Ground Object Segmentation With Residual Blocks and Attention
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Solution Overview
Problem
Existing RS image segmentation methods face challenges in accurately segmenting ground objects due to blurred boundaries and low contrast, which are exacerbated by the lack of spatial correlation information in simple convolution operations, leading to reduced recognition precision.
Innovation Solution
A ground object segmentation method utilizing a U-Net neural network enhanced with a residual module and attention mechanism, incorporating residual blocks and hybrid attention mechanisms to connect long-distance context information and enhance adaptive capabilities.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If a simple convolution operation is used for feature extraction, then the global feature of the image can be extracted, but the spatial correlation information is lost and redundant pixels are weighted equally, leading to reduced recognition precision
Solution Approach 1:
The patent divides the feature extraction process into multiple parallel convolutional paths (e.g., different kernel sizes like 3x3, 5x5, 7x7) that operate simultaneously on the input feature map. Each path extracts features at different receptive fields, and their outputs are concatenated to preserve both local spatial details and global contextual information, thereby resolving the loss of spatial correlation while maintaining global feature extraction capability
Solution Approach 2:
The patent introduces depthwise separable convolutions that separate the spatial convolution from the channel mixing operation. This dimensional separation allows the network to process spatial correlations independently for each channel while still capturing global features through the subsequent pointwise convolution, thus preserving spatial information that would otherwise be lost in standard convolutions
2Measurement precision
If the network depth is increased to improve segmentation precision, then better feature representation can be achieved, but the training difficulty and computational complexity increase significantly
Solution Approach 1:
The patent introduces residual connections as intermediary pathways that allow gradients to flow directly through the network without passing through multiple nonlinear transformations. These skip connections connect intermediate layers directly to later layers, enabling the training of very deep networks by mitigating the vanishing gradient problem and reducing optimization difficulty, thus allowing increased network depth for better segmentation precision
Solution Approach 2:
The network is segmented into multiple functional blocks (e.g., encoder blocks, decoder blocks, bottleneck blocks) where each block contains a specific number of convolutional layers. This modular segmentation allows the overall deep network to be constructed by stacking these manageable units, making the complex deep network easier to train while achieving high segmentation precision through sufficient depth
Data Source
AI summary
The present disclosure provides a ground object segmentation method based on a residual module and an attention mechanism, and a related apparatus, and relates to the field of remote sensing (RS) ground object segmentation technologies. The method includes the following steps: obtaining a to-be-segmented RS image; and inputting the to-be-segmented RS image into a trained ground object segmentation model to obtain a ground object segmentation result, where the ground object segmentation model is a network model obtained based on a U-Net neural network and with reference to the residual module and an attention module. In the present disclosure, a U-Net model with reference to a residual network structure and the attention mechanism is used.