Spatial and Spectral Extraction Network With Adaptive Feature Fusion for Pansharpening

Zhang, Kai; Wang, Anfei; Zhang, Feng; Diao, Wenxiu; Sun, Jiande; Bruzzone, Lorenzo

doi:10.1109/tgrs.2022.3187025

Cited by 20 publications

(13 citation statements)

References 49 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…To further demonstrate the effectiveness of our fusion method, we compare it with other fusion strategies that are widely used in RGB-Depth and RGB-Thermal perception tasks, including: channel-wise weighted feature fusion (CWF) [14], cross gates (CRGs) [15], cross reference module (CRM) [16], gated information fusion (GIF) [17], and a fusion method for PAN and MS data fusion, i.e., the adaptive feature fusion module (AFFM) [18]. For CWF, CRGs, and GIF, we reimplement them in strict accordance with the paper; for SCA and AFMM, we use the codes the authors provided.…”

Section: F Overall Resultsmentioning

confidence: 99%

“…AFFM [18] generates weights from the concatenation of features after two convolutional layers. A softmax operation will then normalizes the weights along the channel.…”

Section: F Overall Resultsmentioning

confidence: 99%

“…(d) Gated information fusion (GIF) [17]. (e) Adaptive feature fusion modules (AFFM) [18] Table VI shows all methods' running time and complexity based on Faster R-CNN [25]. Our DAFNet has fewer parameters than other feature fusion methods and achieves better performance.…”

Section: F Overall Resultsmentioning

confidence: 99%

See 2 more Smart Citations

Building Detection From Panchromatic and Multispectral Images With Dual-Stream Asymmetric Fusion Networks

Huang

Liu

Zhou

et al. 2023

IEEE J. Sel. Top. Appl. Earth Observations Remote Sensing

View full text Add to dashboard Cite

Building detection from panchromatic (PAN) and Multi-spectral (MS) images is an essential task for many practical applications. In this paper, a dual-stream asymmetric fusion network is proposed, named DAFNet. DAFNet can achieve effective information fusion at the feature level. It obtains better building detection performance from three perspectives: 1) A two-stream network structure is designed to guarantee the ability to extract information from PAN and MS images; 2) An asymmetric feature fusion (AFF) module is proposed to fuse features efficiently and concisely; 3) Two consistency regularization losses, i.e., PAN information preservation (PiP) loss and cross-modal semantic consistency (CSC) loss are applied to further explore the consistency between features for better fusion. The experiments are conducted on a challenging building detection dataset collected from GaoFen-2 satellite images. Comprehensive evaluations on 12 popular detection methods demonstrate the superiority of our DAFNet compared with the existing state-of-the-art fusion methods. We reveal that feature-level fusion is more suitable for building detection from PAN-MS images. Code is released at https://github.com/floatingstarZ/DAFNet

show abstract

Section: F Overall Resultsmentioning

confidence: 99%

“…AFFM [18] generates weights from the concatenation of features after two convolutional layers. A softmax operation will then normalizes the weights along the channel.…”

Section: F Overall Resultsmentioning

confidence: 99%

See 1 more Smart Citation

Building Detection From Panchromatic and Multispectral Images With Dual-Stream Asymmetric Fusion Networks

Huang

Liu

Zhou

et al. 2023

IEEE J. Sel. Top. Appl. Earth Observations Remote Sensing

View full text Add to dashboard Cite

show abstract

“…(1) Component substitution-based (CS) methods [2][3][4] (2) Multiscale analysis-based (MRA) methods [5,6] (3) Degradation model-based (DM) methods [7][8][9][10][11][12][13][14][15][16][17] (4) Deep neural network-based (DNN) methods [18][19][20][21] CS methods include principal component analysis (PCA) and intensity-hue-saturation (IHS). CS methods behave well in computational e ciency.…”

Section: Introductionmentioning

confidence: 99%

“…[19], the Pan-GAN model was proposed, and this method did not rely on the ground truth. Zhang et al [20] proposed an SSE network-based pansharpening. In this paper, AFFMs were used to merge image features through information content.…”

Section: Introductionmentioning

confidence: 99%

Pansharpening with the Multidirection Tree Ridgelet Dictionary

Gai-ning

et al. 2022

Mathematical Problems in Engineering

View full text Add to dashboard Cite

In this work, we propose a novel pansharpening method based on the multidirection tree ridgelet dictionary. A pansharpened image has a wide-ranging application area, such as object detection, image segmentation, feature extraction, and so on. Remote sensing (RS) imagery contains more abundant information on surface features. In order to represent different object information, we use three main classes of different dictionaries, which can reveal the latent structure of RS image. First, RS imagery is divided into several blocks. Each block is classified as smooth, irregular, or multidirection categories. Different categories are sparsely represented in different dictionaries. Second, the smooth blocks are sparsely represented in the discrete cosine transform (DCT) dictionary. The irregular and the multidirection blocks are sparsely represented in the KSVD and multidirection tree ridgelet (MDTR) dictionary, respectively. Finally, we can obtain the fusion image by reconstructing those blocks. Some experiments are taken on three different datasets acquired by QuickBird, GeoEye, and IKONOS satellites. Experimental results show that the proposed method can reduce spectral distortion and enhance spatial information. Meanwhile, numerical guidelines outperform some related methods.

show abstract

Calculation of dielectric constant, loss property and scattering characteristics from the future martian GPR data

Zhang¹,

Xu²,

Chen³

et al. 2022

Icarus

View full text Add to dashboard Cite

Spatial and Spectral Extraction Network With Adaptive Feature Fusion for Pansharpening

Cited by 20 publications

References 49 publications

Building Detection From Panchromatic and Multispectral Images With Dual-Stream Asymmetric Fusion Networks

Building Detection From Panchromatic and Multispectral Images With Dual-Stream Asymmetric Fusion Networks

Pansharpening with the Multidirection Tree Ridgelet Dictionary

Calculation of dielectric constant, loss property and scattering characteristics from the future martian GPR data

Contact Info

Product

Resources

About