Vehicle Detection In Satellite Images Research Articles

Automated vehicle detection in satellite images using deep learning

Automatic detection of small objects such as vehicles in satellite images is a very challenging task, due to the complexity of the background, vehicles colors, the large size of ground sample distance (GSD) for satellite images and jamming caused by buildings and trees. Many methods were proposed for this task by using handcrafted features (such as a Histogram of an Oriented Gradient, Local Binary Pattern, Scale-Invariant Feature Transform, etc.) along with support vector machine classifier, however, Convolutional Neural Networks (CNN) have proved to be potentially more effective. In this paper, we use two advanced deep learning frameworks, Faster Region CNN (Faster R-CNN) and Single Shot Multi-Box (SSD) based on (CNN) with Inception-V2 as a feature map generator instead of VGG-16, to detect vehicles through Transfer Learning, and making an experimental analysis comparison between the two models. Experimental results on the test dataset demonstrate the effectiveness and efficiency of the proposed methods.

Weakly supervised vehicle detection in satellite images via multi-instance discriminative learning

Vehicle detection in satellite images has attracted extensive research interest with widespreading application potentials. The main challenge lies in the difficulty of labeling sufficient training instances (vehicle rectangles) across all resolutions and imaging conditions of satellite images, which degenerates the performance of vehicle detectors trained correspondingly. To tackle this challenge, in this paper we propose an intelligent and labor-light scheme for large-scale training of vehicle detectors. Our scheme only requires region-level group annotation, i.e. whether this region contains vehicle(s) or not, without explicitly labeling the bounding boxes of vehicles. To this end, a novel weakly supervised, multi-instance learning algorithm is designed to learn instance-wise vehicle detectors from such “weak labels”. In particular, a density estimator is firstly adopted to estimate the density map of vehicle instances from the positive regions. Then, a multi-instance SVM is trained to classify and locate vehicle instances from this map. We have carried out extensive experiments on a large-scale satellite image collection that contains various resolutions and imaging conditions. We have demonstrated that the proposed scheme has achieved superior performance by comparing to a set of state-of-the-art and alternative approaches.

Robust vehicle detection by combining deep features with exemplar classification

Very recently, vehicle detection in satellite images has become an emerging research topic with various applications ranging from military to commercial systems. However, it retains as an open problem, mainly due to the complex variations in imaging conditions, object intra-class changes, as well as due to its low-resolution. Coming with the rapid advances in deep learning for feature representation, in this paper we investigate the possibility to exploit deep neural features towards robust vehicle detection. In addition, along with the rapid growth in the data volume, new classification methodology is also demanded to explicitly handle the intra-class variations. In this paper, we propose a vehicle detection framework, which combines Deep Convolutional Neural Network (DNN) based feature learning with Exemplar-SVMs (E-SVMS) based, robust instance classifier to achieve robust vehicle detection in satellite images. In particular, we adopt DNN to learn discriminative image features, which has a high learning capacity. In our practice, the leverage of DNN has achieve significant performance boost by comparing to a serial of handcraft designed features. In addition, we adopt E-SVMs based robust classifier to further improve the classification robustness, which can be considered as an instance-specific metric learning scheme. By conducting extensive experiments with comparisons to a serial of state-of-the-art and alternative works, we further show that the combination of both schemes can benefit from each other to jointly improve the detection accuracy and effectiveness.

Vehicle Detection in Satellite Images by Incorporating Objectness and Convolutional Neural Network

Vehicle Detection in Satellite Images by Incorporating Objectness and Convolutional Neural Network

Vehicle Detection in Satellite Images by Hybrid Deep Convolutional Neural Networks

Detecting small objects such as vehicles in satellite images is a difficult problem. Many features (such as histogram of oriented gradient, local binary pattern, scale-invariant featuretransform, etc.) have been used to improve the performance of object detection, but mostly in simple environments such as those on roads. Kembhavi et al. proposed that no satisfactory accuracy has been achieved in complex environments such as the City of San Francisco. Deep convolutional neural networks (DNNs) can learn rich features from the training data automatically and has achieved state-of-the-art performance in many image classification databases. Though the DNN has shown robustness to distortion, it only extracts features of the same scale, and hence is insufficient to tolerate large-scale variance of object. In this letter, we present a hybrid DNN (HDNN), by dividing the maps of the last convolutional layer and the max-pooling layer of DNN into multiple blocks of variable receptive field sizes or max-pooling field sizes, to enable the HDNN to extract variable-scale features. Comparative experimental results indicate that our proposed HDNN significantly outperforms the traditional DNN on vehicle detection.