Remote Sensing Image Defogging Networks Based on Dual Self-Attention Boost Residual Octave Convolution

Abstract

Remote sensing images have been widely used in military, national defense, disaster emergency response, ecological environment monitoring, among other applications. However, fog always causes definition of remote sensing images to decrease. The performance of traditional image defogging methods relies on the fog-related prior knowledge, but they cannot always accurately obtain the scene depth information used in the defogging process. Existing deep learning-based image defogging methods often perform well, but they mainly focus on defogging ordinary outdoor foggy images rather than remote sensing images. Due to the different imaging mechanisms used in ordinary outdoor images and remote sensing images, fog residue may exist in the defogged remote sensing images obtained by existing deep learning-based image defogging methods. Therefore, this paper proposes remote sensing image defogging networks based on dual self-attention boost residual octave convolution (DOC). Residual octave convolution (residual OctConv) is used to decompose a source image into high- and low-frequency components. During the extraction of feature maps, high- and low-frequency components are processed by convolution operations, respectively. The entire network structure is mainly composed of encoding and decoding stages. The feature maps of each network layer in the encoding stage are passed to the corresponding network layer in the decoding stage. The dual self-attention module is applied to the feature enhancement of the output feature maps of the encoding stage, thereby obtaining the refined feature maps. The strengthen-operate-subtract (SOS) boosted module is used to fuse the refined feature maps of each network layer with the upsampling feature maps from the corresponding decoding stage. Compared with existing image defogging methods, comparative experimental results confirm the proposed method improves both visual effects and objective indicators to varying degrees and effectively enhances the definition of foggy remote sensing images.

A Domain Adaptive Person Re-Identification Based on Dual Attention Mechanism and Camstyle Transfer

“…The source domain is labeled and the target domain is not labeled. The UDA methods focus on solving the target domain without any labels [29][30][31][32]. In order to learn the discriminative features in the target domain, early-stage methods focus on the feature/sample mapping between the source domain and target domain.…”

A Semantic-Preserving Deep Hashing Model for Multi-Label Remote Sensing Image Retrieval

“…With the rapid development of Earth observation technology, the volume of remote sensing data has increased exponentially [1][2][3]. Remote sensing images have broad applications such as early warning for natural disasters, emergency response, and urban construction planning [4,5]. It is a challenging task to quickly obtain remote sensing images of interest from a pool of massive remote sensing images, stimulating extensive research interests among the scientific community [6,7].…”

Sea-Land Clutter Classification Based on Graph Spectrum Features

“…The radar sends electromagnetic waves to a specific area and receives the target's reflected echoes, which are always mixed with clutter and noise [1][2][3]. These uninteresting echoes from rain, fog, sea and land are important factors affecting radar performance.…”

“…A central question of clutter concerns the selection of features, that is, which features are present in radar returns and able to accurately identify each clutter type and be removed from the returns. In [2], the overall quality of the defogged remote sensing image is improved by decomposing the source image and feature enhancement based on the dual self-attention boost residual octave convolution. Preivous literature [3] applied an atmospheric scattering model that is based on the estimated atmospheric light and transmission map to remove clutter from remote sensing images.…”