Missing Data and Regression Models for Spatial Images

Zhang, Jun; Clayton, Murray K.; Townsend, Philip A.

doi:10.1109/tgrs.2014.2345513

Cited by 24 publications

(7 citation statements)

References 10 publications

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“…Furthermore, Li et al [33] established a relationship map between the original and temporal data, with multi-temporal dictionary learning based on sparse representation. Zhang et al [34] presented a functional concurrent linear model (FCLM) to address missing data problems in series of temporal images. Chen et al [35] developed a novel spatially and temporally weighted regression (STWR) model for cloud removal to produce continuous cloud-free Landsat images.…”

Section: Temporal-based Methodsmentioning

confidence: 99%

Missing Data Reconstruction in Remote Sensing Image With a Unified Spatial–Temporal–Spectral Deep Convolutional Neural Network

Zhang

Yuan

Zeng

et al. 2018

IEEE Trans. Geosci. Remote Sensing

344

154

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Because of the internal malfunction of satellite sensors and poor atmospheric conditions such as thick cloud, the acquired remote sensing data often suffer from missing information, i.e., the data usability is greatly reduced. In this paper, a novel method of missing information reconstruction in remote sensing images is proposed. The unified spatial-temporalspectral framework based on a deep convolutional neural network (STS-CNN) employs a unified deep convolutional neural network combined with spatial-temporal-spectral supplementary information. In addition, to address the fact that most methods can only deal with a single missing information reconstruction task, the proposed approach can solve three typical missing information reconstruction tasks: 1) dead lines in Aqua MODIS band 6; 2) the Landsat ETM+ Scan Line Corrector (SLC)-off problem; and 3) thick cloud removal. It should be noted that the proposed model can use multi-source data (spatial, spectral, and temporal) as the input of the unified framework. The results of both simulated and real-data experiments demonstrate that the proposed model exhibits high effectiveness in the three missing information reconstruction tasks listed above.Index Terms-Spatial-temporal-spectral, reconstruction of missing data, deep convolutional neural network, Aqua MODIS band 6, ETM+ SLC-off, cloud removal.

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Section: Temporal-based Methodsmentioning

confidence: 99%

Missing Data Reconstruction in Remote Sensing Image With a Unified Spatial–Temporal–Spectral Deep Convolutional Neural Network

Zhang

Yuan

Zeng

et al. 2018

IEEE Trans. Geosci. Remote Sensing

344

154

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“…The six datasets used in the experiment are glass,hayes-roth,iris, lymphography,monk-2 and tae 1 . Table 1 shows their specific information.…”

Section: Datasetsmentioning

confidence: 99%

“…Classification is a common task in machine learning, which widely exists in many fields, such as computer vision, image processing [1], natural language processing and bioinformatics. The current mainstream classification algorithms include support vector machines [2], neural networks and K-Nearest Neighbors.…”

Section: Introductionmentioning

confidence: 99%

Combining Imputation Method and Feature Weighting Algorithms to Improve the Classification Accuracy of Incomplete Data

Wang

Guo

Xiao

et al. 2022

J. Phys.: Conf. Ser.

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It is a common problem that datasets in pattern classification tasks contain missing values. In order to deal with missing values, the K-Nearest Neighbor Imputation method with Euclidean distance is often used to find K nearest neighbors, and then the mean of these samples is considered as the filling value. Although this method can deal with the problem of missing values well, it cannot effectively deal with the classification task with noise and redundant features because it ignores the importance of features. In order to do this, the paper proposes a novel imputation method. In our method, missing values are firstly filled by using the K-Nearest Neighbor Imputation method based on Euclidean distance, then a feature weighting vector can be obtained by using a feature weighting algorithm trained on the initially filled data, and finally the K-Nearest Neighbor Imputation with weighted Euclidean distance is used to fill missing values. The experimental results on six datasets show that the proposed method can improve the classification accuracy of incomplete data.

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“…A new combination of kernel functions in support vector regression was designed with auxiliary radiometric information [23]. Zhang et al [24] proposed a functional concurrent linear model between cloudy and temporal Landsat 7 images to fill in the missing data. Gao et al [25] proposed a tempo-spectral angle mapping (TSAM) index in the temporal dimension and then conducted the multi-temporal replacement method based on the index.…”

Section: Of 19mentioning

confidence: 99%

Gated Convolutional Networks for Cloud Removal From Bi-Temporal Remote Sensing Images

Dai

Zhang

2020

Remote Sensing

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Pixels of clouds and cloud shadows in a remote sensing image impact image quality, image interpretation, and subsequent applications. In this paper, we propose a novel cloud removal method based on deep learning that automatically reconstructs the invalid pixels with the auxiliary information from multi-temporal images. Our method’s innovation lies in its feature extraction and loss functions, which reside in a novel gated convolutional network (GCN) instead of a series of common convolutions. It takes the current cloudy image, a recent cloudless image, and the mask of clouds as input, without any requirements of external training samples, to realize a self-training process with clean pixels in the bi-temporal images as natural training samples. In our feature extraction, gated convolutional layers, for the first time, are introduced to discriminate cloudy pixels from clean pixels, which make up for a common convolution layer’s lack of the ability to discriminate. Our multi-level constrained joint loss function, which consists of an image-level loss, a feature-level loss, and a total variation loss, can achieve local and global consistency both in shallow and deep levels of features. The total variation loss is introduced into the deep-learning-based cloud removal task for the first time to eliminate the color and texture discontinuity around cloud outlines needing repair. On the WHU cloud dataset with diverse land cover scenes and different imaging conditions, our experimental results demonstrated that our method consistently reconstructed the cloud and cloud shadow pixels in various remote sensing images and outperformed several mainstream deep-learning-based methods and a conventional method for every indicator by a large margin.

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Missing Data and Regression Models for Spatial Images

Cited by 24 publications

References 10 publications

Missing Data Reconstruction in Remote Sensing Image With a Unified Spatial–Temporal–Spectral Deep Convolutional Neural Network

Missing Data Reconstruction in Remote Sensing Image With a Unified Spatial–Temporal–Spectral Deep Convolutional Neural Network

Combining Imputation Method and Feature Weighting Algorithms to Improve the Classification Accuracy of Incomplete Data

Gated Convolutional Networks for Cloud Removal From Bi-Temporal Remote Sensing Images

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