EFCOMFF-Net: A Multiscale Feature Fusion Architecture With Enhanced Feature Correlation for Remote Sensing Image Scene Classification

Chen, Junsong; Yi, Jizheng; Chen, Aibin; Jin, Ze

doi:10.1109/tgrs.2023.3255211

Cited by 13 publications

(4 citation statements)

References 56 publications

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“…Moreover, we compare our method with [67,68]. We observe that our method outperforms those aerial image classifiers not specifically designed for LR aerial photo categorization.…”

Section: B Comparative Study 1)accuracy Comparisonmentioning

confidence: 90%

“…We observe that our method outperforms those aerial image classifiers not specifically designed for LR aerial photo categorization. Besides, [67,68] cannot encode auxiliary information from HR aerial photos. Thus their performances are inferior.…”

Section: B Comparative Study 1)accuracy Comparisonmentioning

confidence: 99%

“…EgNet [22] AIDER [23] MLAIC [24] CAConvBi [25] DLSIC [26] GLNet [27] DARS [28] SPP-CNN [50] CleanNet [12] MSFF [67] Tall For the categorization models implemented by ourselves, the experimental setups are briefed as follows. In [25], we utilize the ResNet-152 [36] as the backbone, which is subsequently upgraded into a multi-label variant.…”

Section: Categorymentioning

confidence: 99%

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LR Aerial Photo Categorization by Semi-Supervised Perceptual Feature Selection

Li,

Sheng

2023

IEEE Access

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Recognizing the semantic categories of low-resolution (LR) aerial photos is an indispensable technique in geoscience and remote sensing. However, it is also a challenging task in practice. In this work, a semi-supervised perceptual feature selection (SPFS) pipeline is proposed for LR aerial photo categorization, focusing on selecting high quality perception-guided visual features. Specifically, by mimicking human vision system, a novel low-rank model is designed to decompose each LR aerial photo into multiple visually or semantically salient foreground regions coupled with the background non-salient regions. This model can 1) produce the a gaze shifting path (GSP) simulating human gaze behavior, and 2) generate hierarchical deep representation for a GSP. Afterward, a semi-supervised feature selection (FS) is leveraged toward a succinct set of discriminative deep GSP features, wherein only labels of LR aerial photos are required. Based on the selected features, a classifier is trained for visual categorization. Comprehensive experimental results have validated our method's advantage.

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“…Moreover, we compare our method with [67,68]. We observe that our method outperforms those aerial image classifiers not specifically designed for LR aerial photo categorization.…”

Section: B Comparative Study 1)accuracy Comparisonmentioning

confidence: 90%

Section: B Comparative Study 1)accuracy Comparisonmentioning

confidence: 99%

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LR Aerial Photo Categorization by Semi-Supervised Perceptual Feature Selection

Li,

Sheng

2023

IEEE Access

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“…This is the author's version which has not been fully e content may change prior to final publication. Citation information: DOI 10.1109/JSTARS.2023.3344628 IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing through a cascaded approach [46], [47], resulting in a more distinguished representation of scenes. In the semantic space, the Word2Vec [48] model trained on the Wikipedia corpus was used to transform each scene class into a semantic vector, which is then mapped to the visual space.…”

Section: A Model Overviewmentioning

confidence: 99%

Zero-Shot Remote Sensing Scene Classification Method Based on Local-Global Feature Fusion and Weight Mapping Loss

Wang,

Li,

Tanvir

et al. 2024

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

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Zero-shot remote sensing scene classification refers to making the model to have the ability to identify the unseen class scenes based on seen class scenes, and has become a research hotspot in the field of remote sensing. Contemporary approaches in zero-shot remote sensing scene classification primarily focus on extracting global information from scenes, neglecting nuanced local landscape features. This oversight diminishes the discriminative capabilities of recognition models. Furthermore, these methods overlook the semantic relevance between seen and unseen class scenes in training, leading to reduced emphasis on learning from varied scenes and subsequent declines in classification performance. To address these challenges, this paper proposes the "Zero-Shot Remote Sensing Scene Classification Method Based on Local-Global Feature Fusion and Weight Mapping Loss (LGFFWM)." The design incorporates a local-global feature fusion (LGFF) module enabling adaptive labeling and feature modeling of internal local landscapes, effectively merging them with global features for a more discriminative representation of remote sensing scenes. Furthermore, a weight mapping loss (WM Loss) function is introduced, leveraging a semantic correlation matrix to compel the model to prioritize learning seen class scenes that exhibit strong correlations with unseen class scenes by assigning higher training weights. Extensive experiments have been conducted on classical remote sensing scene datasets, including UCM, AID, and NWPU, demonstrate the superiority of the proposed LGFFWM method over ten advanced comparative methods, yielding overall accuracy improvements of over 2.25%, 3.47%, and 0.44%, respectively. Additional experiments on the SIRI-WHU and RSSCN7 datasets underscore the transferability of LGFFWM, achieving overall accuracies of 53.50% and 47.37%, respectively.

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SCECNet: self-correction feature enhancement fusion network for remote sensing scene classification

Liu,

Wu,

et al. 2024

Earth Sci Inform

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EFCOMFF-Net: A Multiscale Feature Fusion Architecture With Enhanced Feature Correlation for Remote Sensing Image Scene Classification

Cited by 13 publications

References 56 publications

LR Aerial Photo Categorization by Semi-Supervised Perceptual Feature Selection

LR Aerial Photo Categorization by Semi-Supervised Perceptual Feature Selection

Zero-Shot Remote Sensing Scene Classification Method Based on Local-Global Feature Fusion and Weight Mapping Loss

SCECNet: self-correction feature enhancement fusion network for remote sensing scene classification

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