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A Combined CNN-LSTM Network for Ship Classification on SAR Images.
Sensors (Basel)
December 2024
ENSTA Bretagne, Lab-STICC, UMR CNRS 6285, 29806 Brest, France.
Satellite SAR (synthetic aperture radar) imagery offers global coverage and all-weather recording capabilities, making it valuable for applications like remote sensing and maritime surveillance. However, its use in machine learning-based automatic target classification faces challenges, including the limited availability of SAR target training samples and the inherent constraints of SAR images, which provide less detailed features compared to natural images. These issues hinder the effective training of convolutional neural networks (CNNs) and complicate the transfer learning process due to the distinct imaging mechanisms of SAR and natural images.
View Article and Find Full Text PDFBO-CNN-BiLSTM deep learning model integrating multisource remote sensing data for improving winter wheat yield estimation.
Front Plant Sci
December 2024
School of Surveying and Land Information Engineering, Henan Polytechnic University, Jiaozuo, China.
Introduction: In the context of climate variability, rapid and accurate estimation of winter wheat yield is essential for agricultural policymaking and food security. With advancements in remote sensing technology and deep learning, methods utilizing remotely sensed data are increasingly being employed for large-scale crop growth monitoring and yield estimation.
Methods: Solar-induced chlorophyll fluorescence (SIF) is a new remote sensing metric that is closely linked to crop photosynthesis and has been applied to crop growth and drought monitoring.
Assessment of the effect of management activities like a drought salinity barrier and herbicide treatments on the spread of submersed and floating aquatic estuary macrophytes.
Sci Total Environ
January 2025
Center for Spatial Technologies and Remote Sensing (CSTARS), Institute of the Environment, University of California, One Shields Avenue, Davis, CA 95616, USA. Electronic address:
Estuaries are complex ecosystems, being difficult to determine the way management actions affect them. This study quantitatively evaluated the spread of invasive submerged and floating aquatic macrophyte vegetation in Franks Tract of the Sacramento-San Joaquin Delta in response to two types of management actions, drought salinity barriers in years 2015, 2021 and 2022, and herbicide treatments in years 2004-2022. A Random Forest algorithm applied to airborne hyperspectral and satellite multispectral images generated maps of macrophyte cover in 2004-2022.
View Article and Find Full Text PDFSubstantially underestimated global health risks of current ozone pollution.
Nat Commun
January 2025
The State Key Laboratory of Information Engineering in Surveying, Mapping and Remote Sensing, Wuhan University, Wuhan, China.
Existing assessments might have underappreciated ozone-related health impacts worldwide. Here our study assesses current global ozone pollution using the high-resolution (0.05°) estimation from a geo-ensemble learning model, with key focuses on population exposure and all-cause mortality burden.
View Article and Find Full Text PDFReconfigurable Sequential-Logic-in-Memory Implementation Utilizing Ferroelectric Field-Effect Transistors.
ACS Nano
January 2025
SKKU Advanced Institute of Nanotechnology (SAINT), Sungkyunkwan University, Suwon 16419, Korea.
In modern digital systems, sequential logic circuits store and process information over time, whereas combinational logic circuits process only the current inputs. Conventional sequential systems, however, are complex and energy-inefficient due to the separation of volatile and nonvolatile memory components. This study proposes a compact, nonvolatile, and reconfigurable van der Waals (vdW) ferroelectric field-effect transistor (FeFET)-based sequential logic-in-memory (S-LiM) unit that performs sequential logic operations in two nonvolatile states.
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