Convolutional Neural Networks for Cloud Screening: Transfer Learning from Landsat-8 to Proba-V

Mateo-García, Gonzalo; Gómez-Chova, L.

doi:10.1109/igarss.2018.8517975

Cited by 8 publications

(8 citation statements)

References 7 publications

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“…The overall accuracy obtained by Cloud-Net+ is 3.5% higher than that of RS-Net. We have also compared the results of Cloud-Net+ with two other state-ofthe-art methods (the Multitemporal model [12] and Simplified U-Net [25]). To have a fair comparison in both cases, we have used the same fraction of Biome 8 dataset.…”

Section: ) Results Over Biome 8 Datasetmentioning

confidence: 99%

“…In their work, a built-in boundary refinement approach (BR blocks) has been incorporated into the proposed CDnet to avoid further post-processing. Mateo-García et al [25] have tested a simplified U-Net for transfer learning of the cloud detection in Landsat 8 images to Proba-V satellite images. They have shown that in the case of lack of Proba-V ground truths for training, it is possible to train a system on Landsat 8 images (which their proper ground truths are more available) and then using the found weights to predict the cloud locations in Proba-V images.…”

Section: Related Workmentioning

confidence: 99%

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Cloud and Cloud Shadow Segmentation for Remote Sensing Imagery Via Filtered Jaccard Loss Function and Parametric Augmentation

Mohajerani

Saeedi

2021

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

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Cloud and cloud shadow segmentation are fundamental processes in optical remote sensing image analysis. Current methods for cloud/shadow identification in geospatial imagery are not as accurate as they should, especially in the presence of snow and haze. This paper presents a deep learningbased framework for the detection of cloud/shadow in Landsat 8 images. Our method benefits from a convolutional neural network, Cloud-Net+ (a modification of our previously proposed Cloud-Net [1]) that is trained with a novel loss function (Filtered Jaccard Loss). The proposed loss function is more sensitive to the absence of foreground objects in an image and penalizes/rewards the predicted mask more accurately than other common loss functions. In addition, a sunlight direction-aware data augmentation technique is developed for the task of cloud shadow detection to extend the generalization ability of the proposed model by expanding existing training sets. The combination of Cloud-Net+, Filtered Jaccard Loss function, and the proposed augmentation algorithm delivers superior results on four public cloud/shadow detection datasets. Our experiments on Pascal VOC dataset exemplifies the applicability and quality of our proposed network and loss function in other computer vision applications.

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Section: ) Results Over Biome 8 Datasetmentioning

confidence: 99%

Section: Related Workmentioning

confidence: 99%

Cloud and Cloud Shadow Segmentation for Remote Sensing Imagery Via Filtered Jaccard Loss Function and Parametric Augmentation

Mohajerani

Saeedi

2021

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

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“…Typically, these masks are not enough to use satellite imagery. The current trend in the selection of satellite images is based on the use of deep machine learning [32][33][34][35]. It can be assumed that the use of deep machine learning will allow to select the necessary satellite images.…”

Section: Introductionmentioning

confidence: 99%

The Use of Deep Machine Learning for the Automated Selection of Remote Sensing Data for the Determination of Areas of Arable Land Degradation Processes Distribution

et al. 2021

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Soil degradation processes are widespread on agricultural land. Ground-based methods for detecting degradation require a lot of labor and time. Remote methods based on the analysis of vegetation indices can significantly reduce the volume of ground surveys. Currently, machine learning methods are increasingly being used to analyze remote sensing data. In this paper, the task is set to apply deep machine learning methods and methods of vegetation indices calculation to automate the detection of areas of soil degradation development on arable land. In the course of the work, a method was developed for determining the location of degraded areas of soil cover on arable fields. The method is based on the use of multi-temporal remote sensing data. The selection of suitable remote sensing data scenes is based on deep machine learning. Deep machine learning was based on an analysis of 1028 scenes of Landsats 4, 5, 7 and 8 on 530 agricultural fields. Landsat data from 1984 to 2019 was analyzed. Dataset was created manually for each pair of “Landsat scene”/“agricultural field number”(for each agricultural field, the suitability of each Landsat scene was assessed). Areas of soil degradation were calculated based on the frequency of occurrence of low NDVI values over 35 years. Low NDVI values were calculated separately for each suitable fragment of the satellite image within the boundaries of each agricultural field. NDVI values of one-third of the field area and lower than the other two-thirds were considered low. During testing, the method gave 12.5% of type I errors (false positive) and 3.8% of type II errors (false negative). Independent verification of the method was carried out on six agricultural fields on an area of 713.3 hectares. Humus content and thickness of the humus horizon were determined in 42 ground-based points. In arable land degradation areas identified by the proposed method, the probability of detecting soil degradation by field methods was 87.5%. The probability of detecting soil degradation by ground-based methods outside the predicted regions was 3.8%. The results indicate that deep machine learning is feasible for remote sensing data selection based on a binary dataset. This eliminates the need for intermediate filtering systems in the selection of satellite imagery (determination of clouds, shadows from clouds, open soil surface, etc.). Direct selection of Landsat scenes suitable for calculations has been made. It allows automating the process of constructing soil degradation maps.

