Resolution Enhancement for Large-Scale Land Cover Mapping via Weakly Supervised Deep Learning

Multispectral satellite imagery is the primary data source for monitoring land cover change and characterizing land cover globally. However, the consistency of land cover monitoring is limited by the spatial and temporal resolutions of the acquired satellite images. The public availability of daily high-resolution images is still scarce. This paper aims to fill this gap by proposing a novel spatiotemporal fusion method to enhance daily low spatial resolution land cover mapping using a weakly supervised deep convolutional neural network. We merge Sentinel images and moderate resolution imaging spectroradiometer (MODIS )-derived thematic land cover maps under the application background of massive remote sensing data and the large spatial resolution gaps between MODIS data and Sentinel images. The neural network training was conducted on the public data set SEN12MS, while the validation and testing used ground truth data from the 2020 IEEE Geoscience and Remote Sensing Society data fusion contest. The proposed data fusion method shows that the synthesized land cover map has significantly higher spatial resolution than the corresponding MODIS-derived land cover map. The ensemble approach can be implemented for generating high-resolution time series of satellite images by fusing fine images from Sentinel-1 and -2 and daily coarse images from MODIS.

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Toward a Yearly Country-Scale CORINE Land-Cover Map without Using Images: A Map Translation Approach

Remote Sensing ◽

10.3390/rs13061060 ◽

2021 ◽

Vol 13 (6) ◽

pp. 1060

Author(s):

Luc Baudoux ◽

Jordi Inglada ◽

Clément Mallet

Keyword(s):

Remote Sensing ◽

Land Cover ◽

Time Frame ◽

Superior Performance ◽

Land Cover Mapping ◽

Visual Interpretation ◽

Remote Sensing Images ◽

Corine Land Cover ◽

Land Cover Map ◽

By Products

CORINE Land-Cover (CLC) and its by-products are considered as a reference baseline for land-cover mapping over Europe and subsequent applications. CLC is currently tediously produced each six years from both the visual interpretation and the automatic analysis of a large amount of remote sensing images. Observing that various European countries regularly produce in parallel their own land-cover country-scaled maps with their own specifications, we propose to directly infer CORINE Land-Cover from an existing map, therefore steadily decreasing the updating time-frame. No additional remote sensing image is required. In this paper, we focus more specifically on translating a country-scale remote sensed map, OSO (France), into CORINE Land Cover, in a supervised way. OSO and CLC not only differ in nomenclature but also in spatial resolution. We jointly harmonize both dimensions using a contextual and asymmetrical Convolution Neural Network with positional encoding. We show for various use cases that our method achieves a superior performance than the traditional semantic-based translation approach, achieving an 81% accuracy over all of France, close to the targeted 85% accuracy of CLC.

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Spatio-Temporal Super-Resolution Land Cover Mapping Based on Fuzzy C-Means Clustering

Remote Sensing ◽

10.3390/rs10081212 ◽

2018 ◽

Vol 10 (8) ◽

pp. 1212 ◽

Cited By ~ 4

Author(s):

Xiaohong Yang ◽

Zhong Xie ◽

Feng Ling ◽

Xiaodong Li ◽

Yihang Zhang ◽

...

Keyword(s):

Land Cover ◽

Spatial Pattern ◽

Spatial Resolution ◽

Super Resolution ◽

Land Cover Mapping ◽

Fuzzy C Means Clustering ◽

Land Cover Map ◽

Spatio Temporal ◽

Fine Resolution ◽

Land Cover Maps

Super-resolution land cover mapping (SRM) is a method that aims to generate land cover maps with fine spatial resolutions from the original coarse spatial resolution remotely sensed image. The accuracy of the resultant land cover map produced by existing SRM methods is often limited by the errors of fraction images and the uncertainty of spatial pattern models. To address these limitations in this study, we proposed a fuzzy c-means clustering (FCM)-based spatio-temporal SRM (FCM_STSRM) model that combines the spectral, spatial, and temporal information into a single objective function. The spectral term is constructed with the FCM criterion, the spatial term is constructed with the maximal spatial dependence principle, and the temporal term is characterized by the land cover transition probabilities in the bitemporal land cover maps. The performance of the proposed FCM_STSRM method is assessed using data simulated from the National Land Cover Database dataset and real Landsat images. Results of the two experiments show that the proposed FCM_STSRM method can decrease the influence of fraction errors by directly using the original images as the input and the spatial pattern uncertainty by inheriting land cover information from the existing fine resolution land cover map. Compared with the hard classification and FCM_SRM method applied to mono-temporal images, the proposed FCM_STSRM method produced fine resolution land cover maps with high accuracy, thus showing the efficiency and potential of the novel approach for producing fine spatial resolution maps from coarse resolution remotely sensed images.

