Identification method of oil depot target in remote sensing image

Abstract

The present invention discloses an identification method of an oil depot target in a remote sensing image. The method comprises the steps of firstly calculating the phase spectrum significance of a whole scene, and extracting all interested areas possibly containing the target in the scene according to the phase spectrum significance; in the feature extraction, adopting a local regression nuclear model to calculate the local structural features of the interested areas point by point, and generating a feature descriptor capable of describing the structure of the target; at a target detection stage, carrying out the similarity measurement by the cosine similarity, calculating the similarities of the interested areas and an oil depot sample image, utilizing the positive and negative sample distinguishing ability of the feature descriptor and the features of the similarity surfaces to construct a decision network having the adaptive capability, obtaining the preliminary results of the target detection by the decision network, and removing the redundant preliminary results by a non-maxima suppression algorithm, thereby obtaining a final target detection result. A universal detection method of the oil depot target in the remote sensing image provided by the present invention is good in multi-scale and multi-view angle target identification effect.

Description

technical field [0001] The invention belongs to the technical field of remote sensing image target recognition, and more particularly relates to a method for recognizing an oil depot target in a remote sensing image. Background technique [0002] High-resolution remote sensing images provide a wealth of detailed information, making it possible to identify various specific targets; however, noise interference, seasonal weather, shadows, light intensity, occlusion and other factors will cause fluctuations in the structure and texture information of internal details of the target , which brings difficulties to the recognition of high-resolution images. [0003] The methods for target detection of oil depots in remote sensing images in the prior art include: target detection methods based on deep learning, target recognition and detection methods based on prior knowledge, and target detection methods based on models; It has a high dependence on the number of samples, and in mos...

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Problems solved by technology

[0002] High-resolution remote sensing images provide a wealth of detailed information, making it possible to identify various specific targets; however, noise interference, seasonal weather, shadows, light intensity, occlusion and other factors will cause fluctuations in the structure and texture information of internal details of the target , which brings difficulties to the recognition of high-resolution images

[0003] The methods for target detection of oil depots in remote sensing images in the prior art include: target detection methods based on deep learning, target recognition and detection methods based on prior knowledge, and target detection methods based on models; and the number of samples have a very high dependence, and in most cases, the recognition of remote sensing image oil depot targets can only provide simple data image sources; the target recognition method based on prior knowledge is to use the prior knowledge of the target such as aircraft The priori features such as the mean, variance, and invariant moments of the target are used as the basis for judging the position of the target. It is necessary to accurately express the characteristics of the target, and a decision-making method with adaptive ability is required. When the prior knowledge expression is not accurate enough or the decision-making method is not enough In the case of perfection, the accuracy of target detection is low; the model-based method is to extract target features through a large number of experiments, mark the model parameters of the target to generate hypotheses and predict the target characteristics, and measure the background or target model in actual use. Then match with the predicted characteristics and reach a certain degree of similarity, that is, it is considered to be the target; this model-based target detection method has high requirements for the accuracy and fault tolerance of the modeling, which is very important for remote sensing images in complex scenes. The identification of oil depot targets is very difficult

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