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A Relation-Augmented Fully Convolutional Network for Semantic Segmentation in Aerial Scenes
Lichao Mou, Yuansheng Hua, Xiao Xiang Zhu
TL;DR
Convolutional segmentation struggles to model contextual spatial relations because its effective operations are local, while prior approaches may miss long-range dependencies. The paper adds spatial and channel relation modules to FCNs to learn global relationships and produce augmented features. On aerial-image segmentation, the relation-augmented network achieves improved reported performance over the baseline, including a highest mean F1 score of 88.54%.
Problem
Convolution layers have local valid receptive fields, and prior relation methods may inadequately capture long-range spatial relations needed in aerial-scene segmentation.
Method
The paper appends plug-and-play spatial and channel relation modules to FCNs to learn global relations and generate relation-augmented features.
Results
88.54% mean F1 score is reported for the highest-performing design, along with 89.23% overall accuracy.
Takeaways & Limitations
Spatial- and channel-augmented features carry high-level semantics and global relations that reinforce semantic-segmentation performance in aerial scenes.
Abstract
from arXiv · showhide
Most current semantic segmentation approaches fall back on deep convolutional neural networks (CNNs). However, their use of convolution operations with local receptive fields causes failures in modeling contextual spatial relations. Prior works have sought to address this issue by using graphical models or spatial propagation modules in networks. But such models often fail to capture long-range spatial relationships between entities, which leads to spatially fragmented predictions. Moreover, recent works have demonstrated that channel-wise information also acts a pivotal part in CNNs. In this work, we introduce two simple yet effective network units, the spatial relation module and the channel relation module, to learn and reason about global relationships between any two spatial positions or feature maps, and then produce relation-augmented feature representations. The spatial and channel relation modules are general and extensible, and can be used in a plug-and-play fashion with the existing fully convolutional network (FCN) framework. We evaluate relation module-equipped networks on semantic segmentation tasks using two aerial image datasets, which fundamentally depend on long-range spatial relational reasoning. The networks achieve very competitive results, bringing significant improvements over baselines.
1. Introduction
The paper identifies contextual spatial relations as a limitation of convolutional segmentation, especially for aerial scenes with long-range dependencies. It proposes spatial and channel relation modules that augment FCNs with global relational reasoning.
- Convolution layers cannot model spatial relations effectively because their valid receptive fields are local.These relations include semantic similarity, compatibility, and incompatibility between image regions.
- Existing graphical-model and spatial-propagation approaches may inadequately capture long-range relations in aerial scenes.Their implicit chain propagation depends heavily on long-term memorization, while aerial images often contain long-range spatial relationships.
- The proposed network increases FCN representation capacity by learning relationships between observations in convolved images.It produces relation-augmented feature representations for aerial-image semantic segmentation.
- A spatial relation module and a channel relation module explicitly model global relations in spatial and channel domains.The modules are appended to FCN feature maps and generate spatial- and channel-augmented features.
- The relation modules are evaluated through extensive ablation studies.
2. Related Work
Prior aerial-segmentation work includes handcrafted features, deep CNNs, graphical models, and spatial propagation networks. This paper distinguishes its approach by explicitly modeling spatial and channel relations for both short- and long-range reasoning.
- Semantic segmentation of aerial imagery: Aerial-image segmentation research has used handcrafted visual features, mid-level semantic features, and deep CNNs for end-to-end parsing.The area has also been represented by contests involving overhead imagery.
- Graphical models: Graphical-model methods use CRFs, differentiable CRF modules, or structural cues such as object contours to improve segmentation.These methods include both post-processing and jointly trained network components.
- Spatial propagation networks: Spatial propagation methods learn spatial affinities or propagation networks, but some approaches are not trainable end-to-end because they use nondifferentiable spectral embedding.
- Relation networks: Relation networks have been applied to visual question answering, video classification, and object detection by modeling relationships among entities.This paper adapts the relational-reasoning motivation to spatial and channel relations in CNNs.
- Unlike graphical-model and spatial-propagation methods, the proposed approach explicitly models spatial and channel relations for short- and long-range relational reasoning.
3. Our Approach
The approach augments an FCN with spatial and channel relation modules that model global dependencies and preserve relation-enhanced representations for aerial-image segmentation.
- Network Overview: The network uses VGG-16 to extract multi-level features, then processes conv3, conv4, and conv5 outputs with relation modules before producing full-resolution segmentation maps.The resulting features are projected with 1 × 1 convolutions, upsampled, and element-wise added.
- Spatial Relation Module: The spatial relation module models global relationships between feature-map vectors at pairs of spatial positions.It uses learned 1 × 1 projections, matrix multiplication, and ReLU to produce fine-grained spatial relation features.
- Spatial Relation Module: Spatial relation features are concatenated with the original feature maps, preserving high-level semantic features while adding global spatial information.The concatenation avoids relying on spatial relations alone and retains the original representation.
- Channel Relation Module: The channel relation module models global dependencies between feature-map channels to improve feature discriminability when objects have similar distributions but different channel dimensionality.It applies global average pooling before learned projections and pairwise channel-relation computation.
- Channel Relation Module: A softmax-based channel relation map emphasizes class-relevant channel dependencies, and matrix multiplication embeds these relations into the original feature maps.The resulting features retain initial channel properties together with global inter-channel correlations.
- Module Integration: The spatial and channel relation modules are combined through serial or parallel integration to jointly aggregate their relation-augmented features.Serial integration feeds one module's output into the other, whereas parallel integration computes the two branches separately.
4. Experiments
Experiments on Vaihingen and Potsdam evaluate relation modules, their integration strategies, and comparisons with existing segmentation methods. Results show improved performance and more accurate predictions in scenes requiring long-range spatial reasoning.
- Experimental setup: Experiments use the ISPRS Vaihingen and Potsdam aerial image semantic segmentation datasets to evaluate long-range relation modeling.Aerial imagery is selected because object distributions and spatial relations are diverse and complicated.
- Ablation study: Ablations on Vaihingen show 87.24% mean F1 for the channel relation module, a 3.50% gain, while the spatial relation module provides a 4.62% gain over the baseline.The experiments also compare parallel and serial integration strategies.
- Ablation study: Combining spatial and channel relation features further improves performance; parallel integration adds 1.26% and 0.14% mean F1 relative to the single-module variants.The serial design achieves the highest mean F1 of 88.54% and overall accuracy of 89.23%.
- Qualitative results: Qualitative results show RA-FCN recovering ambiguous impervious surfaces, identifying dispersed objects, resolving low-vegetation ambiguity, and removing building outliers more accurately than competing methods.The reported examples attribute these improvements to exploiting remote or global spatial relations.
- Potsdam results: On Potsdam, the spatial relation module improves mean F1 by 2.25% over FCN-dCRF and 2.67% over SCNN, while serial integration adds 1.39% and 1.54% across reported metrics.The supplied passage names mean F1, mean IoU, and overall accuracy for the serial-integration increments.
5. Conclusion
The paper introduces spatial and channel relation modules for relational reasoning in aerial-scene semantic segmentation. Ablation experiments suggest both modules learn global relations, while their operation remains insufficiently understood.
- The spatial relation module and channel relation module enable relational reasoning for semantic segmentation in aerial scenes.
- Ablation experiments on aerial datasets with long-range spatial relations suggest that both modules learn global information between objects and feature maps.
- The authors identify understanding how the relation modules work for segmentation as preliminary and leave it for future work.