An Advanced Spectral–Spatial Classification Framework for Hyperspectral Imagery Based on DeepLab v3+

Si, Ying; Gong, D.; Guo, Yang; Zhu, Xinhua; Huang, Qiangsheng; Evans, Julian; He, Sailing; Sun, Yaoran

doi:10.3390/app11125703

Cited by 12 publications

(5 citation statements)

References 15 publications

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“…In this section, we use the PDCNet model structure shown in Figure 5 to conduct a comparative experiment with another hyperspectral image segmentation method (DeepLab v3+) [49] on the UP and KSC datasets. The corresponding classification results are shown in Table 11.…”

Section: Comparison With Other Segmentation Methodsmentioning

confidence: 99%

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Densely Connected Pyramidal Dilated Convolutional Network for Hyperspectral Image Classification

et al. 2021

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Recently, with the extensive application of deep learning techniques in the hyperspectral image (HSI) field, particularly convolutional neural network (CNN), the research of HSI classification has stepped into a new stage. To avoid the problem that the receptive field of naive convolution is small, the dilated convolution is introduced into the field of HSI classification. However, the dilated convolution usually generates blind spots in the receptive field, resulting in discontinuous spatial information obtained. In order to solve the above problem, a densely connected pyramidal dilated convolutional network (PDCNet) is proposed in this paper. Firstly, a pyramidal dilated convolutional (PDC) layer integrates different numbers of sub-dilated convolutional layers is proposed, where the dilated factor of the sub-dilated convolution increases exponentially, achieving multi-sacle receptive fields. Secondly, the number of sub-dilated convolutional layers increases in a pyramidal pattern with the depth of the network, thereby capturing more comprehensive hyperspectral information in the receptive field. Furthermore, a feature fusion mechanism combining pixel-by-pixel addition and channel stacking is adopted to extract more abstract spectral–spatial features. Finally, in order to reuse the features of the previous layers more effectively, dense connections are applied in densely pyramidal dilated convolutional (DPDC) blocks. Experiments on three well-known HSI datasets indicate that PDCNet proposed in this paper has good classification performance compared with other popular models.

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Section: Comparison With Other Segmentation Methodsmentioning

confidence: 99%

“…The DeepLab v3+ network shows active performance in the field of semantic segmentation. Si et al applied it to the field of HSI image classification for feature extraction [49]. Then, the SVM classifier is used to get the final classification result.…”

Section: Introductionmentioning

confidence: 99%

Densely Connected Pyramidal Dilated Convolutional Network for Hyperspectral Image Classification

et al. 2021

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“…It is generally necessary for the networks for semantic segmentation to perform downsampling multiple times during the feature extraction, thus losing many spatial details. Therefore, the boundary of images cannot be favorably reproduced in the subsequent up-sampling process, that is, with the boundary refinement problem [33,34]. Thus, some improvements are made in current research from two aspects, i.e., post-processing and training process.…”

Section: Boundary Refinementmentioning

confidence: 99%

Earthquake-Damaged Buildings Detection in Very High-Resolution Remote Sensing Images Based on Object Context and Boundary Enhanced Loss

et al. 2021

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Fully convolutional networks (FCN) such as UNet and DeepLabv3+ are highly competitive when being applied in the detection of earthquake-damaged buildings in very high-resolution (VHR) remote sensing images. However, existing methods show some drawbacks, including incomplete extraction of different sizes of buildings and inaccurate boundary prediction. It is attributed to a deficiency in the global context-aware and inaccurate correlation mining in the spatial context as well as failure to consider the relative positional relationship between pixels and boundaries. Hence, a detection method for earthquake-damaged buildings based on the object contextual representations (OCR) and boundary enhanced loss (BE loss) was proposed. At first, the OCR module was separately embedded into high-level feature extractions of the two networks DeepLabv3+ and UNet in order to enhance the feature representation; in addition, a novel loss function, that is, BE loss, was designed according to the distance between the pixels and boundaries to force the networks to pay more attention to the learning of the boundary pixels. Finally, two improved networks (including OB-DeepLabv3+ and OB-UNet) were established according to the two strategies. To verify the performance of the proposed method, two benchmark datasets (including YSH and HTI) for detecting earthquake-damaged buildings were constructed according to the post-earthquake images in China and Haiti in 2010, respectively. The experimental results show that both the embedment of the OCR module and application of BE loss contribute to significantly increasing the detection accuracy of earthquake-damaged buildings and the two proposed networks are feasible and effective.

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“…The image size is 512 × 614 pixels with a spatial resolution of 18 m. Some atmospheric water absorption bands and low signal-to-noise ratio (SNR) bands were discarded, and only 176 bands were reserved for analysis. A total of 13 categories and 5211 labeled samples were calibrated, as shown in Figure 7 [37].…”

Section: Hyperspecral Dataset Descriptionmentioning

confidence: 99%

Depthwise Separable Relation Network for Small Sample Hyperspectral Image Classification

et al. 2021

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Although hyperspectral data provide rich feature information and are widely used in other fields, the data are still scarce. Training small sample data classification is still a major challenge for HSI classification based on deep learning. Recently, the method of mining sample relationships has been proved to be an effective method for training small samples. However, this strategy requires high computational power, which will increase the difficulty of network model training. This paper proposes a modified depthwise separable relational network to deeply capture the similarity between samples. In addition, in order to effectively mine the similarity between samples, the feature vectors of support samples and query samples are symmetrically spliced. According to the metric distance between symmetrical structures, the dependence of the model on samples can be effectively reduced. Firstly, in order to improve the training efficiency of the model, depthwise separable convolution is introduced to reduce the computational cost of the model. Secondly, the Leaky-ReLU function effectively activates all neurons in each layer of neural network to improve the training efficiency of the model. Finally, the cosine annealing learning rate adjustment strategy is introduced to avoid the model falling into the local optimal solution and enhance the robustness of the model. The experimental results on two widely used hyperspectral remote sensing image data sets (Pavia University and Kennedy Space Center) show that compared with seven other advanced classification methods, the proposed method achieves better classification accuracy under the condition of limited training samples.

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An Advanced Spectral–Spatial Classification Framework for Hyperspectral Imagery Based on DeepLab v3+

Cited by 12 publications

References 15 publications

Densely Connected Pyramidal Dilated Convolutional Network for Hyperspectral Image Classification

Densely Connected Pyramidal Dilated Convolutional Network for Hyperspectral Image Classification

Earthquake-Damaged Buildings Detection in Very High-Resolution Remote Sensing Images Based on Object Context and Boundary Enhanced Loss

Depthwise Separable Relation Network for Small Sample Hyperspectral Image Classification

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