Bag-of-Words and Object-Based Classification for Cloud Extraction From Satellite Imagery

Yuan, Yi; Hu, Xiangyun

doi:10.1109/jstars.2015.2431676

Cited by 85 publications

(47 citation statements)

References 27 publications

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“…To fully evaluate the efficiency of the cloud detection algorithm, the proposed method was compared to some automatic cloud detection methods [18,22,23], some popular automatic image segmentation methods [33][34][35], and some interactive image segmentation methods [36,37]. Forty-two scenes of GF-1 images about 4500 pixels × 4500 pixels in size were used in the experiments.…”

Section: Resultsmentioning

confidence: 99%

“…First, the proposed method was compared with three other automatic cloud detection methods: k-means + ICM (Iterated Conditional Model) [18], RGB refinement [22] and SIFT + GrabCut [23], as shown in Figure 16. It can be seen that the visual effect of the proposed method was the best result among all four methods.…”

Section: Methods and Resultsmentioning

confidence: 99%

“…Zhang, et al (2012) proposed an automatic and effective cloud detection algorithm (denoted as RGB refinement in this paper) based on the properties derived from observations and statistic results on a large number of images with cloud layers [22]. Yuan, et al (2015) proposed an automatic cloud detection (denoted as SIFT + GrabCut in this paper) based on object classification of image features [23]. A brief introduction of each automatic cloud detection algorithm is provided in Table 1.…”

Section: Introductionmentioning

confidence: 99%

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Cloud Extraction from Chinese High Resolution Satellite Imagery by Probabilistic Latent Semantic Analysis and Object-Based Machine Learning

2016

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Automatic cloud extraction from satellite imagery is a vital process for many applications in optical remote sensing since clouds can locally obscure the surface features and alter the reflectance. Clouds can be easily distinguished by the human eyes in satellite imagery via remarkable regional characteristics, but finding a way to automatically detect various kinds of clouds by computer programs to speed up the processing efficiency remains a challenge. This paper introduces a new cloud detection method based on probabilistic latent semantic analysis (PLSA) and object-based machine learning. The method begins by segmenting satellite images into superpixels by Simple Linear Iterative Clustering (SLIC) algorithm while also extracting the spectral, texture, frequency and line segment features. Then, the implicit information in each superpixel is extracted from the feature histogram through the PLSA model by which the descriptor of each superpixel can be computed to form a feature vector for classification. Thereafter, the cloud mask is extracted by optimal thresholding and applying the Support Vector Machine (SVM) algorithm at the superpixel level. The GrabCut algorithm is then applied to extract more accurate cloud regions at the pixel level by assuming the cloud mask as the prior knowledge. When compared to different cloud detection methods in the literature, the overall accuracy of the proposed cloud detection method was up to 90 percent for ZY-3 and GF-1 images, which is about a 6.8 percent improvement over the traditional spectral-based methods. The experimental results show that the proposed method can automatically and accurately detect clouds using the multispectral information of the available four bands.

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Section: Resultsmentioning

confidence: 99%

Section: Methods and Resultsmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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Cloud Extraction from Chinese High Resolution Satellite Imagery by Probabilistic Latent Semantic Analysis and Object-Based Machine Learning

2016

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“…Mean AP (mAP) computes the average value of AP over all object categories. AP and mAP are used as the quantitative indicators in object detection [6,9]. Most papers recognize higher AP as proof of benchmark beating.…”

Section: Average Precisionmentioning

confidence: 99%

“…The second method involves extracting transform invariance features. By finding scale-invariant feature transform descriptors, researchers created scaleinvariant feature transform (SIFT) [6] and histograms of oriented gradients (HOG) [7], which are widely used in computer vision and other image related areas.…”

Section: Introductionmentioning

confidence: 99%

Deformable ConvNet with Aspect Ratio Constrained NMS for Object Detection in Remote Sensing Imagery

Wang

et al. 2017

Remote Sensing

104

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Convolutional neural networks (CNNs) have demonstrated their ability object detection of very high resolution remote sensing images. However, CNNs have obvious limitations for modeling geometric variations in remote sensing targets. In this paper, we introduced a CNN structure, namely deformable ConvNet, to address geometric modeling in object recognition. By adding offsets to the convolution layers, feature mapping of CNN can be applied to unfixed locations, enhancing CNNs' visual appearance understanding. In our work, a deformable region-based fully convolutional networks (R-FCN) was constructed by substituting the regular convolution layer with a deformable convolution layer. To efficiently use this deformable convolutional neural network (ConvNet), a training mechanism is developed in our work. We first set the pre-trained R-FCN natural image model as the default network parameters in deformable R-FCN. Then, this deformable ConvNet was fine-tuned on very high resolution (VHR) remote sensing images. To remedy the increase in lines like false region proposals, we developed aspect ratio constrained non maximum suppression (arcNMS). The precision of deformable ConvNet for detecting objects was then improved. An end-to-end approach was then developed by combining deformable R-FCN, a smart fine-tuning strategy and aspect ratio constrained NMS. The developed method was better than a state-of-the-art benchmark in object detection without data augmentation.

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Cloud Detection in High-Resolution Multispectral Satellite Imagery Using Deep Learning

Morales

Huaman

Telles

2018

Lecture Notes in Computer Science

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Bag-of-Words and Object-Based Classification for Cloud Extraction From Satellite Imagery

Cited by 85 publications

References 27 publications

Cloud Extraction from Chinese High Resolution Satellite Imagery by Probabilistic Latent Semantic Analysis and Object-Based Machine Learning

Cloud Extraction from Chinese High Resolution Satellite Imagery by Probabilistic Latent Semantic Analysis and Object-Based Machine Learning

Deformable ConvNet with Aspect Ratio Constrained NMS for Object Detection in Remote Sensing Imagery

Cloud Detection in High-Resolution Multispectral Satellite Imagery Using Deep Learning

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