DFL-LC: Deep Feature Learning With Label Consistencies for Hyperspectral Image Classification

Liu, Siyuan; Cao, Yun; Wang, Yuebin; Peng, Junhuan; Mathiopoulos, P. Takis; Li, Yong

doi:10.1109/jstars.2021.3063679

Cited by 11 publications

(3 citation statements)

References 40 publications

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“…Additionally, the transferred knowledge is applied to the model prior to knowledge acquisition, model parameter training, and model architecture of the target dataset. For the top layer of the target scene dataset, the deep neural network is used to extract the deep features of the images and classify the target scene data [55][56][57]. The specific implementation steps are described as follows.…”

Section: Processes Of Implementationmentioning

confidence: 99%

A New SCAE-MT Classification Model for Hyperspectral Remote Sensing Images

Chen

Wang

et al. 2022

Sensors

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Hyperspectral remote sensing images (HRSI) have the characteristics of foreign objects with the same spectrum. As it is difficult to label samples manually, the hyperspectral remote sensing images are understood to be typical “small sample” datasets. Deep neural networks can effectively extract the deep features from the HRSI, but the classification accuracy mainly depends on the training label samples. Therefore, the stacked convolutional autoencoder network and transfer learning strategy are employed in order to design a new stacked convolutional autoencoder network model transfer (SCAE-MT) for the purposes of classifying the HRSI in this paper. In the proposed classification method, the stacked convolutional au-to-encoding network is employed in order to effectively extract the deep features from the HRSI. Then, the transfer learning strategy is applied to design a stacked convolutional autoencoder network model transfer under the small and limited training samples. The SCAE-MT model is used to propose a new HRSI classification method in order to solve the small samples of the HRSI. In this study, in order to prove the effectiveness of the proposed classification method, two HRSI datasets were chosen. In order to verify the effectiveness of the methods, the overall classification accuracy (OA) of the convolutional self-coding network classification method (CAE), the stack convolutional self-coding network classification method (SCAE), and the SCAE-MT method under 5%, 10%, and 15% training sets are calculated. When compared with the CAE and SCAE models in 5%, 10%, and 15% training datasets, the overall accuracy (OA) of the SCAE-MT method was improved by 2.71%, 3.33%, and 3.07% (on average), respectively. The SCAE-MT method is, thus, clearly superior to the other methods and also shows a good classification performance.

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Section: Processes Of Implementationmentioning

confidence: 99%

A New SCAE-MT Classification Model for Hyperspectral Remote Sensing Images

Chen

Wang

et al. 2022

Sensors

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“…It utilizes the rich spectral information contained in hyperspectral images to effectively separate targets from background pixels. However, in practical situations, it is difficult to obtain labeled sample data [11][12][13]. Therefore, anomaly detection (an unsupervised approach to detect targets) has a wider practical application.…”

Section: Introductionmentioning

confidence: 99%

CE-RX: A Collaborative Cloud-Edge Anomaly Detection Approach for Hyperspectral Images

Wang,

Cai,

Zhou

et al. 2023

Remote Sensing

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Due to the constrained processing capabilities of real-time detection techniques in remote sensing applications, it is often difficult to obtain detection results with high accuracy in practice. To address this problem, we introduce a new real-time anomaly detection algorithm for hyperspectral images called cloud–edge RX (CE-RX). The algorithm combines the advantages of cloud and edge computing. During the data acquisition process, the edge performs real-time detection on the data just captured to obtain a coarse result and find the suspicious anomalies. At regular intervals, the suspicious anomalies are sent to the cloud for further detection with a highly accurate algorithm, then the cloud sends back the (high-accuracy) results to the edge for information updating. After receiving the results from the cloud, the edge updates the information of the detector in the real-time algorithm to improve the detection accuracy of the next acquired piece of data. Our experimental results demonstrate that the proposed cloud–edge collaborative algorithm can obtain more accurate results than existing real-time detection algorithms.

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“…Yang et al proposed GCN hyperspectral classification by sampling and aggregating, referred to as GraphSAGE 22 . Liu et al 23 studied GCN hyperspectral classification based on label consistency and multi-scale convolutional networks. Ding et al 24 proposed a globally consistent GCN based on SLIC.…”

Section: Introductionmentioning

confidence: 99%

Asymmetric coordinate attention spectral-spatial feature fusion network for hyperspectral image classification

Cheng

Wang

2021

Sci Rep

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In recent years, the hyperspectral classification algorithm based on deep learning has received widespread attention, but the existing network models have higher model complexity and require more time consumption. In order to further improve the accuracy of hyperspectral image classification and reduce model complexity, this paper proposes an asymmetric coordinate attention spectral-spatial feature fusion network (ACAS2F2N) to capture distinguishing hyperspectral features. Specifically, adaptive asymmetric iterative attention was proposed to obtain the discriminative spectral-spatial features. Different from the common feature fusion method, this feature fusion method can adapt to most skip connection tasks. In addition, there is no manual parameter setting. Coordinate attention is used to obtain accurate coordinate information and channel relationship. The strip pooling module was introduced to increase the network’s receptive field and avoid irrelevant information brought by conventional convolution kernels. The proposed algorithm is tested on the mainstream hyperspectral datasets (IP, KSC, and Botswana), experimental results show that the proposed ACAS2F2N can achieve state-of-the-art performance with lower time complexity.

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DFL-LC: Deep Feature Learning With Label Consistencies for Hyperspectral Image Classification

Cited by 11 publications

References 40 publications

A New SCAE-MT Classification Model for Hyperspectral Remote Sensing Images

A New SCAE-MT Classification Model for Hyperspectral Remote Sensing Images

CE-RX: A Collaborative Cloud-Edge Anomaly Detection Approach for Hyperspectral Images

Asymmetric coordinate attention spectral-spatial feature fusion network for hyperspectral image classification

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