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In recent years, deep-learning-based methods for remote sensing image interpretation have undergone rapid development, due to the increasing amount of image data and the advanced techniques of machine learning. The abundant spatial and contextual information within the images is helpful to improve the interpretation performance. However, the contextual information is ignored by most of the current deep-learning-based methods. In this letter, we explore the contextual information by taking advantage of the object-to-object relationship. Then, the feature representation of the individual objects can be enhanced. To be specific, we first build a knowledge database which reveals the relationship between different categories and generate a region-to-region graph that indicates the relationship between different regions of interest (RoIs). For each RoI, the features of its related regions are then combined with the original region features, and the fused features are finally used for object detection. The experiments conducted on a public ten-class object detection dataset demonstrate the validity of the proposed method. [ABSTRACT FROM AUTHOR]

Due to the ability of powerful feature representation, deep-learning-based object detection has attracted considerable research attention, and many methods have been proposed for remote sensing images. However, there are still some problems that need to be addressed. In this paper, a novel and effective detection framework based on faster region-based convolutional neural network is designed. Specifically, first, in order to locate the boundaries of large objects and find the missing small objects, DetNet is incorporated into the detection framework as the backbone network. DetNet fixes the spatial resolution in deep layers and adopts dilated bottleneck with convolution projection to increase the divergence between input and output feature maps. Then, the proposed framework uses the backbone network to extract the scene features and region features simultaneously, which are both mapped to feature vectors and then fused together. The feature fusion operation can improve the feature representation of the generated region. Last, to improve the performance of localization, the cascade structure is adopted in the framework. The cascade structure has multiple phases and every phase has independent classifier and regressor. The results obtained from the previous phase are used as the regions of interest in the next phase. Therefore, the multiphase detector can increase the detection accuracy phase by phase. Comprehensive evaluations on a public ten-class object detection dataset demonstrate the effectiveness of the proposed framework. Moreover, ablation experiments are also implemented to show the respective influence of different parts of the framework on the performance improvement. [ABSTRACT FROM AUTHOR]

Nowadays, object detection methods based on deep learning are applied more and more to the interpretation of optical remote sensing images. However, the complex background and the wide range of object sizes in remote sensing images increase the difficulty of object detection. In this paper, we improve the detection performance by combining the attention information, and generate adaptive anchor boxes based on the attention map. Specifically, the attention mechanism is introduced into the proposed method to enhance the features of the object regions while reducing the influence of the background. The generated attention map is then used to obtain diverse and adaptable anchor boxes using the guided anchoring method. The generated anchor boxes can match better with the scene and the objects, compared with the traditional proposal boxes. Finally, the modulated feature adaptation module is applied to transform the feature maps to adapt to the diverse anchor boxes. Comprehensive evaluations on the DIOR dataset demonstrate the superiority of the proposed method over the state-of-the-art methods, such as RetinaNet, FCOS and CornerNet. The mean average precision of the proposed method is 4.5% higher than the feature pyramid network. In addition, the ablation experiments are also implemented to further analyze the respective influence of different blocks on the performance improvement. [ABSTRACT FROM AUTHOR]

Geospatial object detection from high spatial resolution (HSR) remote sensing imagery is a heated and challenging problem in the field of automatic image interpretation. Despite convolutional neural networks (CNNs) having facilitated the development in this domain, the computation efficiency under real-time application and the accurate positioning on relatively small objects in HSR images are two noticeable obstacles which have largely restricted the performance of detection methods. To tackle the above issues, we first introduce semantic segmentation-aware CNN features to activate the detection feature maps from the lowest level layer. In conjunction with this segmentation branch, another module which consists of several global activation blocks is proposed to enrich the semantic information of feature maps from higher level layers. Then, these two parts are integrated and deployed into the original single shot detection framework. Finally, we use the modified multi-scale feature maps with enriched semantics and multi-task training strategy to achieve end-to-end detection with high efficiency. Extensive experiments and comprehensive evaluations on a publicly available 10-class object detection dataset have demonstrated the superiority of the presented method. [ABSTRACT FROM AUTHOR]