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This paper proposes a coupled region based convolutional neural networks (R-CNN) to automatically detect vehicles in aerial images. Traditional methods are mostly based on sliding-window search, and use handcrafted or shallow-learning based features. They have limited description ability and heavy computational costs. Recently, a series of R-CNN based methods have achieved great success in general...
Landslides occur frequently and it is very meaningful to monitor them in disaster researches. Extracting the landslide from the high resolution satellite with fewer false changes is an important problem to be solved for remote sensing change detection research. In high spatial resolution and multi-temporal remote sensing images, landslides show significant differences with the background in both temporal...
Faster Region based convolutional neural networks (FRCN) has shown great success in object detection in recent years. However, its performance will degrade on densely packed objects in real remote sensing applications. To address this problem, an enhanced deep CNN based method is developed in this paper. Following the common pipeline of “CNN feature extraction + region proposal + Region classification”,...
To reduce the impact of outliers and noises on point pattern matching, a novel point pattern matching algorithm based on local topological characteristic and probabilistic relaxation labeling (LTC-PRL) is proposed in this paper. For each point in a point set, partial adjacent points are used to describe its local topological characteristic. To avoid the defects in angle coding of the existing global...
Image registration is an important research topic in the field of computer vision. Traditional non-rigid image registration methods are based on the correctly matched corresponding landmarks, which usually needs artificial markers. It is a rather challenging and demanding task to locate the accurate position of the points and get the correspondance. In order to get the most correctly matched point...
A novel template-based change detection technique for harbor ship target has been proposed. UnLike classical methods, the proposed approach exploits information by the assistance of template map. Firstly, high resolution optical images are used to make binary templates. Secondly, the template and the two images to be detected are registered separately, through which we obtain the region of interesting...
A novel affine invariant region detector based on the 4th differential invariant (DI4) is proposed in this paper. The detector combines scale-space theory with an autocorrelation matrix. Since it is proved that DI4 is a scale- space selection function, feature points and their characteristic scales are first detected by the local maxima of the normalized DI4 over scale-space. Then, the auto-correlation...
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