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Multitemporal Unmixing of Medium-Spatial-Resolution Satellite Images: A Case Study Using MERIS Images for Land-Cover Mapping

IEEE Transactions on Geoscience and Remote Sensing ◽

10.1109/tgrs.2011.2158320 ◽

2011 ◽

Vol 49 (11) ◽

pp. 4308-4317 ◽

Cited By ~ 29

Author(s):

Raúl Zurita-Milla ◽

Luis Gomez-Chova ◽

Luis Guanter ◽

Jan G. P. W. Clevers ◽

Gustavo Camps-Valls

Keyword(s):

Land Cover ◽

Spatial Resolution ◽

Satellite Images ◽

Land Cover Mapping

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Spatio-Temporal Sub-Pixel Land Cover Mapping of Remote Sensing Imagery Using Spatial Distribution Information From Same-Class Pixels

Remote Sensing ◽

10.3390/rs12030503 ◽

2020 ◽

Vol 12 (3) ◽

pp. 503

Author(s):

Li ◽

Chen ◽

Foody ◽

Wang ◽

Yang ◽

...

Keyword(s):

Remote Sensing ◽

Land Cover ◽

Spatial Resolution ◽

Temporal Frequency ◽

Land Cover Mapping ◽

Remote Sensing Images ◽

Ancillary Data ◽

Data Set ◽

Spatio Temporal ◽

Land Cover Maps

The generation of land cover maps with both fine spatial and temporal resolution would aid the monitoring of change on the Earth’s surface. Spatio-temporal sub-pixel land cover mapping (STSPM) uses a few fine spatial resolution (FR) maps and a time series of coarse spatial resolution (CR) remote sensing images as input to generate FR land cover maps with a temporal frequency of the CR data set. Traditional STSPM selects spatially adjacent FR pixels within a local window as neighborhoods to model the land cover spatial dependence, which can be a source of error and uncertainty in the maps generated by the analysis. This paper proposes a new STSPM using FR remote sensing images that pre- and/or post-date the CR image as ancillary data to enhance the quality of the FR map outputs. Spectrally similar pixels within the locality of a target FR pixel in the ancillary data are likely to represent the same land cover class and hence such same-class pixels can provide spatial information to aid the analysis. Experimental results showed that the proposed STSPM predicted land cover maps more accurately than two comparative state-of-the-art STSPM algorithms.

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Classification of Paddy Fields using Convolutional Neural Network with MODIS imagery in Northeast Asia

10.5194/egusphere-egu2020-13335 ◽

2020 ◽

Author(s):

Seungtaek Jeong ◽

Jonghan Ko ◽

Gwanyong Jeong ◽

Myungjin Choi

Keyword(s):

Neural Network ◽

Land Cover ◽

Convolutional Neural Network ◽

Spatial Resolution ◽

Satellite Images ◽

High Spatial Resolution ◽

Northeast Asia ◽

Land Cover Classification ◽

Paddy Fields ◽

Continental Scale

<p>A satellite image-based classification for crop types can provide information on an arable land area and its changes over time. The classified information is also useful as a base dataset for various geospatial projects to retrieve crop growth and production processes for a wide area. Convolutional neural network (CNN) algorithms based on a deep neural network technique have been frequently applied for land cover classification using satellite images with a high spatial resolution, producing consistent classification outcomes. However, it is still challenging to adopt the coarse resolution images such as Moderate Resolution Imaging Spectroradiometer (MODIS) for classification purposes mainly because of uncertainty from mixed pixels, which can cause difficulty in collecting and labeling actual land cover data. Nevertheless, using coarse images is a very efficient approach for obtaining high temporal and continuous land spectral information for comparatively extensive areas (e.g., those at national and continental scales). In this study, we will classify paddy fields applying a CNN algorithm to MODIS images in Northeast Asia. Time series features of vegetation indices that appear only in paddy fields will be created as 2-dimensional images to use inputs for the classification algorithm. We will use reference land cover maps with a high spatial resolution in Korea and Japan as training and test datasets, employing identified data in person for validation. The current research effort would propose that the CNN-based classification approach using coarse spatial resolution images could have its applicability and reliability for the land cover classification process at a continental scale, providing a direction of its solution for the cause of errors in satellite images with a low spatial resolution.</p>

