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Nowadays, with the development of remote sensing technology, very-high-resolution (VHR) remote sensing image object detection technology attracts more and more attention. However, various challenges still exist in remote sensing image object detection field, such as the complex and varied appearances, the expensive manual annotation, and difficult in fast detecting the large scene image. In this paper, we propose a multi-scale image block-level fully convolutional neural network (MIF-CNN) for remote sensing image object detection, which can solve the above problems to a certain degree. First, the training data sets which only require class labels and do not need the bounding box label can reduce the spend of manual annotation during training. Second, the MIF-CNN is designed to extract the multi-scale-based high-level feature which can better represent the various types of objects. The image block-level fully convolutional network contributes to improving computing efficiency and can directly detect any size of the input image, including a large scene remote sensing image. In the testing phase, the large scene image is directly input to MIF-CNN model, and the detection results are generated from the MIF-CNN output maps which are improved by the proposed bounding boxes modification strategy and local re-recognized strategy. The experiments on NWPU VHR-10 [1] and two Airports data sets demonstrate the effectiveness of the proposed method.

Multi-Scale Image Block-Level F-CNN for Remote Sensing Images Object Detection