Semantic Segmentation of High-Resolution Remote Sensing Images Based on Sparse Self-Attention

Sun, Li; Zou, Huanxin; Wei, Juan; Li, Meilin; Cao, Xu; He, Shitian; Liu, Shuo

doi:10.1109/igarss46834.2022.9883343

Cited by 2 publications

(1 citation statement)

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“…The concept of the self-attention mechanism was initially introduced in the year 2017, primarily for the purpose of processing information in text form [27]. After that, the self-attention module expanded into other fields, such as image classification [28] and semantic segmentation [29]. Self-attention module-based methods are capable of capturing the interrelationships and dependencies among image pixels, thereby enhancing the precision of semantic segmentation [30].…”

Section: Introductionmentioning

confidence: 99%

Carrier-Free Ultra-Wideband Sensor Target Recognition in the Jungle Environment

Li,

Zhang,

Zhu

et al. 2024

Remote Sensing

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Carrier-free ultra-wideband sensors have high penetrability anti-jamming solid ability, which is not easily affected by the external environment, such as weather. Also, it has good performance in the complex jungle environment. In this paper, we propose a jungle vehicle identification system based on a carrier-free ultra-wideband sensor. Firstly, a composite jungle environment with the target vehicle is modeled. From this model, the simulation obtains time-domain echoes under the excitation of carrier-free ultra-wideband sensor signals in different orientations. Secondly, the time-domain signals are transformed into MTF images through the Markov transfer field to show the statistical characteristics of the time-domain echoes. At the same time, we propose an improved RepVGG network. The structure of the RepVGG network contains five stages, which consist of several RepVGG Blocks. Each RepVGG Block is created by combining convolutional kernels of different sizes using a weighted sum. We add the self-attention module to the output of stage 0 to improve the ability to extract the features of the MTF map and better capture the complex relationship between characteristics during training. In addition, a self-attention module is added before the linear layer classification output in stage 4 to improve the classification accuracy of the network. Moreover, a combined cross-entropy loss and sparsity penalty loss function helps enhance the performance and accuracy of the network. The experimental results show that the system can recognize jungle vehicle targets well.

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Section: Introductionmentioning

confidence: 99%