Multiclass geospatial object detection is a vital fundamental task for many remote sensing applications. However, it still faces several challenges in very high-resolution (VHR) images in remote sensing, such as the ambiguity of object appearance and the complexity of spatial distribution. In this letter, we propose a novel Siamese graph embedding network (SGEN) that leverages the spatial and semantic information to jointly extract the high-level feature representation for object detection. The main purpose of our SGEN is to learn an embedding discriminative feature space that strengthens the interclass compactness while alleviating the intraclass separability. Specifically, we first design a novel contrastive loss in terms of spatial dependence and semantic correspondence for graph similarity metric learning (ML). Then, the SGEN architecture is adopted for spatial and semantic similarity learning by training the novel contrastive loss function. The SGEN model contains two-stream graph convolutional networks (GCNs) for ML, which is helpful to capture the discriminative features. At last, these extracted features with high spatial and semantic discrimination are served to improve the performance of object detection. The comprehensive evaluations on a combined data set consisting of two public object detection data sets demonstrate the effectiveness of the proposed method.
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Siamese Graph Embedding Network for Object Detection in Remote Sensing Images
Semantic Scholar · Environmental Science · 2021
Abstract
Multiclass geospatial object detection is a vital fundamental task for many remote sensing applications. However, it still faces several challenges in very high-resolution (VHR) images in remote sensing, such as the ambiguity of object appearance and the complexity of spatial distribution. In this letter, we propose a novel Siamese graph embedding network (SGEN) that leverages the spatial and semantic information to jointly extract the high-level feature representation for object detection. The main purpose of our SGEN is to learn an embedding discriminative feature space that strengthens the interclass compactness while alleviating the intraclass separability. Specifically, we first design a novel contrastive loss in terms of spatial dependence and semantic correspondence for graph similarity metric learning (ML). Then, the SGEN architecture is adopted for spatial and semantic similarity learning by training the novel contrastive loss function. The SGEN model contains two-stream graph convolutional networks (GCNs) for ML, which is helpful to capture the discriminative features. At last, these extracted features with high spatial and semantic discrimination are served to improve the performance of object detection. The comprehensive evaluations on a combined data set consisting of two public object detection data sets demonstrate the effectiveness of the proposed method.