The invention relates to a remote sensing image semantic segmentation method and device. The method comprises the steps of obtaining a to-be-segmented remote sensing image; inputting the to-be-segmented remote sensing image into a pre-established self-attention multi-scale feature aggregation network, and obtaining an initial prediction result of the to-be-segmented remote sensing image output bythe pre-established self-attention multi-scale feature aggregation network; up-sampling the initial prediction result of the remote sensing image to be segmented to the image size of the remote sensing image to be segmented, and obtaining the final prediction result of the remote sensing image to be segmented; according to the method provided by the invention, the association between modal features and spatial information can be effectively enhanced, the multi-scale target downward text information perception capability is improved, and a finer semantic annotation result is obtained.

Remote sensing image semantic segmentation method and device

The invention discloses a kaleidoscope convolution network construction method and belongs to a deep learning algorithm network According to the prior art, a void filter is used for expanding the receptive field of a convolutional network, and when the void ratio is set to be too large, a boundary effect influences feature capture The invention aims to solve the above problem in the prior art The method comprises the following steps that: a kaleidoscope filter is constructed, the outer contour of the kaleidoscope filter is made to be a square, the square is equally divided into four segmentation units, and nine primary sampling points are determined according to the four corners of the four segmentation units; three secondary sampling points are determined in each segmentation unit according to the intersection point positions of kaleidoscope type petals; a kaleidoscope cascade spatial pyramid pooling module is constructed based on kaleidoscope filters of different sizes; and finally, Xception is adopted as a backbone network, and the kaleidoscope cascaded spatial pyramid pooling module is assembled on a decoder layer, so that a kaleidoscope convolutional network can be formedWith the method of the invention adopted, the shape features of an object can be better captured

Kaleidoscope convolution network construction method

The invention discloses a new coronal pneumonia CT image lesion segmentation image processing method based on a lesion weighting loss function. The method comprises the following steps: S1, constructing an FPN model by taking ResNet50 as a basic network to realize lesion segmentation; S2, proposing a focus weighted loss function to train the model; S3, training the segmentation model by using themanually annotated image; and S4, utilizing the trained model to realize automatic segmentation image processing of the new coronal pneumonia lesion. The invention provides a method for training a segmentation model based on a focus weighted loss function. The loss function can consider the central actual change region and the edge region of the lesion respectively, so that the segmentation of theedge region of the lesion is emphasized during model training, the segmentation precision of the edge region of the lesion is ensured, and the overall lesion segmentation precision is further improved.

New coronal pneumonia CT image lesion segmentation image processing method based on lesion weighting loss function

The invention discloses a multi-scale image semantic segmentation method. The method comprises the following steps: obtaining a to-be-segmented image and a corresponding label; constructing a full convolutional deep neural network, wherein the full convolutional deep neural network comprises a convolution module, a hole convolution module, a pyramid pooling module, a 1 * 1 * depth convolution layer and a deconvolution structure; setting hole convolution as channel-by-channel operation, and utilizing low-scale, medium-scale and high-scale characteristics in a targeted mode; training the full convolutional deep neural network, establishing a loss function, and determining parameters of the full convolutional deep neural network by training the sample image; and inputting the to-be-segmentedimage into the trained full convolutional deep neural network to obtain a semantic segmentation result. By means of the method, the image semantic segmentation problem with complex details, holes andlarge targets can be well solved while the calculated amount and the parameter number are reduced, and the consistency of category labels can be reserved while the target edges can be well segmented.

Multi-scale image semantic segmentation method

Handle everyday research tasks with reliable, citation-backed results

Your personal Research Agent to handle research tasks with citation-backed results

Popular Tasks used by Researchers

How can I help with your research?

Meet SciSpace

Get more enhanced response by uploading the PDFs you want me to reference.

No relevant PDFs in your library

SciSpace is the AI research assistant for academics. Run systematic literature reviews on 280M+ papers, and write papers with cited sources. Trusted by 1M+ students, PhDs & researchers.

SciSpace | AI for Research

Analyze PDFs

Code & Manuscripts

Funding & Grants

Literature & Patents

Medical & Clinical Data

Systematic Review

Visualize & Present

Web & Data

Build a Google Scholar-like website for your research.

Build a website

Create charts and images for your research

Create a Chart

Write a paper for submission to a journal

Draft a manuscript

Patent Search

Design eye-catching scientific posters in minutes.

Scientific Poster Generation

Systematic Literature Review

One task is running at the moment. Your messages will be shown right after.

Drag and drop or click here to browse

Loved by <highlight>1 million+</highlight> researchers

Extract a list of specific topics and their sources from unstructured text

Topics

Compare and analyze relevant papers that matches with your search

Papers

Get insights from PDFs and bookmarked papers from your library

My library

Recent searches

Try searching for:

Catch AI-generated content in scholarly and non-scholarly content

{ai} Detector

Ai Writer

Get PDF Summaries, highlighted text explanations 

Chat with PDF

Effortlessly create in-text citations and bibliographies in APA and 2,500 other formats

Citation generator

Get explanations, summaries, and answers on academic papers

Ease up your research workflow with {scispace}'s cohort of exciting AI tools

Elevate your academic writing skills and convey your ideas the way you want

Paraphraser

Explore our range of reading and writing tools

Your file is being prepared and should be ready in a few minutes. If it's a large file, it might take a bit longer. You can close this window, and we'll email you the file when it's done.

You have reached a maximum limit of <strong>{limit}</strong> columns in the table. Remove at least <strong>1</strong> column to add or create another one.

Xu Yong

Author Tools

Chat about Author

Papers

Multi-scale image semantic segmentation method