For the problems of large viewpoint variation, heavy distortion and small overlap area in UAV images registration, this paper proposes a density analysis based method to remove mismatches in putative feature correspondences. Our method uses intra-cluster topological constraints for mismatch filtering, which is based on a density-based hierarchical clustering algorithm. Compared with other methods that perform mismatch filtering based on neighborhood topological relationship, our method is more robust to viewpoint changes both in horizontal and vertical directions. The algorithm in this paper uses a coarse-to-fine strategy, which starts with establishing putative feature correspondences based on local descriptors, such as SIFT, ORB, etc. After that we focus on removing outliers by clustering these feature points and verifying topology consistency of the clusters in different images. We view the feature point matching problem as a correspondence problem of the same visual model in two images, and clustering the feature points based on density can approximate the separation of multiple visual patterns. We tested our algorithm on a UAV image dataset which includes several pairs of images and their ground truth. These image pairs contain viewpoint changes in horizontal, vertical and their mixture which produce problems of low overlap, image distortion and severe outliers. Experiments demonstrate that our method significantly outperforms the state-of-the-art in terms of matching precision.
As the application of UAVs in military and civilian fields becomes more and more widespread, the detection of UAVs in the low-altitude range has also become an important research direction. Compared with radar and visible light detection, infrared technology has become the major UAV detection method with its advantages of all-weather and long range. Most of the current infrared target detection methods are based on convolutional neural networks (CNN), which achieve target detection through feature extraction and feature classification. The performance of all such detection algorithms is highly dependent on their training set. A data set with a large number of samples and wide coverage tends to train a more robust and accurate detector. So, to obtain better detection effects, we perform data augmentation on the infrared UAV dataset by adversarial generative network (GAN). First, we extract the targets from the training set and train a GAN network, using its generator to obtain many new targets which are different from the training set samples, then we randomly extend these targets to the original dataset, and finally we retrain the detectors using the new dataset to achieve better detection. We created an infrared UAV image dataset for our experiments, with only a single target on each image. After data augmentation, multiple UAV targets are randomly generated. The experiments demonstrate that the new dataset trains the model with better detection results. And the GAN data augmentation can be combined with many advanced detectors to make a large improvement in detection.
The integration between polarization and intensity images possessing complementary and discriminative information has
emerged as a new and important research area. On the basis of the consideration that the resulting image has different
clarity and layering requirement for the target and background, we propose a novel fusion method based on
non-subsampled Contourlet transform (NSCT) and fuzzy C-means (FCM) segmentation for IR polarization and light
intensity images. First, the polarization characteristic image is derived from fusion of the degree of polarization (DOP)
and the angle of polarization (AOP) images using local standard variation and abrupt change degree (ACD) combined
criteria. Then, the polarization characteristic image is segmented with FCM algorithm. Meanwhile, the two source
images are respectively decomposed by NSCT. The regional energy-weighted and similarity measure are adopted to
combine the low-frequency sub-band coefficients of the object. The high-frequency sub-band coefficients of the object
boundaries are integrated through the maximum selection rule. In addition, the high-frequency sub-band coefficients of
internal objects are integrated by utilizing local variation, matching measure and region feature weighting. The weighted
average and maximum rules are employed independently in fusing the low-frequency and high-frequency components of
the background. Finally, an inverse NSCT operation is accomplished and the final fused image is obtained. The
experimental results illustrate that the proposed IR polarization image fusion algorithm can yield an improved
performance in terms of the contrast between artificial target and cluttered background and a more detailed
representation of the depicted scene.
Access to the requested content is limited to institutions that have purchased or subscribe to SPIE eBooks.
You are receiving this notice because your organization may not have SPIE eBooks access.*
*Shibboleth/Open Athens users─please
sign in
to access your institution's subscriptions.
To obtain this item, you may purchase the complete book in print or electronic format on
SPIE.org.
INSTITUTIONAL Select your institution to access the SPIE Digital Library.
PERSONAL Sign in with your SPIE account to access your personal subscriptions or to use specific features such as save to my library, sign up for alerts, save searches, etc.