arXiv:2008.00168
공식 코드 · Papers with Code
Land Cover Classification from Remote Sensing Images Based on Multi-Scale Fully Convolutional Network
원문 초록
In this paper, a Multi-Scale Fully Convolutional Network (MSFCN) with multi-scale convolutional kernel is proposed to exploit discriminative representations from two-dimensional (2D) satellite images.
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HyperPointFormer: Multimodal Fusion in 3D Space with Dual-Branch Cross-Attention Transformers
Prototype-Based Information Compensation Network for Multi-Source Remote Sensing Data Classification
Core-Set Selection for Data-efficient Land Cover Segmentation
PAD: Phase-Amplitude Decoupling Fusion for Multi-Modal Land Cover Classification
Geographical Context Matters: Bridging Fine and Coarse Spatial Information to Enhance Continental Land Cover Mapping
A Survey on Remote Sensing Foundation Models: From Vision to Multimodality
토론
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