Now the most advanced satellite has achieved an accuracy of near 0.1-meter per pixel. Therefore, when the satellite scan over the lands and oceans in a swift speed, a huge amount of image data will be produced, which can not be read and interpreted by human in a short time, and developing an automatical and efficient target detection method becomes an imperative and essential task. Aimed at the deficiencies of the existing methods in object search and location, feature extraction and classification in remote sensing images, we proposed several efficient methods base on deep neural networks, and build two large databases for detection of aircraft and vehicle respectively. The Experiments verify the significance of our new methods. The main contributes of this paper are: 1. For improving the searching efficiency, we proposed a new thresholding method based on edge smooth property, a orientation computing method based on projection curve and a new sliding window method based on multi-thresholding images of gray scale or gradient. The new method has a 12-20 higher efficiency than the traditional sliding window method. 2. A new histogram of oriented curvatures (HOC) feature is proposed for target detection. Compared with histogram of oriented gradient(HOG) and local binary pattern(LBP), HOC showed more robustness and discriminability. In experiments on LFW,MNIST and vehicle detection database, HOC+LBP outperforms HOG+LBP, HOG, LBP with significant margins. In order to reduce the training time of Nonlinear SVM, we presented an algorithm to estimate the optimal kernel parameter based on the optimal margin. The theoretical analysis and experiments show the validness of the algorithm. 3. A hybrid deep convolutional neural network (HDNN) is proposed. Compared with deep convolutional neural network (DNN), HDNN is capable of extracting multi-scale features, more powerful ability to adapt to scale varieties of object. HDNN can extract more subtle features than DNN. The experiments on vehicle database showed that HDNN deduced the false alarm rate of DNN by 40\%. In the database of MNIST, HDNN gets a new record. 4. A parallel deep convolutional neural network (PDNN) is proposed, which was capable of fusing different types of features by different branches. PDNN can avoid the interfere of different types of images in the feature extraction process. This made PDNN improve the performance of the object detector. The vehicle detection experiment showed that PDNN...
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