CAD-Net: A Context-Aware Detection Network for Objects in Remote Sensing Imagery

Abstract

Accurate and robust detection of multi-class objects in optical remote sensing images is essential to many real-world applications such as urban planning, traffic control, searching and rescuing, etc. However, state-of-the-art object detection techniques designed for images captured using ground-level sensors usually experience a sharp performance drop when directly applied to remote sensing images, largely due to the object appearance differences in remote sensing images in term of sparse texture, low contrast, arbitrary orientations, large scale variations, etc. This paper presents a novel object detection network (CAD-Net) that exploits attention-modulated features as well as global and local contexts to address the new challenges in detecting objects from remote sensing images. The proposed CAD-Net learns global and local contexts of objects by capturing their correlations with the global scene (at scene-level) and the local neighboring objects or features (at object-level), respectively. In addition, it designs a spatial-and-scale-aware attention module that guides the network to focus on more informative regions and features as well as more appropriate feature scales. Experiments over two publicly available object detection datasets for remote sensing images demonstrate that the proposed CAD-Net achieves superior detection performance. The implementation codes will be made publicly available for facilitating future researches.

References in corpus (4)

• A Survey on Object Detection in Optical Remote Sensing Images
• Automatic Ship Detection of Remote Sensing Images from Google Earth in Complex Scenes Based on Multi-Scale Rotation Dense Feature Pyramid Networks
• Locality and Structure Regularized Low Rank Representation for Hyperspectral Image Classification
• Position Detection and Direction Prediction for Arbitrary-Oriented Ships via Multitask Rotation Region Convolutional Neural Network

Cited by in corpus (12)

• Gliding vertex on the horizontal bounding box for multi-oriented object detection
• Towards Large-Scale Small Object Detection: Survey and Benchmarks
• CFC-Net: A Critical Feature Capturing Network for Arbitrary-Oriented Object Detection in Remote Sensing Images
• An Empirical Study of Remote Sensing Pretraining
• Meta-DETR: Image-Level Few-Shot Detection with Inter-Class Correlation Exploitation
• G-Rep: Gaussian Representation for Arbitrary-Oriented Object Detection
• Oriented Bounding Boxes for Small and Freely Rotated Objects
• LO-Det: Lightweight Oriented Object Detection in Remote Sensing Images
• Learning to Reduce Information Bottleneck for Object Detection in Aerial Images
• Rotated Object Detection via Scale-invariant Mahalanobis Distance in Aerial Images
• Anchor Retouching via Model Interaction for Robust Object Detection in Aerial Images
• UESegNet: Context Aware Unconstrained ROI Segmentation Networks for Ear Biometric