Xinyan Li

Mixed pixels often hinder accurate cropland mapping from remote sensing images with coarse spatial resolution. Image spatial super-resolution reconstruction technology is widely applied to address this issue, typically transforming coarse-resolution remote sensing images into fine spatial resolution images, which are then used to generate fine-resolution land cover maps using classification techniques. Deep learning has been widely used for image spatial super-resolution reconstruction; however, collecting training samples is often difficult for cropland mapping. Given that the quality of spatial super-resolution reconstruction directly impacts classification accuracy, this study aims to assess the impact of different types of training samples on image spatial super-resolution reconstruction and cropland mapping results by employing a Residual Channel Attention Network (RCAN) model combined with a spatial attention mechanism. Four types of samples were used for spatial super-resolution reconstruction model training, namely fine-resolution images and their corresponding coarse-resolution images, including original Sentinel-2 and degraded Sentinel-2 images, original GF-2 and degraded GF-2 images, histogram-matched GF-2 and degraded GF-2 images, and registered original GF-2 and Sentinel-2 images. The results indicate that the samples acquired by the histogram-matched GF-2 and degraded GF-2 images can resolve spectral band mismatches when simulating training samples from fine spatial resolution imagery, while the other three methods have limitations in their inability to fully address spectral and spatial mismatches. The histogram-matched method yielded the best image quality with PSNR, SSIM, and QNR values of 42.2813, 0.9778, and 0.9872, respectively, and produced the best mapping results, achieving an overall accuracy of 0.9306. By assessing the impact of training samples on image spatial super-resolution reconstruction and classification, this study addresses data limitations and contributes to improving the accuracy of cropland mapping, which is crucial for agricultural management and decision-making.

Encrypted range schemes enable the user to perform expressive range queries over encrypted databases by only revealing the necessary information for the search. In comparison to the extensively studied keyword schemes, range schemes inherently carry a more detrimental leakage profile as they contain extra structural information that can be exploited by attackers. This is evidenced by the escalation of attacks and defenses targeting range schemes in recent years, showing an inadequate understanding of range schemes' leakage implications. To this end, we identify and investigate two major aspects that have been largely overlooked in prior leakage analysis: 1) lack of a comprehensive approach for coordinating access, volume, and search patterns simultaneously, resulting in separate resolutions for each type of leakage; 2) absence of granular leakage quantification aimed at individual queries, leading to the gradual accumulation of imperceptible leakage over time. Based on this understanding, we present the query control framework RangeQC that provides a novel uniformed viewpoint of all three primary patterns at a per-query level. At the core of our techniques is a set of customized entropy analyzes tailored for the representative structures extracting various types of patterns. We further develop feasible countermeasures to suppress pattern leakage with tunable query control rules. Extensive evaluation results on real-world datasets demonstrate the superiority of RangeQC for leakage quantification and mitigation effectiveness.

Recent years have seen advances in generalization bounds for noisy stochastic algorithms, especially stochastic gradient Langevin dynamics (SGLD) based on stability (Mou et al., 2018; Li et al., 2020) and information theoretic approaches (Xu and Raginsky, 2017; Negrea et al., 2019; Steinke and Zakynthinou, 2020). In this paper, we unify and substantially generalize stability based generalization bounds and make three technical contributions. First, we bound the generalization error in terms of expected (not uniform) stability which arguably leads to quantitatively sharper bounds. Second, as our main contribution, we introduce Exponential Family Langevin Dynamics (EFLD), a substantial generalization of SGLD, which includes noisy versions of Sign-SGD and quantized SGD as special cases. We establish data-dependent expected stability based generalization bounds for any EFLD algorithm with a O(1/n) sample dependence and dependence on gradient discrepancy rather than the norm of gradients, yielding significantly sharper bounds. Third, we establish optimization guarantees for special cases of EFLD. Further, empirical results on benchmarks illustrate that our bounds are non-vacuous, quantitatively sharper than existing bounds, and behave correctly under noisy labels.

Rivers are important elements of the earth’s ecosystem, and their spatial distribution information is critical for the study of hydrological and biogeochemical processes. Moderate Resolution Imaging Spectroradiometer (MODIS) imagery has been widely used for river mapping due to its high temporal resolution and long-term observation records, which are essential to capture the rapid fluctuation of rivers. However, when the conventional hard classification methods are used, the accuracy of the river maps produced from MODIS data (and especially for those rivers with narrow widths) is often limited because mixed pixels are common in MODIS imagery, due to coarse spatial resolution. In this article, a cascaded spectral–spatial information combined deep convolutional neural network (CNN) model for super-resolution river mapping (DeepRivSRM) is proposed to produce Landsat-like fine-resolution river maps from MODIS images. In DeepRivSRM, a CNN made up of a spectral unmixing module and a super-resolution mapping (SRM) module is introduced to handle the spectral and spatial information simultaneously. Moreover, for training the DeepRivSRM model, an adaptive cross-entropy loss function incorporating the fraction information of the rivers is designed to improve the performance of the DeepRivSRM model for small rivers. The proposed method was evaluated with MODIS images from three test sites and was compared with hard classification, a conventional SRM method, and a CNN-based SRM method. The results show that DeepRivSRM can generate more accurate river maps by effectively learning the subpixel-scale spatial–spectral information in MODIS imagery.