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Fine Land-Cover Mapping in China Using Landsat Datacube and an Operational SPECLib-Based Approach

Remote Sensing ◽

10.3390/rs11091056 ◽

2019 ◽

Vol 11 (9) ◽

pp. 1056 ◽

Cited By ~ 7

Author(s):

Xiao Zhang ◽

Liangyun Liu ◽

Xidong Chen ◽

Shuai Xie ◽

Yuan Gao

Keyword(s):

Land Cover ◽

Spatial Resolution ◽

Earth Science ◽

Landsat Imagery ◽

European Space Agency ◽

Accurate Method ◽

Land Cover Mapping ◽

Land Cover Types ◽

Multi Temporal ◽

Land Cover Map

Fine resolution land cover information is a vital foundation of Earth science. In this paper, a novel SPECLib-based operational method is presented for the classification of multi-temporal Landsat imagery using reflectance spectra from the spatial-temporal spectral library (SPECLib) for 30 m land-cover mapping for the whole of China. Firstly, using the European Space Agency (ESA) Climate Change Initiative Global Land Cover (CCI_LC) product and the MODIS Version 6 Nadir bidirectional reflectance distribution function adjusted reflectance (NBAR) product (MCD43A4), a global SPECLib with a spatial resolution of 158.85 km (equivalent to 1.43° at the equator) and a temporal resolution of eight days was developed in the sinusoidal projection. Then, the Landsat datacube covering the whole of China was developed using all available observations of Landsat OLI imagery in 2015. Thirdly, the multi-temporal random forest method based on SPECLib was presented to produce an annual land-cover map with 22 land-cover types using the Landsat datacube. Finally, the annual China land-cover map was validated by two different validation systems using approximately 11,000 interpretation points. The mapping results achieved the overall accuracy of 71.3% and 80.7% and the kappa coefficient of 0.664 and 0.757 for the level-2 validation system (19 land-cover types) and the level-1 validation system (nine land-cover types), respectively. Therefore, the case study in China indicates that the proposed SPECLib method is an operational and accurate method for regional/global fine land-cover mapping at a spatial resolution of 30 m.

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Mapping Land Use from High Resolution Satellite Images by Exploiting the Spatial Arrangement of Land Cover Objects

Remote Sensing ◽

10.3390/rs12244158 ◽

2020 ◽

Vol 12 (24) ◽

pp. 4158

Author(s):

Mengmeng Li ◽

Alfred Stein

Keyword(s):

Remote Sensing ◽

Land Use ◽

High Resolution ◽

Land Cover ◽

Spatial Resolution ◽

Satellite Images ◽

Spatial Arrangement ◽

Remote Sensing Images ◽

Land Use Types ◽

High Resolution Satellite Images

Spatial information regarding the arrangement of land cover objects plays an important role in distinguishing the land use types at land parcel or local neighborhood levels. This study investigates the use of graph convolutional networks (GCNs) in order to characterize spatial arrangement features for land use classification from high resolution remote sensing images, with particular interest in comparing land use classifications between different graph-based methods and between different remote sensing images. We examine three kinds of graph-based methods, i.e., feature engineering, graph kernels, and GCNs. Based upon the extracted arrangement features and features regarding the spatial composition of land cover objects, we formulated ten land use classifications. We tested those on two different remote sensing images, which were acquired from GaoFen-2 (with a spatial resolution of 0.8 m) and ZiYuan-3 (of 2.5 m) satellites in 2020 on Fuzhou City, China. Our results showed that land use classifications that are based on the arrangement features derived from GCNs achieved the highest classification accuracy than using graph kernels and handcrafted graph features for both images. We also found that the contribution to separating land use types by arrangement features varies between GaoFen-2 and ZiYuan-3 images, due to the difference in the spatial resolution. This study offers a set of approaches for effectively mapping land use types from (very) high resolution satellite images.

