Land Cover Classification from Remote Sensing Images Based on Multi-Scale Fully Convolutional Network

Abstract

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.

References in corpus (8)

• Batch Normalization: Accelerating Deep Network Training by Reducing Internal Covariate Shift
• ABCNet: Attentive Bilateral Contextual Network for Efficient Semantic Segmentation of Fine-Resolution Remote Sensing Images
• A Novel Transformer Based Semantic Segmentation Scheme for Fine-Resolution Remote Sensing Images
• A2-FPN for Semantic Segmentation of Fine-Resolution Remotely Sensed Images
• Multi-Attention-Network for Semantic Segmentation of Fine Resolution Remote Sensing Images
• Multi-stage Attention ResU-Net for Semantic Segmentation of Fine-Resolution Remote Sensing Images
• Dense Dilated Convolutions Merging Network for Land Cover Classification
• MACU-Net for Semantic Segmentation of Fine-Resolution Remotely Sensed Images

Cited by in corpus (4)

• A Novel Transformer Based Semantic Segmentation Scheme for Fine-Resolution Remote Sensing Images
• Building extraction with vision transformer
• ASANet: Asymmetric Semantic Aligning Network for RGB and SAR image land cover classification
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