Abstract
This paper proposes a multi-temporal image change detection algorithm based on adaptive parameter estimation, which is used to solve the problems of severe interference of coherent speckle noise and the retention of detailed information about changing regions in synthetic aperture radar remote sensing images. The change area in the initial differential image has local consistency and global prominence. By detecting the significant area to locate similar change areas, the coherent speckle noise outside the area can be eliminated. The use of hierarchical FCM clustering to automatically generate training samples can improve the reliability of training samples. In addition, in order to increase the distinction between the changed area and the non-changed area, a sparse automatic encoder is used to extract the changed features and generate a change detection map. Experiments using 4 sets of SAR images show that the algorithm can effectively reduce the effect of speckle noise on detection accuracy, the extraction of changing areas is more complete and meticulous, and the false detection rate is greatly reduced. Since the images in different time phases will be disturbed by weather, clouds, sea water, etc., the target segmentation algorithm can be used to extract the target of interest and highlight the changing area. Principal component analysis and k-means clustering method are used to reduce the influence of isolated pixels, and change information is extracted to obtain different images. The experiment uses four sets of image data of islands and reefs. The experiment proves that the algorithm can well eliminate external interference, improve the accuracy of change detection, and have a good detection effect on the area of islands and reefs. The adaptive parameter estimation plays a good role in the detection of changing areas, and the visual effect is better, which can improve the accuracy of the detection results.
Highlights
With the development of remote sensing technology, the acquisition of high-resolution remote sensing data has become faster and the means are becoming more and more abundant
CLASSIFICATION ANALYSES OF REMOTE SENSING IMAGES In order to verify the proposed saliency guidance and the performance of the sparse autoloader in the SAR image change detection task, the experiment in this chapter is mainly divided into two parts
In order to verify the effectiveness of this method, five remote sensing data sets will be compared with other excellent change detection algorithms, mainly including fuzzy C-means (FCM)-based Extreme Learning Machine with FCM (FCM-ELM), FCM-based Stacked Automatic Encoder wit FCM (FCM-Sparse Automatic Encoder (SAE)), FCM-based Supervised Contractive Auto encoders and Fuzzy Clustering (FCM-SCAE) and significant guidance based on means clustering Detection (Saliencyguided and K-means Clustering, SGK) Four change detection methods, Bern, San Francisco, Red River, and Shihmen Dam multi-temporal remote sensing images use 5 algorithms to perform the change detection experiments
Summary
With the development of remote sensing technology, the acquisition of high-resolution remote sensing data has become faster and the means are becoming more and more abundant. High-resolution remote sensing image change detection technology has important applications in economic development, national defense construction, urban planning, environmental monitoring and other fields [9]. Using the sparse representation technique, the MGS framework can obtain the matrix S reflecting the local relationship of the sample points without determining the parameters, and the matrix S obtained naturally has discriminant information, which can improve the discriminant effect of the algorithm. The sparse matrix obtained by sparse representation often has good discriminating ability, and the time-consuming process of selecting suitable parameter size by classic algorithms such as UDP is uncontrollable From this point of view, it can be said that the SUDP algorithm has a higher time complexity in theory than classic algorithms such as UDP, in practical applications, it’s time performance may be better than the classic algorithm. After obtaining the intra-class sparseness and non-local sparseness, they are unified into a discriminant formula, as showed in equation (12)
Sign-in/Register to view highlights & summary Talk to us
Join us for a 30 min session where you can share your feedback and ask us any queries you have
Schedule a callDisclaimer: All third-party content on this website/platform is and will remain the property of their respective owners and is provided on "as is" basis without any warranties, express or implied. Use of third-party content does not indicate any affiliation, sponsorship with or endorsement by them. Any references to third-party content is to identify the corresponding services and shall be considered fair use under The CopyrightLaw.
