Research On Target Detection In Remote Sensing Images

Along with the development of remote sensing, target detection in remote sensing images attracts more and more attentions in recent years. Target detection in remote sensing images is of great importance in a variety of applications such as resources survey, disaster evaluation, and military target detection etc. For the affection of land temperature, atmosphere attenuation and remote sensing systems, the interferences in remote sensing are more severe than common surveillance systems on land, which are the key challenge of target detection in remote sensing images.In this article we will investigate target detection in remote sensing images including anomaly detection and target detection. The following works are carried out.1. Background models in target detection algorithms and their disadavantages are analyzed, based on which a new model is proposed. First, the distribution of gray scale images and hyperspectral images is discussed. And the derivation of Gaussian model and real background distribution is also evaluated. Through analyzing the limitation of Gussian model, we present a new anomaly detection algorithm by transforming background into Gaussian space, and the Gaussian transformation based model is also extended to the hyperspectal images.2. Target models in target detection algorithms are reviewed and an application of a target spectral model is addressed. The target models in both gray scale images and hyperspectral images are discussed especially hyperspectal target models such as multidimensional Gaussian model, singular value decomposition model, and autoregression model. By comparing the similarity of target models in hyperspectral images and temporal profiles of sequence images, we apply a target spectral model to temporal based target detection in infrared image sequence and present a new temporal profile based small moving target detection algorithm in infrared image sequence. Experiment with real infrared image sequence has proved the validity of the new approach.3. Anomaly detection algorithms are discussed. First, a detection algorithm based on gray scale cluster is analyzed, and a background segmentation model is proposed to avoid the influence of complex background, with which a new algorithm is developed by introducing 2D Markov model to segment complex background into homogenous regions. Then the characteristic of hyperspectral image is analyzed as well as the limitation of the RX algorithm. The background segmentation model is also employed. We have extended the 2D texture segmentation model to 3D to model hyperspectral images, and obtained a new texture segmentation based anomaly detection algorithm in hyperspectral images.4. Target detection algorithms are examined. According to the relationship between the resolution of imaging systems and targets size, several full-pixel detection algorithms and sub-pixel algorithms are discussed especially the Kelly detection algorithm, the adaptive cosine estimator algorithm, and the adaptive background subspace algorithm. Employing the idea of the background segmentation model, a new background-segmentation-based full-pixel target detection algorithm is presented. And three background-segmentation-based sub-pixel target detection algorithms are also presented including background segmentation based Kelly algorithm, background segmentation based adaptive cosine estimator algorithm, and background segmentation based adaptive background subspace algorithm.