A2-FPN for Semantic Segmentation of Fine-Resolution Remotely Sensed Images

Abstract

Semantic segmentation using fine-resolution remotely sensed images plays a critical role in many practical applications, such as urban planning, environmental protection, natural and anthropogenic landscape monitoring, etc. However, the automation of semantic segmentation, i.e., automatic categorization/labeling and segmentation is still a challenging task, particularly for fine-resolution images with huge spatial and spectral complexity. Addressing such a problem represents an exciting research field, which paves the way for scene-level landscape pattern analysis and decision making. In this paper, we propose an approach for automatic land segmentation based on the Feature Pyramid Network (FPN). As a classic architecture, FPN can build a feature pyramid with high-level semantics throughout. However, intrinsic defects in feature extraction and fusion hinder FPN from further aggregating more discriminative features. Hence, we propose an Attention Aggregation Module (AAM) to enhance multi-scale feature learning through attention-guided feature aggregation. Based on FPN and AAM, a novel framework named Attention Aggregation Feature Pyramid Network (A2-FPN) is developed for semantic segmentation of fine-resolution remotely sensed images. Extensive experiments conducted on three datasets demonstrate the effectiveness of our A2 -FPN in segmentation accuracy. Code is available at https://github.com/lironui/A2-FPN.

References in corpus (9)

• Encoder-Decoder with Atrous Separable Convolution for Semantic Image Segmentation
• SpecAugment: A Simple Data Augmentation Method for Automatic Speech Recognition
• U${}^2$-Net: Going Deeper with Nested U-Structure for Salient Object Detection
• MultiResUNet : Rethinking the U-Net Architecture for Multimodal Biomedical Image Segmentation
• Deep Visual Attention Prediction
• Improving Semantic Segmentation of Aerial Images Using Patch-based Attention
• 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 (10)

• A Novel Transformer Based Semantic Segmentation Scheme for Fine-Resolution Remote Sensing Images
• Building extraction with vision transformer
• Land Cover Classification from Remote Sensing Images Based on Multi-Scale Fully Convolutional Network
• UNetMamba: An Efficient UNet-Like Mamba for Semantic Segmentation of High-Resolution Remote Sensing Images
• Rethinking Scanning Strategies with Vision Mamba in Semantic Segmentation of Remote Sensing Imagery: An Experimental Study
• Globally Scalable Glacier Mapping by Deep Learning Matches Expert Delineation Accuracy
• SBSS: Stacking-Based Semantic Segmentation Framework for Very High Resolution Remote Sensing Image
• MetaSegNet: Metadata-collaborative Vision-Language Representation Learning for Semantic Segmentation of Remote Sensing Images
• Progressive Cross Attention Network for Flood Segmentation using Multispectral Satellite Imagery
• SemanticSugarBeets: A Multi-Task Framework and Dataset for Inspecting Harvest and Storage Characteristics of Sugar Beets