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Object detection in high resolution remote sensing images is a fundamental and challenging problem in the field of remote sensing imagery analysis for civil and military application due to the complex neighboring environments, which can cause the recognition algorithms to mistake irrelevant ground objects for target objects. Deep Convolution Neural Network(DCNN) is the hotspot in object detection for its powerful ability of feature extraction and has achieved state-of-the-art results in Computer Vision. Common pipeline of object detection based on DCNN consists of region proposal, CNN feature extraction, region classification and post processing. YOLO model frames object detection as a regression problem, using a single CNN predicts bounding boxes and class probabilities in an end-to-end way and make the predict faster. In this paper, a YOLO based model is used for object detection in high resolution sensing images. The experiments on NWPU VHR-10 dataset and our airport/airplane dataset gain from GoogleEarth show that, compare with the common pipeline, the proposed model speeds up the detection process and have good accuracy.

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In this paper, a novel small target detection algorithm based on robust master analysis (RPCA) is proposed to solve the problem of small target difficult to detection in single infrared image. Because of the background matrix is a low rank matrix and the target matrix is a sparse matrix, small target detection can be formulated as an optimization problem of recovering low-rank and sparse matrices. RPCA method is used to restore the background matrix and targets matrix, and then choose the inexact augmented Lagrange multipliers to solve RPCA model which is faster. At last, two simulation experiments result indicate that can adapt to a variety of single image and the timeliness is good for small matrix.