Multi-Stage Feature Constraints Learning for Age Estimation

Xia, Min; Zhang, Xu; Liu, Wan’an; Weng, Liguo; Xu, Yiqing

doi:10.1109/tifs.2020.2969552

Cited by 96 publications

(56 citation statements)

References 38 publications

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“…In addition to the common ResNet, we can also use MobileNetv2 [35] as a lightweight backbone. With a little modification to different backbones, we can extract the features of different levels that we need to prepare for the up-sampling process [36].…”

Section: Channel Feature Compressionmentioning

confidence: 99%

MFANet: A Multi-Level Feature Aggregation Network for Semantic Segmentation of Land Cover

2021

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Detailed information regarding land utilization/cover is a valuable resource in various fields. In recent years, remote sensing images, especially aerial images, have become higher in resolution and larger span in time and space, and the phenomenon that the objects in an identical category may yield a different spectrum would lead to the fact that relying on spectral features only is often insufficient to accurately segment the target objects. In convolutional neural networks, down-sampling operations are usually used to extract abstract semantic features, which leads to loss of details and fuzzy edges. To solve these problems, the paper proposes a Multi-level Feature Aggregation Network (MFANet), which is improved in two aspects: deep feature extraction and up-sampling feature fusion. Firstly, the proposed Channel Feature Compression module extracts the deep features and filters the redundant channel information from the backbone to optimize the learned context. Secondly, the proposed Multi-level Feature Aggregation Upsample module nestedly uses the idea that high-level features provide guidance information for low-level features, which is of great significance for positioning the restoration of high-resolution remote sensing images. Finally, the proposed Channel Ladder Refinement module is used to refine the restored high-resolution feature maps. Experimental results show that the proposed method achieves state-of-the-art performance 86.45% mean IOU on LandCover dataset.

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Section: Channel Feature Compressionmentioning

confidence: 99%

MFANet: A Multi-Level Feature Aggregation Network for Semantic Segmentation of Land Cover

2021

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“…The Deep neural network needs a large number of training data, but it is difficult to obtain these learning samples. Therefore, it is very necessary to use data augmentation to avoid overfitting when there are only a few training samples [36]. Thus, 5000 pictures were generated by scaling, translation, flipping, and rotation.…”

Section: Data Augmentationmentioning

confidence: 99%

Water Areas Segmentation from Remote Sensing Images Using a Separable Residual SegNet Network

Weng

Xia

et al. 2020

IJGI

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Changes on lakes and rivers are of great significance for the study of global climate change. Accurate segmentation of lakes and rivers is critical to the study of their changes. However, traditional water area segmentation methods almost all share the following deficiencies: high computational requirements, poor generalization performance, and low extraction accuracy. In recent years, semantic segmentation algorithms based on deep learning have been emerging. Addressing problems associated to a very large number of parameters, low accuracy, and network degradation during training process, this paper proposes a separable residual SegNet (SR-SegNet) to perform the water area segmentation using remote sensing images. On the one hand, without compromising the ability of feature extraction, the problem of network degradation is alleviated by adding modified residual blocks into the encoder, the number of parameters is limited by introducing depthwise separable convolutions, and the ability of feature extraction is improved by using dilated convolutions to expand the receptive field. On the other hand, SR-SegNet removes the convolution layers with relatively more convolution kernels in the encoding stage, and uses the cascading method to fuse the low-level and high-level features of the image. As a result, the whole network can obtain more spatial information. Experimental results show that the proposed method exhibits significant improvements over several traditional methods, including FCN, DeconvNet, and SegNet.

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“…In recent years, with the improvement of computer hardware and the increasing demand for image processing in practical work, deep learning (DL) has made great progress in the field of security [8], handwritten digit recognition [9], human action recognition [10], financial trading [11], remote image processing [12][13][14][15][16][17], and others [18][19][20][21][22]. According to the study of Kussul et al [23] in processing land cover remote sensing images, deep learning algorithms are significantly better than machine learning algorithms such as the SVM.…”

Section: Introductionmentioning

confidence: 99%

DFFAN: Dual Function Feature Aggregation Network for Semantic Segmentation of Land Cover

Huang

Weng

Chen

et al. 2021

IJGI

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Analyzing land cover using remote sensing images has broad prospects, the precise segmentation of land cover is the key to the application of this technology. Nowadays, the Convolution Neural Network (CNN) is widely used in many image semantic segmentation tasks. However, existing CNN models often exhibit poor generalization ability and low segmentation accuracy when dealing with land cover segmentation tasks. To solve this problem, this paper proposes Dual Function Feature Aggregation Network (DFFAN). This method combines image context information, gathers image spatial information, and extracts and fuses features. DFFAN uses residual neural networks as backbone to obtain different dimensional feature information of remote sensing images through multiple downsamplings. This work designs Affinity Matrix Module (AMM) to obtain the context of each feature map and proposes Boundary Feature Fusion Module (BFF) to fuse the context information and spatial information of an image to determine the location distribution of each image’s category. Compared with existing methods, the proposed method is significantly improved in accuracy. Its mean intersection over union (MIoU) on the LandCover dataset reaches 84.81%.

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Multi-Stage Feature Constraints Learning for Age Estimation

Cited by 96 publications

References 38 publications

MFANet: A Multi-Level Feature Aggregation Network for Semantic Segmentation of Land Cover

MFANet: A Multi-Level Feature Aggregation Network for Semantic Segmentation of Land Cover

Water Areas Segmentation from Remote Sensing Images Using a Separable Residual SegNet Network

DFFAN: Dual Function Feature Aggregation Network for Semantic Segmentation of Land Cover

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