Remote Targets Recognition Based on Adaptive Weighting Feature Dictionaries and Joint Sparse Representations
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RESEARCH ARTICLE
Remote Targets Recognition Based on Adaptive Weighting Feature Dictionaries and Joint Sparse Representations Wei Wang1 • Junwu Chen1 • Ji Li1
•
Xin Wang1
Received: 14 November 2017 / Accepted: 8 August 2018 Ó Indian Society of Remote Sensing 2018
Abstract With the improvement in resolution, more and more useful information is contained in the space of remote sensing images, which makes the processing of remote sensing data become more complex, and it is easy to cause the curse of dimensionality and the poor recognition effect. In this paper, a remote target recognition approach named AJRC is proposed, which uses joint feature dictionary for sparse representation based on different feature information for adaptive weighting. Firstly, the features of the images are extracted to calculate the contribution weight of each eigenvalue in sparse representation, and each eigenvalue contribution weight is calculated in sparse representation. Through the adaptive method, the contribution ability of each feature value in sparse representation is strengthened, and new atoms are formed to construct feature dictionary, which makes the dictionary more discriminative. Then, the common features of each category image and the private features of a single image are extracted from the feature vector, and a joint dictionary is formed to represent the test image sparse and recognize the output of the target. Aiming at the problem that the target visual contrast difference, the low resolution and the rotation of the target with different angles, the experiment is carried out by different feature extraction methods. At the same time, we use the PCA method to reduce the feature dictionary in order to avoid dimensionality. Experiments show that compared with the existing SRC method and JSM method, this method has better recognition rate. Keywords Remote sensing target Sparse representation Feature transform Joint sparse (JS)
Introduction Remote sensing technique, as a technique for ground observation, can provide the real-time, multispectral, high spectral and wide range information. Along with the rapid development of space industry in recent years, the use of remote sensing images to detect and identify important goals has been widely used in all aspects of social life. The robust accurately classification of remote sensing image
& Ji Li [email protected] & Xin Wang [email protected] 1
Hunan Provincial Key Laboratory of Intelligent Processing of Big Data on Transportation, School of Computer and Communication Engineering, Changsha University of Science and Technology, Changsha 410114, Hunan, People’s Republic of China
has become a hot spot in the field of target recognition (Cui and Prasad 2013). In particular, the sparse representation and compression sensing algorithm in recent years provide better support to the recognition technology of remote sensing image. Sparse representation can be regarded as an important signal and image representation model in the field of computer vision. Essentially, the reco
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