P_Segnet and NP_Segnet: New Neural Network Architectures for Cloud Recognition of Remote Sensing Images

Lu, Jiarui; Wang, Yuzhu; Zhu, Yueqin; Ji, Xiaohui; Xing, Tingyan; Li, Wei; Zomaya, Albert Y.

doi:10.1109/access.2019.2925565

Cited by 29 publications

(15 citation statements)

References 23 publications

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“…We used the accuracy, precision, recall, and Kappa coefficient to evaluate the performance of the four models [45] (Table 5). The average accuracy of PP-CNN was 13.7% higher than SegNet, 7.2% higher than RefineNet, and 6.2% higher than PP-SegNet.…”

Section: Resultsmentioning

confidence: 99%

“…The two-dimensional convolution method is suitable for processing images with a small number of channels, such as camera images and optical remote sensing images [43,44]. Improved classic CNNs have been widely applied to remote sensing image segmentation [45] as well as target identification [46][47][48], monitoring [49][50][51], and other fields. For example, CNNs have been successfully used to extract spatial distribution information for various crops, including wheat [52], rice [53], and corn [54].…”

Section: Introductionmentioning

confidence: 99%

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Improved Winter Wheat Spatial Distribution Extraction from High-Resolution Remote Sensing Imagery Using Semantic Features and Statistical Analysis

Zhang

et al. 2020

Remote Sensing

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Improving the accuracy of edge pixel classification is an important aspect of using convolutional neural networks (CNNs) to extract winter wheat spatial distribution information from remote sensing imagery. In this study, we established a method using prior knowledge obtained from statistical analysis to refine CNN classification results, named post-processing CNN (PP-CNN). First, we used an improved RefineNet model to roughly segment remote sensing imagery in order to obtain the initial winter wheat area and the category probability vector for each pixel. Second, we used manual labels as references and performed statistical analysis on the class probability vectors to determine the filtering conditions and select the pixels that required optimization. Third, based on the prior knowledge that winter wheat pixels were internally similar in color, texture, and other aspects, but different from other neighboring land-use types, the filtered pixels were post-processed to improve the classification accuracy. We used 63 Gaofen-2 images obtained from 2017 to 2019 of a representative Chinese winter wheat region (Feicheng, Shandong Province) to create the dataset and employed RefineNet and SegNet as standard CNN and conditional random field (CRF) as post-process methods, respectively, to conduct comparison experiments. PP-CNN’s accuracy (94.4%), precision (93.9%), and recall (94.4%) were clearly superior, demonstrating its advantages for the improved refinement of edge areas during image classification.

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Section: Resultsmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Improved Winter Wheat Spatial Distribution Extraction from High-Resolution Remote Sensing Imagery Using Semantic Features and Statistical Analysis

Zhang

et al. 2020

Remote Sensing

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“…As the state of the art models for pixel-wise semantic segmentation, fully convolutional neural networks (FCNs) have been widely applied in research concerning remote sensing, such as sea-land segmentation [26], cloud recognition [27], and the semantic segmentation of high-resolution remote sensing images of land [28], [29]. Compared to typical convolutional neural networks (CNNs), a stack of deconvolution layers are added after the convolution layers in FCNs, which upsamples the coarse outputs to pixel-wise outputs.…”

Section: Introductionmentioning

confidence: 99%

Rain-Contaminated Region Segmentation of X-Band Marine Radar Images With an Ensemble of SegNets

Chen

Huang

Haller

et al. 2021

IEEE J. Sel. Top. Appl. Earth Observations Remote Sensing

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“…Cloud segmentation can be treated as an application of image segmentation, and therefore, applying semantic segmentation techniques for cloud detection is a reasonable consideration. Moreover, existing approaches based on deep learning for cloud segmentation have largely concentrated on satellite data (Drönner et al, ; Lu et al, ). Hence, it deserves to exploring the cloud segmentation performance by means of deep learning on the ground‐based cloud dataset.…”

Section: Introductionmentioning

confidence: 99%

An Efficient Solution for Semantic Segmentation of Three Ground‐based Cloud Datasets

Song

Cui²,

Liu

2020

Earth and Space Science

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The machine learning approach has shown its state-of-the-art ability to handle segmentation and detection tasks. It is increasingly employed to extract patterns and spatiotemporal features from the ever-increasing stream of Earth system data. However, there is still a significant challenge, which is the generalization capability of the model on cloud images in different types and weather conditions. After studying several popular methods, we propose a semantic segmentation neural network for cloud segmentation. It extracts features learned by source and target domains in an end-to-end behavior, which can address the problem of significant lack of labels in the observed cloud image data. It is further evaluated on the Singapore Whole Sky Image Segmentation (SWIMSEG) dataset by using Mean Intersection-over-Union, recall, F-score, and accuracy matrices. The scores of these matrices are 86%, 97%, 92%, and 96%, which prove that it has excellent efficiency and robustness. Most importantly, a new benchmark based on the SWIMSEG dataset for the task of cloud segmentation is introduced. The others, BENCHMARK, Cirrus Cumulus Stratus Nimbus are evaluated through the model trained from the SWIMSEG dataset by way of visualization. Plain Language Summary The machine learning approaches offer a new view about howto effectively and comprehensively understand ground-based cloud datasets. The essential advantage of deep learning methods is that it can extract more critical cloud features automatically than traditional algorithms, such as spatiotemporal features. Therefore, it is worth exploring the possibility of cloud segmentation with the help of deep learning techniques. We first introduce a semantic segmentation neural network for cloud segmentation problems after measuring a few classic neural networks. The results exceed the traditional methods by a large margin with standard evaluation matrices, such as Mean Intersection-over-Union, recall, F-score, and accuracy. The scores achieved here may accomplish as a baseline for competitive development. Second, the trained model is used to produce a few cloud masks in two public datasets: BENCHMARK, Singapore Whole Sky Image Segmentation, and their respective performance is further evaluated. Finally, the segmentation results show the excellent performance and generalization in another untrained dataset, Cirrus Cumulus Stratus Nimbus.

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P_Segnet and NP_Segnet: New Neural Network Architectures for Cloud Recognition of Remote Sensing Images

Cited by 29 publications

References 23 publications

Improved Winter Wheat Spatial Distribution Extraction from High-Resolution Remote Sensing Imagery Using Semantic Features and Statistical Analysis

Improved Winter Wheat Spatial Distribution Extraction from High-Resolution Remote Sensing Imagery Using Semantic Features and Statistical Analysis

Rain-Contaminated Region Segmentation of X-Band Marine Radar Images With an Ensemble of SegNets

An Efficient Solution for Semantic Segmentation of Three Ground‐based Cloud Datasets

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