Super-Resolution Land Cover Mapping Based on the Convolutional Neural Network

Jia, Yuanxin; Ge, Yong; Chen, Yuehong; Li, Sanping; Heuvelink, G.B.M.; Ling, Feng

doi:10.3390/rs11151815

Cited by 40 publications

(29 citation statements)

References 58 publications

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“…With the rapid development of cities and the expanding population, food security has increasingly become an issue of widespread concern. As an important aspect of land-use and land-cover mapping, crop mapping also plays an important role in watershed modeling, as well as crop modeling [1][2][3]. Gathering cropland information can help us to identify the crucial problems we are facing, such as the shrinkage of cropland area caused by urban sprawl, groundwater overdraft due to cropland irrigation, and land degradation due to over-reclamation [4].…”

Section: Introductionmentioning

confidence: 99%

Optimal Temporal Window Selection for Winter Wheat and Rapeseed Mapping with Sentinel-2 Images: A Case Study of Zhongxiang in China

et al. 2020

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Currently, the main remote sensing-based crop mapping methods are based on spectral-temporal features. However, there has been a lack research on the selection of the multi-temporal images, and most of the methods are based on the use of all the available images during the cycle of crop growth. In this study, in order to explore the optimal temporal window for crop mapping with limited remote sensing data, we tested all possible combinations of temporal windows in an exhaustive manner, and made a comprehensive consideration of the spatial accuracy and statistical accuracy as evaluation indices. We collected all the available cloud-free Sentinel-2 multi-spectral images for the winter wheat and rapeseed growth periods in the study area in southern China, and used the random forest (RF) method as the classifier to identify the optimal temporal window. The spatial and statistical accuracies of all the results were assessed by using ground survey data and local agricultural census data. The optimal temporal window for the mapping of winter wheat and rapeseed in the study area was obtained by identifying the best-performing set of results. In addition, the variable importance (VI) index was used to evaluate the importance of the different bands for crop mapping. The results of the spatial accuracy, statistical accuracy, and the VI showed that the combinations of images from the later stages of crop growth were more suitable for crop mapping.

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Section: Introductionmentioning

confidence: 99%

Optimal Temporal Window Selection for Winter Wheat and Rapeseed Mapping with Sentinel-2 Images: A Case Study of Zhongxiang in China

et al. 2020

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“…Not only to enhance the spatial resolution of aerial images, but the super-resolution technique can also be applied to enhance the spatial resolution of land cover maps. Jia et al (2019) showed that CNN can predict a finer land cover map given a coarse remote sensing image.…”

Section: Introductionmentioning

confidence: 99%

Supervised conversion from Landsat-8 images to Sentinel-2 images with deep learning

Isa

Suharjito

Kusuma

et al. 2021

European Journal of Remote Sensing

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In a specific remote sensing study design, the utilization of images from a particular satellite is necessary. However, the images might be unavailable in a certain time range. Therefore, a conversion method from available remote sensing images at the time range is required. In this paper, we proposed machine learning models that are capable to convert Landsat-8 images to Sentinel-2 images. The models are inspired by the advancement of super-resolution model based on Deep learning. The result of this study shows that the proposed models can predict Sentinel-2 images which are quantitatively and qualitatively similar to the original images.

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“…Specifically, in these methods, first, the relationship between an image with a fine spatial resolution and the corresponding image with a coarse spatial resolution is established through the training process with a large amount of training data, and then the trained model is used to super-resolve the testing coarse-spatial-resolution image. These methods have been successfully applied to the SR of sea surface temperature (SST) imagery (Ping et al, 2021) and SR mapping of land cover Jia et al, 2019). Therefore, the CNN-based SR methods have great potential in the field of image downscaling, especially fine-spatial-resolution IST images and lead maps that are useful in THF estimation.…”

Section: Introductionmentioning

confidence: 99%

Estimating subpixel turbulent heat flux over leads from MODIS thermal infrared imagery with deep learning

Yin

et al. 2021

The Cryosphere

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Abstract. The turbulent heat flux (THF) over leads is an important parameter for climate change monitoring in the Arctic region. THF over leads is often calculated from satellite-derived ice surface temperature (IST) products, in which mixed pixels containing both ice and open water along lead boundaries reduce the accuracy of calculated THF. To address this problem, this paper proposes a deep residual convolutional neural network (CNN)-based framework to estimate THF over leads at the subpixel scale (DeepSTHF) based on remotely sensed images. The proposed DeepSTHF provides an IST image and the corresponding lead map with a finer spatial resolution than the input IST image so that the subpixel-scale THF can be estimated from them. The proposed approach is verified using simulated and real Moderate Resolution Imaging Spectroradiometer images and compared with the conventional cubic interpolation and pixel-based methods. The results demonstrate that the proposed CNN-based method can effectively estimate subpixel-scale information from the coarse data and performs well in producing fine-spatial-resolution IST images and lead maps, thereby providing more accurate and reliable THF over leads.

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Super-Resolution Land Cover Mapping Based on the Convolutional Neural Network

Cited by 40 publications

References 58 publications

Optimal Temporal Window Selection for Winter Wheat and Rapeseed Mapping with Sentinel-2 Images: A Case Study of Zhongxiang in China

Optimal Temporal Window Selection for Winter Wheat and Rapeseed Mapping with Sentinel-2 Images: A Case Study of Zhongxiang in China

Supervised conversion from Landsat-8 images to Sentinel-2 images with deep learning

Estimating subpixel turbulent heat flux over leads from MODIS thermal infrared imagery with deep learning

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