Unlock instant, AI-driven research and patent intelligence for your innovation.
High-resolution remote sensing image residential area extraction method based on edge feature
What is Al technical title?
Al technical title is built by PatSnap Al team. It summarizes the technical point description of the patent document.
An edge feature, high-resolution technology, applied in the field of image processing, can solve problems such as limited automation and many interference factors
Inactive Publication Date: 2015-04-22
CENT SOUTH UNIV
View PDF3 Cites 0 Cited by
Summary
Abstract
Description
Claims
Application Information
AI Technical Summary
This helps you quickly interpret patents by identifying the three key elements:
Problems solved by technology
Method used
Benefits of technology
Problems solved by technology
Analyzing the existing residential area extraction methods, it is found that there are two limitations: on the one hand, most methods are based on supervised classification mechanisms, which require a large number of training samples to ensure classification accuracy, and there are many interference factors, and the degree of automation is limited; on the other hand, , the existing residential area extraction methods emphasize the overall texture, spectrum and other features of the image, but ignore the local features of the residential area, such as edge features
Method used
the structure of the environmentally friendly knitted fabric provided by the present invention; figure 2 Flow chart of the yarn wrapping machine for environmentally friendly knitted fabrics and storage devices; image 3 Is the parameter map of the yarn covering machine
View more
Image
Smart Image Click on the blue labels to locate them in the text.
Viewing Examples
Smart Image
Click on the blue label to locate the original text in one second.
Reading with bidirectional positioning of images and text.
Smart Image
Examples
Experimental program
Comparison scheme
Effect test
Embodiment 1
[0048] Example 1, the residential area is extracted from the remote sensing image with a resolution of 1 m, and the remote sensing image is shown in Figure a in Figure 3;
[0049] Step 1: Image preprocessing;
[0050] For picture a in Figure 3, the median filter is used to remove irrelevant noise on the image, and the edge-preserving Mean Shift algorithm is used to smooth the denoised image. As shown in Figure 3 b, a large amount of texture noise is effectively eliminated. It can be effectively suppressed, and at the same time, the edge details in residential areas are also well preserved;
[0051] Step 2: Edge feature extraction;
[0052] Use the canny operator to perform edge detection on graph b in Figure 3, and follow the steps below to fit all edges into one or more straight line segments:
[0053] The canny edge detection operator was developed by John F.Canny in 1986. It mainly uses the gray gradient on the image for multi-level edge detection;
[0054] 1) Refer to ...
Embodiment 2
[0072] Example 2, the residential area is extracted from the remote sensing image with a resolution of 2m, and the remote sensing image is shown in Figure a in Figure 4;
[0073] Step 1: Image preprocessing;
[0074] For picture a in Figure 4, the median filter is used to remove irrelevant noise on the image, and the Mean Shift algorithm with edge preservation is used to smooth the denoised image. As shown in picture b in Figure 4, a large amount of texture noise is effectively eliminated It can be effectively suppressed, and at the same time, the edge details in residential areas are also well preserved;
[0075] Step 2: Edge feature extraction;
[0076] Use the canny operator to detect the edge of graph b in Figure 4, and follow the steps below to fit all the edges into one or more straight line segments:
[0077] The canny edge detection operator was developed by John F.Canny in 1986. It mainly uses the gray gradient on the image for multi-level edge detection;
[0078] ...
the structure of the environmentally friendly knitted fabric provided by the present invention; figure 2 Flow chart of the yarn wrapping machine for environmentally friendly knitted fabrics and storage devices; image 3 Is the parameter map of the yarn covering machine
Login to View More
PUM
Login to View More
Abstract
The invention discloses a high-resolution remote sensing image residential area extraction method based on an edge feature. The method comprises the following steps: step 1, preprocessing an image; step 2, extracting the edge feature; step 3, establishing a space voting matrix; and step 4, segmenting the steps by an ostu threshold. An ostu threshold segmentation method is adopted for obtaining a self-adaptation segmentation threshold of a residential area voting value and a non-residential area voting value, the space voting matrix is segmented in a binaryzation mode according to the self-adaptation segmentation threshold, and a residential area and a non-residential area in the image are obtained. According to the high-resolution remote sensing image residential area extraction method based on the edge feature, the density of the edge feature is used as a measurement level, a Gaussian function is adopted for establishing the space voting matrix, spatial distance is converted into a voting value, the residential area in a high-resolution remote sensing image is extracted by a space voting mechanism. Therefore, the technological defect that the degree of automation and extraction precision are limited in the extraction of the residential area is effectively overcome, and the high-resolution remote sensing image residential area extraction method based on the edge feature is suitable for analyzing and processing of the high-resolution remote sensing image.
Description
technical field [0001] The invention belongs to the technical field of image processing, and relates to a method for extracting residential areas from high-resolution remote sensing images based on edge features. Background technique [0002] With the gradual improvement of remote sensing image resolution, residential area extraction has become a hot research topic. Accurate and rapid extraction of residential areas can provide important decision-making support for municipal departments such as land management and urban planning when doing land use status investigation and macro-planning. In remote sensing images, residential areas have the characteristics of wide coverage and rich ground object information, and it is an area that changes dynamically with time. Although manual extraction of residential areas can maintain high accuracy, it only relies on manual monitoring and Segmenting and extracting residential areas is not only time-consuming but also costly. Therefore, ...
Claims
the structure of the environmentally friendly knitted fabric provided by the present invention; figure 2 Flow chart of the yarn wrapping machine for environmentally friendly knitted fabrics and storage devices; image 3 Is the parameter map of the yarn covering machine
Login to View More
Application Information
Patent Timeline
Application Date:The date an application was filed.
Publication Date:The date a patent or application was officially published.
First Publication Date:The earliest publication date of a patent with the same application number.
Issue Date:Publication date of the patent grant document.
PCT Entry Date:The Entry date of PCT National Phase.
Estimated Expiry Date:The statutory expiry date of a patent right according to the Patent Law, and it is the longest term of protection that the patent right can achieve without the termination of the patent right due to other reasons(Term extension factor has been taken into account ).
Invalid Date:Actual expiry date is based on effective date or publication date of legal transaction data of invalid patent.
Login to View More 
Login to View More