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“…Patch-to-pixel convolutional neural network (CNN) approaches have been applied by Mateo et al [15] for Proba-V and Le Goff et al [16] for Spot 6 and have been also combined with random forest [17]. These approaches have been further proposed for the adaptation between different satellite platforms by Segal et al [18] for WV-2 and Sentinel-2, and by Mateo et al [19] for Landsat-8 and Proba-V. Concerning encoder-decoder segmentation approaches, architectures based on U-Net, Alexnet-FCN, ResNet-50 and Segnet models have been implemented in Landsat 7,8 [20][21][22], Sentinel-2 [23], ZY-3 [24,25], Gaofen-1 [26] and high resolution [27,28] satellites.…”

Section: Introductionmentioning

confidence: 99%

“…The term "fine-tuning" for other types of neural networks (e.g., CNNs) refers to the use of pre-trained neural networks for different applications [19,24,26] than those that they were originally trained for. During fine-tuning, the pre-trained weights are used as initial weights and the network is further trained on the new dataset.…”

Section: Introductionmentioning

confidence: 99%

Fine-Tuning Self-Organizing Maps for Sentinel-2 Imagery: Separating Clouds from Bright Surfaces

Kristollari

Karathanassi

2020

Remote Sensing

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Removal of cloud interference is a crucial step for the exploitation of the spectral information stored in optical satellite images. Several cloud masking approaches have been developed through time, based on direct interpretation of the spectral and temporal properties of clouds through thresholds. The problem has also been tackled by machine learning methods with artificial neural networks being among the most recent ones. Detection of bright non-cloud objects is one of the most difficult tasks in cloud masking applications since spectral information alone often proves inadequate for their separation from clouds. Scientific attention has recently been redrawn on self-organizing maps (SOMs) because of their unique ability to preserve topologic relations, added to the advantage of faster training time and more interpretative behavior compared to other types of artificial neural networks. This study evaluated a SOM for cloud masking Sentinel-2 images and proposed a fine-tuning methodology to separate clouds from bright land areas. The fine-tuning process which is based on the output of the non-fine-tuned network, at first directly locates the neurons that correspond to the misclassified pixels. Then, the incorrect labels of the neurons are altered without applying further training. The fine-tuning method follows a general procedure, thus its applicability is broad and not confined only in the field of cloud-masking. The network was trained on the largest publicly available spectral database for Sentinel-2 cloud masking applications and was tested on a truly independent database of Sentinel-2 cloud masks. It was evaluated both qualitatively and quantitatively with the interpretation of its behavior through multiple visualization techniques being a main part of the evaluation. It was shown that the fine-tuned SOM successfully recognized the bright non-cloud areas and outperformed the state-of-the-art algorithms: Sen2Cor and Fmask, as well as the version that was not fine-tuned.

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Convolutional Neural Networks for Cloud Screening: Transfer Learning from Landsat-8 to Proba-V

Cited by 8 publications

References 7 publications

Cloud and Cloud Shadow Segmentation for Remote Sensing Imagery Via Filtered Jaccard Loss Function and Parametric Augmentation

Cloud and Cloud Shadow Segmentation for Remote Sensing Imagery Via Filtered Jaccard Loss Function and Parametric Augmentation

The Use of Deep Machine Learning for the Automated Selection of Remote Sensing Data for the Determination of Areas of Arable Land Degradation Processes Distribution

Fine-Tuning Self-Organizing Maps for Sentinel-2 Imagery: Separating Clouds from Bright Surfaces

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