaptive parameters related to attenuation rate and critical membership, an improved fuzzy membership is introduced to accommodate the fuzziness and uncertainty of satellite cloud imagery; secondly, by effective combination of the improved fuzzy membership function and sparse representation-based classification (SRC), atoms in training dictionary are optimized; finally, an adaptive fuzzy sparse representation classifier for cloud classification is proposed. Experiment results on FY-2G satellite cloud image show that, the proposed method not only improves the accuracy of cloud classification, but also has strong stability and adaptability with high computational efficiency.

Spatiotemporal fusion has been attracting increasing attention in remote sensing applications, such as environmental monitoring and land cover change detection, due to its excellent ability to obtain high spatial and temporal resolution images. The land cover change has always been a great challenge in spatiotemporal fusion. Although most spatiotemporal fusion methods have demonstrated satisfactory performance in addressing phenological changes, the performance in terms of abrupt land cover type changes, such as floods or mudslides, falls short. To alleviate this issue, we propose a change information guided spatiotemporal fusion (CIG-STF) method. The proposed CIG-STF integrates change detection and spatiotemporal fusion in a unified framework, by taking advantage of change detection in capturing land cover changes to assist spatiotemporal fusion. Specifically, the CIG-STF comprises three modules: multiscale dilated feature extractor module (MDFE), spatiotemporal fusion-change detection integrated module (STF-CD), and reconstruction module. The MDFE employs multiscale dilated convolutions to comprehensively extract features, to prevent crucial information loss by increasing the convolutional receptive field. In the STF-CD, a change detection module on attention strategy is integrated into the spatiotemporal fusion task, by excavating land cover changes to further enhance the fusion performance. In addition, we design a dynamic decay loss function to further leverage change information, ensuring the accuracy of both change information and prediction results. The experiments were verified on the publicly available LGC and Daxing datasets with manual change labels. The experimental results demonstrate the superior performance of the proposed CIG-STF in both phenological variations and land cover type changes.

Observing clouds to understand the weather is a crucial method for people to forecast upcoming conditions. Utilizing content-based satellite cloud image retrieval allows for the swift discovery of comparable historical cloud images, significantly aiding meteorologists in their advanced investigations. Nevertheless, satellite cloud images often present complexities due to their inclusion of diverse cloud types, leading to inadequate retrieval outcomes when relying on conventionally employed single-label retrieval techniques. Despite notable accomplishments in cloud image retrieval applications utilizing deep neural networks, concerns regarding network interpretability undermine confidence in the model's deductive outcomes. This article introduces the interpretable cloud image hash retrieval network, a framework that employs a singular object-level global unit alongside multiple local feature units for the purpose of generating hash codes tailored to cloud image retrieval. Furthermore, an attention branching network is incorporated to enhance the model's focus on discriminative regions within the image. In addition, a suppression module is implemented to progressively uncover complementary regions through the suppression of prominent areas in preceding layers and the amalgamation of relationships among activated regions. This ensures that each feature unit is endowed with distinctive semantic information, thereby imparting a level of interpretability to the retrieval outcomes. On this foundation, multilabel supervision is seamlessly integrated into the deep hash learning framework. This integration not only enhances the depiction of intricate semantic contents within cloud images but also boosts retrieval efficiency. Comprehensive experimental outcomes, grounded in the publicly accessible satellite cloud map dataset LSCIDMR-V2, demonstrate superior performance relative to other methods.