Remote Sensing Image Scene Classification Based on Head-Tail Global Joint Dual Attention Discrimination Network
نویسندگان
چکیده
High-resolution, multi-channel remote sensing images have complex spatial layout and channel structure. Traditional Convolutional Neural Networks (CNNs) methods do not make full use of the features images, ignoring contextual relevance globality, resulting in insufficient discrimination limiting accuracy scene classification. To address these issues, we propose a discriminative network HCA-TSA based on head-tail global joint dual attention mechanism. Considering characteristics between dimension dimension, module Head-Channel Attention (HCA) Tail-Spatial (TSA) are proposed. This uses context information ability Gated Recurrent Unit (GRU) structure to learn output importance weights different channels features, pay more salient image, ignore insignificant thus improve power feature representation. The proposed can be connected any benchmark CNNs, whole trained end-to-end. Through comprehensive comparative experiment three public datasets with large differences, AID, UC-Merced HSRS-SC, highest classification accuracies experimental results 97.68%, 99.41% 98.38%, respectively. show that method effectively discriminate scenarios obtain competitive results, which verifies effectiveness method.
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ژورنال
عنوان ژورنال: IEEE Access
سال: 2023
ISSN: ['2169-3536']
DOI: https://doi.org/10.1109/access.2023.3306083