Abstract
The current research on multiple information fusion of remote sensing images is mainly aimed at remote sensing images of specific satellite sensors, and cannot be extended to other types of data source images. For high-resolution remote sensing images, when its surface coverage changes significantly, most of the mainstream algorithms are difficult to restore satisfactorily. The algorithm proposed in this paper combines the sparse representation and the spectral, spatial, and temporal features of remote sensing images for the first time to solve the above problems. The algorithm proposed in this paper first simulates the human visual mechanism, and obtains the spatial, spectral, and temporal features of the remote sensing image through the spatial spectral dictionary learning and the time-varying weight learning model. Secondly, local constraints are added to the extraction of temporal features to obtain temporal and geographical change information of heterogeneous remote sensing images. Then, a sparse representation model combining space-spectrum-time features is proposed to extract features of high-resolution remote sensing images. Finally, based on the VGG-16 network, this paper proposes a target recognition network with deep fully convolutional network, and uses the extracted feature map as the input of the target recognition network to realize the target recognition of the remote sensing image. Experimental results show that the method proposed in this paper can improve the accuracy of target recognition and improve the accuracy of recognition.
Highlights
High-resolution remote sensing image target recognition is an important part of information extraction and processing of high-resolution ground observation system and automatic recognition system [1]–[3]
Based on the idea of multiple information fusion, this paper combines the sparse representation and the spectral, spatial, and temporal features of remote sensing images, and proposes an image extraction and recognition network based on multiple information fusion
The technical contributions of our paper can be concluded as follows: This paper proposes an image extraction and recognition network based on multiple information fusion
Summary
High-resolution remote sensing image target recognition is an important part of information extraction and processing of high-resolution ground observation system and automatic recognition system [1]–[3]. The associate editor coordinating the review of this manuscript and approving it for publication was Zhihan Lv. high spatial resolution and improve the recognition accuracy and target extraction reliability are of great significance. Target detection in remote sensing images is of great significance in both military and civilian fields [6], [7]. Due to the difference in the appearance of the target and the interference of the complex background and noise in the remote sensing image, in the remote sensing image with high spatial resolution, target detection is usually difficult. Munoz-Mari et al [11] used target contours, Zernike moments, and wavelet features, combined with support vector machines to detect aircraft targets from remotely sensed
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