A semi-supervised generative framework with deep learning features for high-resolution remote sensing image scene classification

Han, Wei; Feng, Ruyi; Wang, Lizhe; Cheng, Yafan

doi:10.1016/j.isprsjprs.2017.11.004

Cited by 169 publications

(68 citation statements)

References 38 publications

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“…VGG19 was found to be more accurate than VGG16 by about 3% (OA), presumably due to the deeper structure of the former convnet. These results are in general agreement with [49], which reported the superiority of ResNet relative to GoogLeNet (Inception), VGG16, and VGG19 for the classification of four public remote sensing datasets (e.g., UCM, WHU-RS19). InceptionResNetV2 benefits from integrating two well-known deep convnets, Inception and ResNet, which positively contribute to the most accurate result in this study.…”

Section: Figure 10supporting

confidence: 89%

Very Deep Convolutional Neural Networks for Complex Land Cover Mapping Using Multispectral Remote Sensing Imagery

et al. 2018

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Despite recent advances of deep Convolutional Neural Networks (CNNs) in various computer vision tasks, their potential for classification of multispectral remote sensing images has not been thoroughly explored. In particular, the applications of deep CNNs using optical remote sensing data have focused on the classification of very high-resolution aerial and satellite data, owing to the similarity of these data to the large datasets in computer vision. Accordingly, this study presents a detailed investigation of state-of-the-art deep learning tools for classification of complex wetland classes using multispectral RapidEye optical imagery. Specifically, we examine the capacity of seven well-known deep convnets, namely DenseNet121, InceptionV3, VGG16, VGG19, Xception, ResNet50, and InceptionResNetV2, for wetland mapping in Canada. In addition, the classification results obtained from deep CNNs are compared with those based on conventional machine learning tools, including Random Forest and Support Vector Machine, to further evaluate the efficiency of the former to classify wetlands. The results illustrate that the full-training of convnets using five spectral bands outperforms the other strategies for all convnets. InceptionResNetV2, ResNet50, and Xception are distinguished as the top three convnets, providing state-of-the-art classification accuracies of 96.17%, 94.81%, and 93.57%, respectively. The classification accuracies obtained using Support Vector Machine (SVM) and Random Forest (RF) are 74.89% and 76.08%, respectively, considerably inferior relative to CNNs. Importantly, InceptionResNetV2 is consistently found to be superior compared to all other convnets, suggesting the integration of Inception and ResNet modules is an efficient architecture for classifying complex remote sensing scenes such as wetlands.

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Section: Figure 10supporting

confidence: 89%

Very Deep Convolutional Neural Networks for Complex Land Cover Mapping Using Multispectral Remote Sensing Imagery

et al. 2018

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“…Natural degradation and hazards like floods and droughts are also a problem impacting rice production [5][6][7]. using a convolutional window and local connections [71][72]. Considering LULC mapping, such as cropland classification, and using moderate-resolution images like Landsat, the CNN and texture features might also have advantages.…”

Section: Introductionmentioning

confidence: 99%

“…CNN has been applied to the high-resolution land-use/-cover (LULC) classification [69,70], and demonstrates great potential in extracting spatial information using a Remote Sens. 2018, 10, 1840 3 of 22 convolutional window and local connections [71,72]. Considering LULC mapping, such as cropland classification, and using moderate-resolution images like Landsat, the CNN and texture features might also have advantages.…”

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confidence: 99%

Mapping Paddy Rice Using a Convolutional Neural Network (CNN) with Landsat 8 Datasets in the Dongting Lake Area, China

et al. 2018

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Rice is one of the world’s major staple foods, especially in China. Highly accurate monitoring on rice-producing land is, therefore, crucial for assessing food supplies and productivity. Recently, the deep-learning convolutional neural network (CNN) has achieved considerable success in remote-sensing data analysis. A CNN-based paddy-rice mapping method using the multitemporal Landsat 8, phenology data, and land-surface temperature (LST) was developed during this study. First, the spatial–temporal adaptive reflectance fusion model (STARFM) was used to blend the moderate-resolution imaging spectroradiometer (MODIS) and Landsat data for obtaining multitemporal Landsat-like data. Subsequently, the threshold method is applied to derive the phenological variables from the Landsat-like (Normalized difference vegetation index) NDVI time series. Then, a generalized single-channel algorithm was employed to derive LST from the Landsat 8. Finally, multitemporal Landsat 8 spectral images, combined with phenology and LST data, were employed to extract paddy-rice information using a patch-based deep-learning CNN algorithm. The results show that the proposed method achieved an overall accuracy of 97.06% and a Kappa coefficient of 0.91, which are 6.43% and 0.07 higher than that of the support vector machine method, and 7.68% and 0.09 higher than that of the random forest method, respectively. Moreover, the Landsat-derived rice area is strongly correlated (R2 = 0.9945) with government statistical data, demonstrating that the proposed method has potential in large-scale paddy-rice mapping using moderate spatial resolution images.

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“…However, most existing methods use the prior knowledge of the spectral information [12], and assume that the constructed CNN can extract the efficient deep spectral-spatial features. This assumption is not always satisfied on the classification of HRS images because of the complex distribution characteristics of HRS images [8], especially for those with high spatial resolution. It is easy to know that the more complex distribution of the data, the greater the number of CNN parameters will be used to extract features [37].…”

Section: Introductionmentioning

confidence: 99%

Convolutional Neural Network with Expert Knowledge for Hyperspectral Remote Sensing Imagery Classification

2019

KSII TIIS

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The recent interest in artificial intelligence and machine learning has partly contributed to an interest in the use of such approaches for hyperspectral remote sensing (HRS) imagery classification, as evidenced by the increasing number of deep framework with deep convolutional neural networks (CNN) structures proposed in the literature. In these approaches, the assumption of obtaining high quality deep features by using CNN is not always easy and efficient because of the complex data distribution and the limited sample size. In this paper, conventional handcrafted learning-based multi features based on expert knowledge are introduced as the input of a special designed CNN to improve the pixel description and classification performance of HRS imagery. The introduction of these handcrafted features can reduce the complexity of the original HRS data and reduce the sample requirements by eliminating redundant information and improving the starting point of deep feature training. It also provides some concise and effective features that are not readily available from direct training with CNN. Evaluations using three public HRS datasets demonstrate the utility of our proposed method in HRS classification.

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A semi-supervised generative framework with deep learning features for high-resolution remote sensing image scene classification

Cited by 169 publications

References 38 publications

Very Deep Convolutional Neural Networks for Complex Land Cover Mapping Using Multispectral Remote Sensing Imagery

Very Deep Convolutional Neural Networks for Complex Land Cover Mapping Using Multispectral Remote Sensing Imagery

Mapping Paddy Rice Using a Convolutional Neural Network (CNN) with Landsat 8 Datasets in the Dongting Lake Area, China

Convolutional Neural Network with Expert Knowledge for Hyperspectral Remote Sensing Imagery Classification

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