Foreground-Aware Relation Network for Geospatial Object Segmentation in High Spatial Resolution Remote Sensing Imagery (2011.09766v1)

Abstract: Geospatial object segmentation, as a particular semantic segmentation task, always faces with larger-scale variation, larger intra-class variance of background, and foreground-background imbalance in the high spatial resolution (HSR) remote sensing imagery. However, general semantic segmentation methods mainly focus on scale variation in the natural scene, with inadequate consideration of the other two problems that usually happen in the large area earth observation scene. In this paper, we argue that the problems lie on the lack of foreground modeling and propose a foreground-aware relation network (FarSeg) from the perspectives of relation-based and optimization-based foreground modeling, to alleviate the above two problems. From perspective of relation, FarSeg enhances the discrimination of foreground features via foreground-correlated contexts associated by learning foreground-scene relation. Meanwhile, from perspective of optimization, a foreground-aware optimization is proposed to focus on foreground examples and hard examples of background during training for a balanced optimization. The experimental results obtained using a large scale dataset suggest that the proposed method is superior to the state-of-the-art general semantic segmentation methods and achieves a better trade-off between speed and accuracy. Code has been made available at: \url{https://github.com/Z-Zheng/FarSeg}.

• The paper introduces FarSeg, a novel approach that explicitly models foreground-scene relations to overcome segmentation challenges in high-resolution remote sensing imagery.
• It employs a dual strategy of relation-based feature enhancement and foreground-aware optimization to improve segmentation accuracy and computational efficiency.
• Empirical results on datasets like iSAID demonstrate superior performance, paving the way for real-time urban management and precise geospatial data extraction.

Foreground-Aware Relation Network for Geospatial Object Segmentation

The paper under discussion presents a novel approach to geospatial object segmentation in high spatial resolution (HSR) remote sensing imagery, leveraging a model termed the Foreground-Aware Relation Network (FarSeg). Object segmentation in HSR images poses unique challenges compared to natural scene images, particularly due to significant scale variation, foreground-background imbalance, and intra-class variance in background regions. The proposed method seeks to tackle these challenges through an enhanced modeling strategy that places emphasis on foreground elements, integrating both relation-based and optimization-based foreground modeling components.

The core proposition of this research is to address the inadequate consideration of foreground modeling in existing semantic segmentation strategies, particularly in HSR imagery contexts. FarSeg improves segmentation accuracy by explicitly modeling foreground-scene relations and applying a foreground-aware optimization process during network training. This involves two main methodological innovations:

1. Foreground-Scene Relation Modeling: The FarSeg model introduces a relation module designed to enhance foreground feature discrimination by associating these with contextually relevant scenes. This module aims to mitigate the false alarms arising from complex background variations typical in HSR images by leveraging the symbiotic relationship between the foreground objects and their larger scene context.
2. Foreground-Aware Optimization (F-A Optimization): The model adopts an optimization strategy that prioritizes foreground examples, particularly difficult background instances, enabling a more balanced training process. This strategy incorporates techniques to adjust the gradient contributions dynamically, thus addressing the foreground-background imbalance by focusing on hard-to-class examples and reducing the weightage of easier examples.

FarSeg's implementation benefits from a multi-branch encoder-decoder architecture, featuring a feature pyramid network (FPN) to address multi-scale representation challenges and a light-weight decoder. Together, these components facilitate efficient additional processing for the foreground-scene relation enhancement without significant computational overhead.

The empirical results exhibited by FarSeg illustrate substantial improvements in terms of segmentation performance and computational efficiency. On large-scale datasets such as the iSAID, the proposed model surpasses existing state-of-the-art methods. The model achieves a favorable balance between processing speed and segmentation accuracy, reflecting its potential applicability in real-time remote sensing applications.

The implications of this research are multifold. Practically, the improvement in segmentation accuracy and speed can significantly enhance urban management and monitoring systems that rely on precise geospatial data extraction. Theoretically, the focus on foreground context relations and foreground-focused optimization provides new directions for advancing semantic segmentation models, particularly for imagery data from remote sensing platforms.

Looking forward, it would be beneficial to explore the integration of FarSeg with other modalities and extend its application to different domains within geospatial analysis. Additionally, the robustness of the model can be further evaluated across varied environments and sensing conditions to determine its adaptability and performance stability. This research could pave the way for more nuanced and contextually aware models in geospatial data processing and beyond.