Cloud detection algorithm using SVM with SWIR2 and tasseled cap applied to Landsat 8

Joshi, P. P.; Wynne, Randolph H.; Thomas, Valerie A.

doi:10.1016/j.jag.2019.101898

Cited by 37 publications

(21 citation statements)

References 31 publications

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“…(i) If all the training data points in a node belong to the same class, then the node label is assigned as the data label; (ii) If there are different labels in a node, the SVM structure is used to classify the data stored in this node. Furthermore, to evaluate the RFM performance, the true positive rate (TPR; the percentage of positive instances correctly classified), the false positive rate (FPR; the percentage of negative instances misclassified), the false negative rate (FNR; the percentage of positive instances misclassified), and the true negative rate (TNR; the percentage of negative instances correctly classified) can be used [52,65,[78][79][80]. These indices are given by:…”

Section: The Proposed Random Forest Machine (Rfm) For Gis-based Landslide Susceptibility Predictionmentioning

confidence: 99%

A Novel GIS-Based Random Forest Machine Algorithm for the Spatial Prediction of Shallow Landslide Susceptibility

Dang

Hoang

Nguyen

et al. 2020

Forests

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This study developed and verified a new hybrid machine learning model, named random forest machine (RFM), for the spatial prediction of shallow landslides. RFM is a hybridization of two state-of-the-art machine learning algorithms, random forest classifier (RFC) and support vector machine (SVM), in which RFC is used to generate subsets from training data and SVM is used to build decision functions for these subsets. To construct and verify the hybrid RFM model, a shallow landslide database of the Lang Son area (northern Vietnam) was prepared. The database consisted of 101 shallow landslide polygons and 14 conditioning factors. The relevance of these factors for shallow landslide susceptibility modeling was assessed using the ReliefF method. Experimental results pointed out that the proposed RFM can help to achieve the desired prediction with an F1 score of roughly 0.96. The performance of the RFM was better than those of benchmark approaches, including the SVM, RFC, and logistic regression. Thus, the newly developed RFM is a promising tool to help local authorities in shallow landslide hazard mitigations.

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Section: The Proposed Random Forest Machine (Rfm) For Gis-based Landslide Susceptibility Predictionmentioning

confidence: 99%

A Novel GIS-Based Random Forest Machine Algorithm for the Spatial Prediction of Shallow Landslide Susceptibility

Dang

Hoang

Nguyen

et al. 2020

Forests

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“…These data each have four spectral bands that overlap with those of the Proba-V satellite. Since most multispectral data do not include thermal infrared bands, and Joshi et al [22] proved that clouds can be effectively detected without thermal bands, CECD uses only the top of atmosphere (TOA) reflectance bands ranging from the visible to short-wave infrared bands as inputs for each type of satellite image (Table 2). Because all the spectral bands in Landsat-8 data have a 30-m spatial resolution, the CECD will generate 30-m cloud masks for Landsat-8 data.…”

Section: Datasets and Preprocessingmentioning

confidence: 99%

“…In addition, the F-measure (F.M. ), which is a single class-specific accuracy metric and the complement of the commission, omission, and overall error, was also calculated using a balanced weighting (β = 1) [22,60,61]. Table 3 summarizes the quantitative accuracy results, and Figure 6 illustrates a comparison between the cloud-detection results produced by CECD and FMASK for three typical Landsat-8 scenes and three Sentinel-2 scenes.…”

