Abstract
Deep learning technology has been extensively explored by existing methods to improve the performance of target detection in remote sensing images, due to its powerful feature extraction and representation abilities. However, these methods usually focus on the interior features of the target, but ignore the exterior semantic information around the target, especially the object-level relationship. Consequently, these methods fail to detect and recognize targets in the complex background where multiple objects crowd together. To handle this problem, a diversified context information fusion framework based on convolutional neural network (DCIFF-CNN) is proposed in this paper, which employs the structured object-level relationship to improve the target detection and recognition in complex backgrounds. The DCIFF-CNN is composed of two successive sub-networks, i.e., a multi-scale local context region proposal network (MLC-RPN) and an object-level relationship context target detection network (ORC-TDN). The MLC-RPN relies on the fine-grained details of objects to generate candidate regions in the remote sensing image. Then, the ORC-TDN utilizes the spatial context information of objects to detect and recognize targets by integrating an attentional message integrated module (AMIM) and an object relational structured graph (ORSG). The AMIM is integrated into the feed-forward CNN to highlight the useful object-level context information, while the ORSG builds the relations between a set of objects by processing their appearance features and geometric features. Finally, the target detection method based on DCIFF-CNN effectively represents the interior and exterior information of the target by exploiting both the multiscale local context information and the object-level relationships. Extensive experiments are conducted, and experimental results demonstrate that the proposed DCIFF-CNN method improves the target detection and recognition accuracy in complex backgrounds, showing superiority to other state-of-the-art methods.
Highlights
The target detection and recognition in remote sensing images facilitates a wide range of applications such as airplane detection [1,2,3], road detection [4], building detection [5], land planning [6], and urban monitoring [7]
The remaining seven methods are based on deep learning, which have made a great breakthrough in the field of target detection
FRCNN and you only look once (YOLO) are representative CNN-based methods for the target detection; MSCNN and single shot multiBox detector (SSD) focus on the multiple scales, and rotation-invariant CNN (RICNN) is widely used to assess new methods, especially the target detection method in remote sensing images
Summary
The target detection and recognition in remote sensing images facilitates a wide range of applications such as airplane detection [1,2,3], road detection [4], building detection [5], land planning [6], and urban monitoring [7]. The remote sensing image contains diverse scenes, including man-made targets with drastic boundaries and a large number of landscape objects with similar characteristics to the background. The target in the remote sensing image is usually small in size, which is easy to change with other objects in different environments. It is challenging to detect and recognize targets in remote sensing images due to various scenes with different objects and diverse targets with different features.
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