Decision-Level Fusion with a Pluginable Importance Factor Generator for Remote Sensing Image Scene Classification

Shen, Junge; Zhang, Chi; Zheng, Yu; Wang, Ruxin

doi:10.3390/rs13183579

Cited by 4 publications

(1 citation statement)

References 51 publications

(72 reference statements)

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“…In contemporary methods, image fusion is mainly divided into three levels, namely, pixel, feature, and decision levels [37]. Decision-level fusion is the highest-level processing method for image fusion [38]. It is based on the preliminary judgment of an algorithm about information such as the location and category of the specific target; it uses information synthesis to ensure judgment accuracy; however, it relies too much on the previous feature processing results.…”

Section: Dff Modulementioning

confidence: 99%

DMFFNet: Dual-Mode Multi-Scale Feature Fusion-Based Pedestrian Detection Method

Rui

Ouyang

et al. 2024

IEEE Access

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Most contemporary pedestrian detection algorithms are based on visible light image detection. However, in environments with dim light, small targets, and easily occluded and cluttered backgrounds, single-mode visible light images relying on color, texture, and other features cannot adequately represent the feature information of targets; as a result, a large number of targets are lost and the algorithm performance is not good. To address this problem, we propose a dual-modal multi-scale feature fusion network (DMFFNet). First, we use the MobileNet v3 backbone network to extract the features of dual-modal images as input for the multi-scale fusion attention (MFA) module, combining the idea of multi-scale feature fusion and attention mechanism. Second, we deeply fuse the multi-scale features output by the MFA with the double deep feature fusion (DDFF) module to enhance the semantic and geometric information of the target. Finally, we optimize the loss function to reflect the distance between the predicted box and the real box more realistically as well as to enhance the ability of the network toward predicting difficult samples. We performed multi-directional evaluations on the KAIST dual-light pedestrian dataset and the visible-thermal infrared pedestrian dataset (VTI) in our laboratory through comparative and ablation experiments. The overall MR -2 on the KAIST duallight pedestrian dataset is 9.26%, and the MR -2 in dim light, partial occlusion, and severe occlusion are 5.17%, 23.35%, and 47.31%, respectively. The overall MR -2 on the VIT dual-light pedestrian dataset is 9.26%, and the MR -2 in dim light, partial occlusion, and severe occlusion are 5.17%, 23.35%, and 47.31%, respectively. The results show that the algorithm performs well on pedestrian detection, especially in dim light and when the target was occluded.

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Section: Dff Modulementioning

confidence: 99%

DMFFNet: Dual-Mode Multi-Scale Feature Fusion-Based Pedestrian Detection Method

Rui

Ouyang

et al. 2024

IEEE Access

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Mapping Irrigated Areas Based on Remotely Sensed Crop Phenology and Soil Moisture

et al. 2023

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Artificial irrigation is critical for improving soil moisture conditions and ensuring crop growth. Its irrational deployment can lead to ecological and environmental issues. Mapping and understanding the changes in irrigated areas are vital to effectively managing limited water. However, most researchers map irrigated areas with a single data resource, which makes it hard to detect irrigated signals in complex situations. The case study area for this paper was China’s winter wheat region, and an irrigated area map was generated by analyzing the effects of artificial irrigation on crop phenological characteristics and soil moisture time series. The mapping process involved three steps: (1) generating a basic irrigated map by employing the ISODATA classification method on the Kolmogorov–Smirnov test irrigation signals from the microwave remote sensing data and reanalysis data; (2) creating the other map with the maximum likelihood ratio classification and zoning scheme on the phenological parameters extracted from the NDVI time series; and (3) fusing these two maps at the decision level to obtain the final map with a higher spatial resolution of 1 km. The map was evaluated against existing irrigated area data and was highly compatible with GMIA 5.0. The overall accuracy (OA) was 73.49%.

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An Attention Cascade Global–Local Network for Remote Sensing Scene Classification

Shen

Yang

et al. 2022

Remote Sensing

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Remote sensing image scene classification is an important task of remote sensing image interpretation, which has recently been well addressed by the convolutional neural network owing to its powerful learning ability. However, due to the multiple types of geographical information and redundant background information of the remote sensing images, most of the CNN-based methods, especially those based on a single CNN model and those ignoring the combination of global and local features, exhibit limited performance on accurate classification. To compensate for such insufficiency, we propose a new dual-model deep feature fusion method based on an attention cascade global–local network (ACGLNet). Specifically, we use two popular CNNs as the feature extractors to extract complementary multiscale features from the input image. Considering the characteristics of the global and local features, the proposed ACGLNet filters the redundant background information from the low-level features through the spatial attention mechanism, followed by which the locally attended features are fused with the high-level features. Then, bilinear fusion is employed to produce the fused representation of the dual model, which is finally fed to the classifier. Through extensive experiments on four public remote sensing scene datasets, including UCM, AID, PatternNet, and OPTIMAL-31, we demonstrate the feasibility of the proposed method and its superiority over the state-of-the-art scene classification methods.

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Decision-Level Fusion with a Pluginable Importance Factor Generator for Remote Sensing Image Scene Classification

Cited by 4 publications

References 51 publications

DMFFNet: Dual-Mode Multi-Scale Feature Fusion-Based Pedestrian Detection Method

DMFFNet: Dual-Mode Multi-Scale Feature Fusion-Based Pedestrian Detection Method

Mapping Irrigated Areas Based on Remotely Sensed Crop Phenology and Soil Moisture

An Attention Cascade Global–Local Network for Remote Sensing Scene Classification

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