Automatic airplane detection in aerial imagery has a variety of applications. Two of the major challenges in this area are variations in scale and direction of the airplanes. In order to solve these challenges, we present a rotation-and-scale invariant airplane proposal generator. This proposal generator is developed based on the symmetric and regular boundaries of airplanes from the top view called symmetric line segments (SLS). Then, the generated proposals are used to train a deep convolutional neural network for removing non-airplane proposals. Since each airplane can have multiple SLS proposals, where some of them are not in the direction of the fuselage, we collect all proposals correspond to one ground truth as a positive bag and the others as the negative instances. To have multiple instance deep learning, we modify the training approach of the network to learn from each positive bag at least one instance as well as all negative instances. Finally, we employ non-maximum suppression to remove duplicate detections. Our experiments on NWPU VHR-10 dataset show that our method is a promising approach for automatic airplane detection in very high resolution images. Moreover, the proposed algorithm can estimate the direction of the airplanes using box-level annotations as an extra achievement.
The boundary of an airplane in top view is quite distinguished from other objects. So, simple shape descriptors can be good features if the boundary of the objects extracted carefully. In @cite_3 some simple geometrical features such as area, perimeter, roundness, and aspect ratio are extracted from the boundaries as the features. Some more meaningful geometrical features are used in @cite_5 . However, accurate boundary extraction is a challenging problem yet. ,cheng2016object Textural features are used more frequently in this field. Histogram of oriented gradients (HOG) was introduced in @cite_42 and used in many airplane detection studies such as @cite_48 @cite_53 @cite_38 . Bag-of-words (BoW) is another useful feature extractor that established by @cite_37 for airplane detection. Gabor filters, Local Binary Patterns (LBP), and Haar-like features are some other common feature extractors that a review of them is collected in @cite_13 .
@cite_38 @cite_37 @cite_48 @cite_53 @cite_42 @cite_3 @cite_5 @cite_13
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Object detection in very high resolution (VHR) optical remote sensing images is one of the most fundamental but challenging problems in the field of remote sensing image analysis. As object detection is usually carried out in feature space, effective feature representation is very important to construct a high-performance object detection system. During the last decades, a great deal of effort has been made to develop various feature representations for the detection of different types of objects. Among various features developed for visual object detection, the histogram of oriented gradients (HOG) feature is maybe one of the most popular features that has been successfully applied to computer vision community. However, although the HOG feature has achieved great success in nature scene images, it is problematic to directly use it for object detection in optical remote sensing images because it is difficult to effectively handle the problem of object rotation variations. To explore a possible solution to the problem, this paper proposes a novel method to learn rotation-invariant HOG (RIHOG) features for object detection in optical remote sensing images. This is achieved by learning a rotation-invariant transformation model via optimizing a new objective function, which constrains the training samples before and after rotation to share the similar features to achieve rotation-invariance. In the experiments, we evaluate the proposed method on a publicly available 10-class VHR geospatial object detection dataset and comprehensive comparisons with state-of-the-arts demonstrate the effectiveness the proposed method. Object detection is an important task in very high-resolution remote sensing image analysis. Traditional detection approaches are often not sufficiently robust in dealing with the variations of targets and sometimes suffer from limited training samples. In this paper, we tackle these two problems by proposing a novel method for object detection based on structural feature description and query expansion. The feature description combines both local and global information of objects. After initial feature extraction from a query image and representative samples, these descriptors are updated through an augmentation process to better describe the object of interest. The object detection step is implemented using a ranking support vector machine (SVM), which converts the detection task to a ranking query task. The ranking SVM is first trained on a small subset of training data with samples automatically ranked based on similarities to the query image. Then, a novel query expansion method is introduced to update the initial object model by active learning with human inputs on ranking of image pairs. Once the query expansion process is completed, which is determined by measuring entropy changes, the model is then applied to the whole target data set in which objects in different classes shall be detected. We evaluate the proposed method on high-resolution satellite images and demonstrate its clear advantages over several other object detection methods. Abstract With a wide range of applications in different fields like airport management and military warfare, airplane detection has been a critical part in remote sensing image processing. In this paper, we focus on the airplane detection in large (usually larger than 10,000 × 10,000 pixels) panchromatic image (PI) with high spatial resolution (usually superior to 1 m), and propose an automated airplane detection system. The system contains two main modules: In the first module, line segment detector (LSD) is applied to detect runway of an airport, thus segmenting airport region in a large PI and reducing airplane candidates. The other is used to detect airplanes in the segmented airport regions. We first use circle frequency filter to further locating airplane candidates, then accomplish precise detection task by combining Histograms of Oriented Gradients (HOG) descriptor and AdaBoost algorithm. Therefore, besides a practical airplane detection system, the other contributions of our approach include the following three parts: (1) it locates runway of an airport with LSD; (2) it classifies airplane candidates by using circle frequency filter; (3) it precisely detects airplanes by exploiting HOG and AdaBoost algorithm. Experimental results on real data indicate the efficacy of the proposed system. The airport and airplane detection rates are higher than 94 and 96 , respectively. Meanwhile, the false alarm rate of airplane detection is superior to 0.05 . Moreover, the whole time cost for handling a large PI is less than 2.5 min, which implies that the system is a satisfactory choice for airplane detection in practical applications. Abstract Automatically detecting objects with complex appearance and arbitrary orientations in remote sensing imagery (RSI) is a big challenge. To explore a possible solution to the problem, this paper develops an object detection framework using a discriminatively trained mixture model. It is mainly composed of two stages: model training and object detection. In the model training stage, multi-scale histogram of oriented gradients (HOG) feature pyramids of all training samples are constructed. A mixture of multi-scale deformable part-based models is then trained for each object category by training a latent Support Vector Machine (SVM), where each part-based model is composed of a coarse root filter, a set of higher resolution part filters, and a set of deformation models. In the object detection stage, given a test imagery, its multi-scale HOG feature pyramid is firstly constructed. Then, object detection is performed by computing and thresholding the response of the mixture model. The quantitative comparisons with state-of-the-art approaches on two datasets demonstrate the effectiveness of the developed framework. Top space experts from around the world have collaborated to produce this comprehensive, authoritative, and clearly illustrated reference guide to the fast growing, multi-billion dollar field of satellite applications and space communications. This handbook, done under the auspices of the International Space University based in France, addresses not only system technologies but also examines market dynamics, technical standards and regulatory constraints. 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No other reference in print today provides such a comprehensive and in-depth guide to all forms of application satellites, including small sats as used by countries just beginning space application programs. Feature plays a very important role in the area of image processing. Before getting features, various image preprocessing techniques like binarization, thresholding, resizing, normalization etc. are applied on the sampled image. After that, feature extraction techniques are applied to get features that will be useful in classifying and recognition of images. Feature extraction techniques are helpful in various image processing applications e.g. character recognition. As features define the behavior of an image, they show its place in terms of storage taken, efficiency in classification and obviously in time consumption also. Here in this paper, we are going to discuss various types of features, feature extraction techniques and explaining in what scenario, which features extraction technique, will be better. Hereby in this paper, we are going to refer features and feature extraction methods in case of character recognition application.