Hybrid Attention-Based Encoder–Decoder Fully Convolutional Network for PolSAR Image Classification

Abstract

Recently, methods based on convolutional neural networks (CNNs) achieve superior performance in polarimetric synthetic aperture radar (PolSAR) image classification. However, the current CNN-based classifiers follow patch-based frameworks, which need input images to be divided into overlapping patches. Consequently, these classification approaches have the drawback of requiring repeated calculations and only relying on local information. In addition, the receptive field size in conventional CNN-based methods is fixed, which limits the potential to extract features. In this paper, a hybrid attention-based encoder–decoder fully convolutional network (HA-EDNet) is presented for PolSAR classification. Unlike traditional CNN-based approaches, the encoder–decoder fully convolutional network (EDNet) can use an arbitrary-size image as input without dividing. Then, the output is the whole image classification result. Meanwhile, the self-attention module is used to establish global spatial dependence and extract context characteristics, which can improve the performance of classification. Moreover, an attention-based selective kernel module (SK module) is included in the network. In the module, softmax attention is employed to fuse several branches with different receptive field sizes. Consequently, the module can capture features with different scales and further boost classification accuracy. The experiment results demonstrate that the HA-EDNet achieves superior performance compared to CNN-based and traditional fully convolutional network methods.

BSDSNet: Dual-Stream Feature Extraction Network Based on Segment Anything Model for Synthetic Aperture Radar Land Cover Classification

“…TP + TN TP + TN + FP + FN (16) where TP, TN, FP, and FN represent true positive, true negative, false positive, and false negative, respectively.…”

“…By inputting depth features extracted from convolution and multilayer attention operations, it generates context-aware graph structures to predict land cover types. Additionally, Fang et al [16] proposed a fully convolutional network (FCN), which exhibited an excellent classification performance. Subsequently, the emergence of Unet [17] revolutionized land cover classification tasks, attaining a superior performance across nearly all categories of land cover.…”

SDF2Net: Shallow to Deep Feature Fusion Network for PolSAR Image Classification

“…In recent years, attention-based techniques have also been widely employed in PolSAR image classification to enhance the model's ability to emphasize informative features and suppress less relevant ones by allocating more attention to the most important features rather than treating the entire input uniformly, which in turn improve the classification performance [29].…”

Global Polarimetric Synthetic Aperture Radar Image Segmentation with Data Augmentation and Hybrid Architecture Model

“…Neural networks are then trained to recognize these small patches, thereby obtaining land cover classes across the entire image [28][29][30][31]. The second strategy, known as direct segmentation, entails feeding the PolSAR image into a neural network to directly segment the image, and then the trained model assigns each pixel to a specific land cover class [32][33][34]. Patch-based classification offers a high level of flexibility, and the model is easy to train, but it is sensitive to noise, and one patch represents a class, which will lead to the loss of spatial information because features spanning multiple patches cannot be accurately captured, resulting in classification errors.…”