Research On Water Identification With Hollow-spectrum Features And Deep Learning Based On Big Data

With the continuous development of remote sensing technology,high-resolution remote sensing image data also provides a richer texture,geometric structure,and spatial distribution information for the identification of surface water bodies.At the same time,due to the increase of geometric progression of remote sensing data in the era of big data and the enhancement of object heterogeneity in the surface area,the traditional pixel-by-pixel method is used to extract water information,which not only ignores the correlation between neighboring pixels in the image.Sex,can not meet the current large-scale application needs.In recent years,with the great achievements of deep learning algorithms in computer vision,natural language processing,text classification,and target detection,it has also provided a new idea for remote sensing water body recognition.How to use existing water feature extraction methods,distributed computing and deep learning algorithms to classify and recognize water in remote sensing images has become an important research topic.This paper analyzes the papers of other researchers and proposes the following innovations:First of all,in order to improve the quality of remote sensing images,the original image was sharpened,filtered,defogged,corrected and calibrated,and multi-spectral band images were combined with pixel-level images with high resolution.Secondly,the spatial and spectral features of the surface water body information are analyzed in depth,and the methods for extracting and selecting the joint features of the spectrum and space are proposed.The spatially constrained algorithms are designed to further fully utilize the adjacent pixel-by-pixel water recognition methods.The correlation between pixels.Finally,due to the large amount of remote sensing image data,a distributed storage and computing platform was built to block and calculate the image data.In order to overcome the shortcomings of human participation in feature extraction,analysis,and threshold setting difficulties,this paper proposes a variety of in-depth neural network automatic water recognition models based on deep learning framework.The joint features are used as input to perform the overall performance of the proposed model.Assessment.And using the excellent learning feature of "deep learning" algorithm,a stable water information extraction model was constructed to realize the mining and identification of water information.Experimental results show that the water identification model combined with the spatial spectral features and deep learning algorithm proposed in this paper is superior to the traditional support vector machine(SVM)and BP neural network in performance.