Multi-Scale Feature Fusion Based on PVTv2 for Deep Hash Remote Sensing Image Retrieval

Abstract

For high-resolution remote sensing image retrieval tasks, single-scale features cannot fully express the complexity of the image information. Due to the large volume of remote sensing images, retrieval requires extensive memory and time. Hence, the problem of how to organically fuse multi-scale features and enhance retrieval efficiency is yet to be resolved. We propose an end-to-end deep hash remote sensing image retrieval model (PVTA_MSF) by fusing multi-scale features based on the Pyramid Vision Transformer network (PVTv2). We construct the multi-scale feature fusion module (MSF) by using a global attention mechanism and a multi-head self-attention mechanism to reduce background interference and enhance the representation capability of image features. Deformable convolution is introduced to address the challenge posed by varying target orientations. Moreover, an intra-class similarity (ICS) loss is proposed to enhance the discriminative capability of the hash feature by minimizing the distance among images of the same category. The experimental results show that, compared with other state-of-the-art methods, the proposed hash feature could yield an excellent representation of remote sensing images and improve remote sensing image retrieval accuracy. The proposed hash feature can gain an increase of 4.2% and 1.6% in terms of mAP on the UC Merced and NWPU-RESISC45 datasets, respectively, in comparison with other methods.

IFE-Net: An Integrated Feature Extraction Network for Single-Image Dehazing

“…Considering the potential cumulative error caused via the separate estimation of atmospheric light and the transmission map, IFE-Net unifies atmospheric light and transmission maps as one parameter to directly obtain a clean image. In addition, attention mechanisms have been widely applied in the design of neural networks [19,[33][34][35][36], which can provide additional flexibility in the network. Inspired by these works and considering the different weights of features in different regions, a feature attention mechanism module called attention mechanism (AM) is designed in the network, which processes different types of information more effectively.…”