Section: Comparison With Fmask Cloud-detection Algorithmmentioning

confidence: 99%

A Novel Classification Extension-Based Cloud Detection Method for Medium-Resolution Optical Images

et al. 2020

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Accurate cloud detection using medium-resolution multispectral satellite imagery (such as Landsat and Sentinel data) is always difficult due to the complex land surfaces, diverse cloud types, and limited number of available spectral bands, especially in the case of images without thermal bands. In this paper, a novel classification extension-based cloud detection (CECD) method was proposed for masking clouds in the medium-resolution images. The new method does not rely on thermal bands and can be used for masking clouds in different types of medium-resolution satellite imagery. First, with the support of low-resolution satellite imagery with short revisit periods, cloud and non-cloud pixels were identified in the resampled low-resolution version of the medium-resolution cloudy image. Then, based on the identified cloud and non-cloud pixels and the resampled cloudy image, training samples were automatically collected to develop a random forest (RF) classifier. Finally, the developed RF classifier was extended to the corresponding medium-resolution cloudy image to generate an accurate cloud mask. The CECD method was applied to Landsat-8 and Sentinel-2 imagery to test the performance for different satellite images, and the well-known function of mask (FMASK) method was employed for comparison with our method. The results indicate that CECD is more accurate at detecting clouds in Landsat-8 and Sentinel-2 imagery, giving an average F-measure value of 97.65% and 97.11% for Landsat-8 and Sentinel-2 imagery, respectively, as against corresponding results of 90.80% and 88.47% for FMASK. It is concluded, therefore, that the proposed CECD algorithm is an effective cloud-classification algorithm that can be applied to the medium-resolution optical satellite imagery.

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“…To avoid manual design and improve the generalization of cloud-detection algorithms, machine-learning techniques that can automatically learn features are applied to clouddetection tasks [35,36], including clustering [37,38], artificial neural networks (ANN) [39,40], random forest (RF) [41,42], support vector machine (SVM) [43][44][45], DL [46][47][48][49][50][51] [52]. The multiscale convolutional feature fusion (MSCFF) cloud-detection method, which integrates multiscale information into convolutional features, was proposed by Zhiwei Li et al [53].…”

Section: Introductionmentioning

confidence: 99%

SFRS-Net: A Cloud-Detection Method Based on Deep Convolutional Neural Networks for GF-1 Remote-Sensing Images

Zheng

Han

et al. 2021

Remote Sensing

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Clouds constitute a major obstacle to the application of optical remote-sensing images as they destroy the continuity of the ground information in the images and reduce their utilization rate. Therefore, cloud detection has become an important preprocessing step for optical remote-sensing image applications. Due to the fact that the features of clouds in current cloud-detection methods are mostly manually interpreted and the information in remote-sensing images is complex, the accuracy and generalization of current cloud-detection methods are unsatisfactory. As cloud detection aims to extract cloud regions from the background, it can be regarded as a semantic segmentation problem. A cloud-detection method based on deep convolutional neural networks (DCNN)—that is, a spatial folding–unfolding remote-sensing network (SFRS-Net)—is introduced in the paper, and the reason for the inaccuracy of DCNN during cloud region segmentation and the concept of space folding/unfolding is presented. The backbone network of the proposed method adopts an encoder–decoder structure, in which the pooling operation in the encoder is replaced by a folding operation, and the upsampling operation in the decoder is replaced by an unfolding operation. As a result, the accuracy of cloud detection is improved, while the generalization is guaranteed. In the experiment, the multispectral data of the GaoFen-1 (GF-1) satellite is collected to form a dataset, and the overall accuracy (OA) of this method reaches 96.98%, which is a satisfactory result. This study aims to develop a method that is suitable for cloud detection and can complement other cloud-detection methods, providing a reference for researchers interested in cloud detection of remote-sensing images.

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Cloud detection algorithm using SVM with SWIR2 and tasseled cap applied to Landsat 8

Cited by 37 publications

References 31 publications

A Novel GIS-Based Random Forest Machine Algorithm for the Spatial Prediction of Shallow Landslide Susceptibility

A Novel GIS-Based Random Forest Machine Algorithm for the Spatial Prediction of Shallow Landslide Susceptibility

A Novel Classification Extension-Based Cloud Detection Method for Medium-Resolution Optical Images

SFRS-Net: A Cloud-Detection Method Based on Deep Convolutional Neural Networks for GF-1 Remote-Sensing Images

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