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Urban Growth Assessment of Chennai District by Remote Sensing and Gis Techniques

International Journal of Recent Technology and Engineering - 2 ◽

10.35940/ijrte.f9059.038620 ◽

2020 ◽

Vol 8 (6) ◽

pp. 5119-5125

Keyword(s):

Remote Sensing ◽

Land Use ◽

Land Cover ◽

Urban Growth ◽

Satellite Images ◽

Remote Sensing And Gis ◽

Land Use Land Cover ◽

Gis Techniques ◽

Land Cover Map ◽

Landsat Satellite

Urban growth of Chennai district is exponential and heading towards extreme urbanisation. Hence this necessitates the study of urban growth in Chennai district. The recent advancement in Remote sensing and GIS has an excellent ability to derive various data from the satellite images obtained .This helps us to map, monitor and picturise various aspects of development with respect to their demands. The basic principle of remote sensing is followed as the methodology. By following the methodology correctly and by proper processing of the data acquired from the satellite images, the exact requirements of information can be obtained. The Change in the urban growth of the Chennai district for three decades from 1989 to 2019 have been found by using remote sensing and GIS techniques. The satellite images of various years are obtained from Landsat satellite from the USGS Earth Explorer .The Land use characteristics of Chennai district of each year can be obtained by preparing the land use land cover map of Chennai district by the use of landsat satellite images. The two software namely ArcGIS and ERDAS Imagine are used to create the Land use land cover map. From the Land use land cover map of Chennai district, the change detection and statistical analysis of three decades are done and these analysis clearly shows that the urban growth of Chennai district is constantly increasing and there is a huge decrease in other natural features such as vegetation, water body and barren land. By performing urban trend analysis the urban growth of Chennai district for the upcoming years are predicted to prove the urban agglomeration in Chennai district.

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Mapping the Land Cover of Africa at 10 m Resolution from Multi-Source Remote Sensing Data with Google Earth Engine

Remote Sensing ◽

10.3390/rs12040602 ◽

2020 ◽

Vol 12 (4) ◽

pp. 602 ◽

Cited By ~ 10

Author(s):

Qingyu Li ◽

Chunping Qiu ◽

Lei Ma ◽

Michael Schmitt ◽

Xiao Zhu

Keyword(s):

Remote Sensing ◽

Land Cover ◽

Google Earth ◽

Land Cover Mapping ◽

Landsat 8 ◽

Climate Zone ◽

Analysis Experiment ◽

Natural Land ◽

Land Cover Map ◽

Google Earth Engine

The remote sensing based mapping of land cover at extensive scales, e.g., of whole continents, is still a challenging task because of the need for sophisticated pipelines that combine every step from data acquisition to land cover classification. Utilizing the Google Earth Engine (GEE), which provides a catalog of multi-source data and a cloud-based environment, this research generates a land cover map of the whole African continent at 10 m resolution. This land cover map could provide a large-scale base layer for a more detailed local climate zone mapping of urban areas, which lie in the focus of interest of many studies. In this regard, we provide a free download link for our land cover maps of African cities at the end of this paper. It is shown that our product has achieved an overall accuracy of 81% for five classes, which is superior to the existing 10 m land cover product FROM-GLC10 in detecting urban class in city areas and identifying the boundaries between trees and low plants in rural areas. The best data input configurations are carefully selected based on a comparison of results from different input sources, which include Sentinel-2, Landsat-8, Global Human Settlement Layer (GHSL), Night Time Light (NTL) Data, Shuttle Radar Topography Mission (SRTM), and MODIS Land Surface Temperature (LST). We provide a further investigation of the importance of individual features derived from a Random Forest (RF) classifier. In order to study the influence of sampling strategies on the land cover mapping performance, we have designed a transferability analysis experiment, which has not been adequately addressed in the current literature. In this experiment, we test whether trained models from several cities contain valuable information to classify a different city. It was found that samples of the urban class have better reusability than those of other natural land cover classes, i.e., trees, low plants, bare soil or sand, and water. After experimental evaluation of different land cover classes across different cities, we conclude that continental land cover mapping results can be considerably improved when training samples of natural land cover classes are collected and combined from areas covering each Köppen climate zone.

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