SIRAN: Sinkhorn Distance Regularized Adversarial Network for DEM Super-resolution using Discriminative Spatial Self-attention

Digital Elevation Model (DEM) is an essential aspect in the remote sensing domain to analyze and explore different applications related to surface elevation information. In this study, we intend to address the generation of high-resolution DEMs using high-resolution multi-spectral (MX) satellite imagery by incorporating adversarial learning. To promptly regulate this process, we utilize the notion of polarized self-attention of discriminator spatial maps as well as introduce a Densely connected Multi-Residual Block (DMRB) module to assist in efficient gradient flow. Further, we present an objective function related to optimizing Sinkhorn distance with traditional GAN to improve the stability of adversarial learning. In this regard, we provide both theoretical and empirical substantiation of better performance in terms of vanishing gradient issues and numerical convergence. We demonstrate both qualitative and quantitative outcomes with available state-of-the-art methods. Based on our experiments on DEM datasets of Shuttle Radar Topographic Mission (SRTM) and Cartosat-1, we show that the proposed model performs preferably against other learning-based state-of-the-art methods. We also generate and visualize several high-resolution DEMs covering terrains with diverse signatures to show the performance of our model.Comment: 15 pages, 14 figure

CLiSA: A Hierarchical Hybrid Transformer Model using Orthogonal Cross Attention for Satellite Image Cloud Segmentation

Clouds in optical satellite images are a major concern since their presence hinders the ability to carry accurate analysis as well as processing. Presence of clouds also affects the image tasking schedule and results in wastage of valuable storage space on ground as well as space-based systems. Due to these reasons, deriving accurate cloud masks from optical remote-sensing images is an important task. Traditional methods such as threshold-based, spatial filtering for cloud detection in satellite images suffer from lack of accuracy. In recent years, deep learning algorithms have emerged as a promising approach to solve image segmentation problems as it allows pixel-level classification and semantic-level segmentation. In this paper, we introduce a deep-learning model based on hybrid transformer architecture for effective cloud mask generation named CLiSA - Cloud segmentation via Lipschitz Stable Attention network. In this context, we propose an concept of orthogonal self-attention combined with hierarchical cross attention model, and we validate its Lipschitz stability theoretically and empirically. We design the whole setup under adversarial setting in presence of Lov\'asz-Softmax loss. We demonstrate both qualitative and quantitative outcomes for multiple satellite image datasets including Landsat-8, Sentinel-2, and Cartosat-2s. Performing comparative study we show that our model performs preferably against other state-of-the-art methods and also provides better generalization in precise cloud extraction from satellite multi-spectral (MX) images. We also showcase different ablation studies to endorse our choices corresponding to different architectural elements and objective functions.Comment: 14 pages, 11 figures, 7 table

ETHNO-MEDICOBOTANY OF SOME TRIBAL COMMUNITIES OF BANKURA DISTRICT, WEST BENGAL, INDIA

Investigation and documentation of the status of medicinal plants and associated knowledge was conducted in Taldangra block situated at south-western part of Bankura district. Data was collected and evaluated with a questionnaire survey, semi-structured interviews, field observations and vegetation surveys. 16 medicinal plant species used to treat 40 different ailments were recorded. Leaves are the most commonly collected plant parts for medicinal purposes. Much of the ethno-medicinal knowledge is concentrated in elderly members of the community. The medicinal plants are facing threats from agricultural expansion, wood extraction and overgrazing as informed by the local authorities. Consequently, medicinal plant resources are declining with time. The study aims to assess the contribution of nonconventional medicinal plants towards community health care. A total of 62 knowledge holders from the tribal community were interviewed and medicinal uses for 16 plants were recorded. The study illustrates that medicinal plant diversity is important for community health care, which in turn, ensures conservation, awareness creation towards sustainable utilization and management of these medicinal plants diversit

The Weyl bound for triple product L-functions

Let $\pi_1, \pi_2, \pi_3$ be three cuspidal automorphic representations for the group ${\rm SL}(2, \Bbb{Z})$, where $\pi_1$ and $\pi_2$ are fixed and $\pi_3$ has large conductor. We prove a subconvex bound for $L(1/2, \pi_1 \otimes \pi_2 \otimes \pi_3)$ of Weyl-type quality. Allowing $\pi_3$ to be an Eisenstein series we also obtain a Weyl-type subconvex bound for $L(1/2 + it, \pi_1 \otimes \pi_2)$.Comment: 37 page