Abstract
Abstract. Object detection in high resolution remote sensing images is a fundamental and challenging problem in the field of remote sensing imagery analysis for civil and military application due to the complex neighboring environments, which can cause the recognition algorithms to mistake irrelevant ground objects for target objects. Deep Convolution Neural Network(DCNN) is the hotspot in object detection for its powerful ability of feature extraction and has achieved state-of-the-art results in Computer Vision. Common pipeline of object detection based on DCNN consists of region proposal, CNN feature extraction, region classification and post processing. YOLO model frames object detection as a regression problem, using a single CNN predicts bounding boxes and class probabilities in an end-to-end way and make the predict faster. In this paper, a YOLO based model is used for object detection in high resolution sensing images. The experiments on NWPU VHR-10 dataset and our airport/airplane dataset gain from GoogleEarth show that, compare with the common pipeline, the proposed model speeds up the detection process and have good accuracy.
Highlights
1.1 General InstructionsObject detection is an important task for understanding highresolution images and has very important military value
Concept of convolution layer introduced from Convolutional neural network (CNN) uses two methods to greatly reduce the number of parameters: local receptive field and parameter sharing
This work addresses the problem of rapid object detection for high-resolution remote sensing image with CNNs
Summary
Object detection is an important task for understanding highresolution images and has very important military value. Since the 1980s, Object detection in remote sensing image is widely studied, mainly using shallow features that were hand-engineered by skilled people who have experience in the field and often required domain-expertise. This means that if the conditions change even slightly, a framework which works well in a given task may fail in another task. Concept of convolution layer introduced from Convolutional neural network (CNN) uses two methods to greatly reduce the number of parameters: local receptive field and parameter sharing. The researches show that the CNN model can be generalized to the field of remote sensing imagery and obtain better results than the traditional methods
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