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Found 6 papers, 3 papers with code
A Mechanism-Learning Deeply Coupled Model for Remote Sensing Retrieval of Global Land Surface Temperature
In this paper, we propose a deeply coupled framework integrating mechanistic modeling and machine learning to enhance the accuracy and generalizability of single-channel LST retrieval.
PGCS: Physical Law embedded Generative Cloud Synthesis in Remote Sensing Images
The PGCS method involves two key phases: spatial synthesis and spectral synthesis.
A physics-constrained machine learning method for mapping gapless land surface temperature
Specifically, the light gradient-boosting machine (LGBM) model, which uses only remote sensing data as input, serves as the pure ML model.
Collaborative Perception in Autonomous Driving: Methods, Datasets and Challenges
The former focuses on collaboration modules and efficiency, and the latter is devoted to addressing the problems in actual application.
Learning to Aggregate Multi-Scale Context for Instance Segmentation in Remote Sensing Images
The proposed model exploits three lightweight plug-and-play modules, namely dense feature pyramid network (DenseFPN), spatial context pyramid (SCP), and hierarchical region of interest extractor (HRoIE), to aggregate global visual context at feature, spatial, and instance domains, respectively.
Multi-feature combined cloud and cloud shadow detection in GaoFen-1 wide field of view imagery
In this paper, an automatic multi-feature combined (MFC) method is proposed for cloud and cloud shadow detection in GF-1 WFV imagery.
