An Enhanced Global Feature-Guided Network Based on Multiple Filtering Noise Reduction for Remote Sensing Image Compression
Cuiping Shi, Kaijie Shi, Fei Zhu, Zexin Zeng, Mengxiang Ding, Zhan Jin · IEEE Transactions on Geoscience and Remote Sensing · 2024
Remote sensing images obtained at high altitudes often contain complete object or scene information, which makes their global visual features richer compared to natural images. In order to enhance the scope and multilevel characteristics of global visual features of remote sensing images, this article proposes an enhanced global feature-guided network based on multiple filtering noise reduction (GFRNet) for remote sensing image compression. First, a pyramid vision transformer (PVT) is introduced into remote sensing image compression for the first time. Based on this, a PVT compression branch (PVTCB) is designed, which can capture multilevel global visual features through a three-stage pyramid transformer module for image compression (TPTC) and utilizes filters to accurately control the output of TPTC. Second, a quadruple-filtered multicore noise reduction attention module (QFMR-AM) is constructed in the four-stage compression branch (FSCB) for denoising and enhancing multilevel features. Finally, a global visual feature guidance module (GVGM) is designed between FSCB and the four-stage reconstruction decoder (FSRD). By calculating the global visual feature loss LossGVF through GVGM, a novel rate-distortion LossTotal is constructed, making the network more focused on extracting global information. Experimental results show that compared with some advanced methods, the proposed GFRNet achieves better compression performance on multiple evaluation indicators. In addition, the reconstructed images obtained by the proposed GFRNet can provide better classification performance, which further proves that the proposed method helps to preserve more important features of remote sensing images during the compression process.