Improved Remote Sensing Image Classification Based on Multi-Scale Feature Fusion

Zhang, Chengming; Chen, Yan; Yang, Xiaoxia; Gao, Shuai; Li, Feng; Kong, Ailing; Zu, Dawei; Sun, Li

doi:10.3390/rs12020213

Cited by 33 publications

(21 citation statements)

References 53 publications

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“…In research of deep learning semantic segmentation, commonly used evaluation metrics are accuracy (Acc) [48], intersection over union (IoU) [49], mean accuracy (mAcc) [48], Kappa [50], mean intersection over union (mIoU) [49], and so on. Acc refers to the ratio of the number of pixels correctly predicted for a category to the total number of pixels in that category.…”

Section: Evaluation Metricsmentioning

confidence: 99%

Recognizing Zucchinis Intercropped with Sunflowers in UAV Visible Images Using an Improved Method Based on OCRNet

et al. 2021

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An improved semantic segmentation method based on object contextual representations network (OCRNet) is proposed to accurately identify zucchinis intercropped with sunflowers from unmanned aerial vehicle (UAV) visible images taken over Hetao Irrigation District, Inner Mongolia, China. The proposed method improves on the performance of OCRNet in two respects. First, based on the object region context extraction structure of the OCRNet, a branch that uses the channel attention module was added in parallel to rationally use channel feature maps with different weights and reduce the noise of invalid channel features. Secondly, Lovász-Softmax loss was introduced to improve the accuracy of the object region representation in the OCRNet and optimize the final segmentation result at the object level. We compared the proposed method with extant advanced semantic segmentation methods (PSPNet, DeepLabV3+, DNLNet, and OCRNet) in two test areas to test its effectiveness. The results showed that the proposed method achieved the best semantic segmentation effect in the two test areas. More specifically, our method performed better in processing image details, segmenting field edges, and identifying intercropping fields. The proposed method has significant advantages for crop classification and intercropping recognition based on UAV visible images, and these advantages are more substantive in object-level evaluation metrics (mIoU and intercropping IoU).

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Section: Evaluation Metricsmentioning

confidence: 99%

Recognizing Zucchinis Intercropped with Sunflowers in UAV Visible Images Using an Improved Method Based on OCRNet

et al. 2021

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“…Generally speaking, hidden Markov models are a linear classification technique. It is a simple linear relationship between music type and feature set [4,5]. But in fact, this is not the case.…”

Section: Related Workmentioning

confidence: 99%

Using Machine Learning Algorithm to Describe the Connection between the Types and Characteristics of Music Signal

Sun

2021

Complexity

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Music classification is conducive to online music retrieval, but the current music classification model finds it difficult to accurately identify various types of music, which makes the classification effect of the current music classification model poor. In order to improve the accuracy of music classification, a music classification model based on multifeature fusion and machine learning algorithm is proposed. First, we obtain the music signal, and then extract various features from the classification of the music signal, and use machine learning algorithms to describe the type of music signal and the relationship between the features. The music classifier and deep belief network machine learning models in shallow logistic regression are established, respectively. Experiments were designed for these two models to verify the applicability of the model for music classification. By comparing the experimental results, it is found that the classification accuracy of the deep confidence network model is higher than that of the logistic regression model, but the number of iterations needed for its accuracy to converge is also higher than that of the logistic regression model. Compared with other current music classification models, this model reduces the time of constructing music classifier, speeds up the speed of music classification, and can identify various types of music with high precision. The accuracy of music classification is obviously improved, which verifies the superiority of this music classification model.

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“…In addition, they design a parallel multi-kernel deconvolution module and a spatial path to further aggregate different scales information. WFCNN [30] is a weight feature value convolutional neural network, consisting of one encoder and one classifier. The encoder uses the linear fusion method to hierarchically fuse semantic features.…”

Section: Related Workmentioning

confidence: 99%

Multi-Scale Aerial Target Detection Based on Densely Connected Inception ResNet

et al. 2020

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With the rapid development of unmanned aerial vehicles (UAVs), aerial targets detection has attracted extensive attention from researchers. The difficulty of aerial image detection lies in the small proportion of ground targets in aerial images and the wide variety of target sizes. After multiple downsampling, the features of small targets are almost not available on the feature maps. To address these drawbacks, a densely connected Inception ResNet (RIDNet) is proposed. RIDNet is a lightweight multi-scale fusion detection network constructed with two residual inception units (RI): the RI-Dense model and the RI-Deconv model. The RI-Dense model consists of densely connected layers and shortcut connections. Each convolutional layer in RI-Dense has access to all the subsequent layers and passes on the information that needs to be preserved. The RI-Deconv fuses the global feature in a residual and hierarchical way, which continuously deconvolutes the output of RI-Dense and concatenates the result with the original output to get fusion layers. The fused layers absorb semantic information and detailed information from deep layers and shallow layers, respectively. Extensive experiments show the effectiveness of the proposed RIDNet. Ablation experiments also demonstrate that the RI-Dense model and RI-Deconv model can improve the mAP by 7.8% and 6.8%, respectively.INDEX TERMS Object detection, convolutional network, unmanned aerial vehicle.

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Improved Remote Sensing Image Classification Based on Multi-Scale Feature Fusion

Cited by 33 publications

References 53 publications

Recognizing Zucchinis Intercropped with Sunflowers in UAV Visible Images Using an Improved Method Based on OCRNet

Recognizing Zucchinis Intercropped with Sunflowers in UAV Visible Images Using an Improved Method Based on OCRNet

Using Machine Learning Algorithm to Describe the Connection between the Types and Characteristics of Music Signal

Multi-Scale Aerial Target Detection Based on Densely Connected Inception ResNet

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