PCBA-Net: Pyramidal Convolutional Block Attention Network for Synthetic Aperture Radar Image Change Detection

Abstract

Synthetic aperture radar (SAR) imagery change detection (CD) is still a crucial and challenging task. Recently, with the boom of deep learning technologies, many deep learning methods have been presented for SAR CD, and they achieve superior performance to traditional methods. However, most of the available convolutional neural networks (CNN) approaches use diminutive and single convolution kernel, which has a small receptive field and cannot make full use of the context information and some useful detail information of SAR images. In order to address the above drawback, pyramidal convolutional block attention network (PCBA-Net) is proposed for SAR image CD in this study. The proposed PCBA-Net consists of pyramidal convolution (PyConv) and convolutional block attention module (CBAM). PyConv can not only extend the receptive field of input to capture enough context information, but also handles input with incremental kernel sizes in parallel to obtain multi-scale detailed information. Additionally, CBAM is introduced in the PCBA-Net to emphasize crucial information. To verify the performance of our proposed method, six actual SAR datasets are used in the experiments. The results of six real SAR datasets reveal that the performance of our approach outperforms several state-of-the-art methods.

A study on the DAM-EfficientNet hail rapid identification algorithm based on FY-4A_AGRI

“…While these operations effectively preserve the texture and background information of the image, they inevitably lead to the loss of some meaningful features. To address this, we employed 1 1, 3 3, and 5 5 convolutions to replace the max pooling and average pooling 33 . Different convolution kernel sizes enable the capture of features with varying receptive fields: larger kernels extract more global features, whereas smaller kernels capture more local features.…”

An SAR Imaging and Detection Model of Multiple Maritime Targets Based on the Electromagnetic Approach and the Modified CBAM-YOLOv7 Neural Network

“…In the SPPFCSPC module, the serial maximum pooling operations are adopted in the parallel maximum pooling operation, which can raise the computation efficiency while maintaining the receptive field. On the basis of the original YOLOv7 network, the Convolutional Block Attention Module (CBAM) [51][52][53] is employed to highlight the important target features. It can enhance the important features while suppressing non-important features.…”

“…The structure of the CBAM is presented in Figure 11. On the basis of the original YOLOv7 network, the Convolutional Block Attention Module (CBAM) [51][52][53] is employed to highlight the important target features. It can enhance the important features while suppressing non-important features.…”

“…The input feature F is transferred into the feature map of F ∈ R C×H×W , where C refers to the channel number and H and W refer to the height and width of the feature map. The channel attention map M c ∈ R C×1×1 is calculated by the CAM module by squeezing the feature maps of the pooling layers with the activation function given by M c (F) = Signmoid(MLP(AvgPool(F)) + MLP(MaxPool(F))) (15) where Signmoid() refers to the sigmoid activation function [53]. MLP is the parameterized multilayer perceptron function, which can integrate the channel information and output the weighted map.…”

DCAT: Dual Cross-Attention-Based Transformer for Change Detection

“…Nowadays, deep learning-based methods have achieved remarkable success and become the de facto standard of VHR image change detection [10]. In the literature, a large number of attempts have been made to solve CD problems using deep learning techniques [11][12][13][14]. Among these, convolutional neural network (CNN) has emerged as the most commonly used architecture due to its ability to preserve fine detail.…”