Water recognition method based on high resolution remote sensing images

Abstract

The invention discloses a water recognition method based on high resolution remote sensing images. The method comprises the steps of (1) image enhancement; (2) data marking; (3) image segmentation andconstruction of a training set; (4) model construction and training on the basis of a convolutional neural network; (5) water recognition by using trained models. Through investigation and analysis of water features, the method for accurately recognizing water and extracting a water part from the remote sensing images is disclosed. The method is based on the convolutional neural network, and spatial information and spectral information of the remote sensing images are fully utilized to conduct deep extraction on image features. The models and parameters of each layer of the convolutional neural network and a training principle and process of the network are introduced in detail, thorough considerations are made in various stages of image processing, and the method can show high accuracy in water recognition.

Description

technical field [0001] The invention belongs to the technical field of image processing and recognition, and in particular relates to a water body recognition method based on high-resolution remote sensing images. Background technique [0002] Water body recognition in high-resolution remote sensing images is an important research topic in different fields, such as lakeside coastal area management, coastline change, flood prediction, and water resource assessment. Timely monitoring of surface water bodies and changes in surface water bodies contributes to The specification of the effective policy. In recent years, the use of remote sensing data to monitor water resources has been widely used. Remote sensing technology has the advantages of short detection distance and wide detection range, and can quickly, repeatedly and accurately obtain water body information and monitor surface water resources. Looking at the research work on water body recognition in remote sensing imag...

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Problems solved by technology

[0003] 1. At present, the research work on water body identification is mainly in two categories: optical remote sensing and machine learning; in terms of optical remote sensing, there are mainly two types of methods to extract water bodies: single-band threshold method and multi-band threshold method, both based on water body and The difference in reflectance between other surface objects is used to extract water body information. However, the reflectance of many buildings and the reflectance of water bodies are the same in some bands, and the accuracy of water body recognition cannot be achieved; the method of machine learning There are also certain applications in water body identification, but for such a complex process as the classification of high-resolution remote sensing images, the use of relatively shallow model structures has certain limitations, while deep structures are more reasonable

[0004] 2. At present, most of the data sources used in the work of water body identification in remote sensing images are multispectral images of Landsat resource satellites, whose spatial resolution is about 30 meters, and the spatial resolution is relatively low. Angle and so on will have an impact on the imaging of remote sensing images, how to carry out corresponding preprocessing to obtain clear, unobstructed high-resolution remote sensing images is also a relatively difficult subject

[0005] 3. Due to the large imaging area of ​​remote sensing images, the complex geomorphic features in the scene, and the different spatial and spectral characteristics of water bodies in different weathers and different geographical regions, how to choose a classification model and how to implement neural network models for water body identification problems Design, transforming the water body recognition problem into an image classification problem is also a topic that needs to be focused on

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