Fuzzy AutoEncode Based Cloud Detection for Remote Sensing Imagery

Shao, Zhenfeng; Deng, Juan; Wang, Lei; Fan, Yuxin; Sumari, Neema Simon; Cheng, Qu

doi:10.3390/rs9040311

Cited by 47 publications

(18 citation statements)

References 42 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Jiao [24] has proposed the urban land density function to characterize the spatial attenuation of urban land density from the city center which is shown in Equation (2): Change detection provides essential information for decision making in the monitoring of land use, assessing global changes, and the nature of the change over the period of time. Change detection was used to identify land use change rates such as urban growth, flooding, drought, and any transition in vegetation [8,20,36,37]. The monitoring of land use/land cover (LULC) change is relevant and useful for understanding the driving force of the change [38].…”

Section: = −mentioning

confidence: 99%

A Geospatial Approach to Sustainable Urban Planning: Lessons for Morogoro Municipal Council, Tanzania

Sumari

Ujoh

et al. 2019

Sustainability

View full text Add to dashboard Cite

Sustainable urban planning is essential in mediating the natural and built environments globally, yet, there is little progress as regards its attainment in developing countries. Rapid and unplanned urbanization continue to threaten the sustainability of many cities in Africa. By selecting Morogoro Municipal Council (MMC) in Tanzania as an example, this study applied well-known remote sensing techniques to understand the dynamics of urban growth and the implications for sustainable urban planning. The study analyzes spatio-temporal characteristics for eighteen years (2000–2018) based on urban land density using gradient and grid-based analysis to further examine land use and urban land density nexus. The results indicate declining urban land densities with distance to the city center, indicating a less compact and fragmented development at the urban fringes; and northward development with limited development to the south of MCC. The knowledge and understanding of the patterns of spatio-temporal conditions, land use planning, and management interventions in MMC are necessary for addressing the inadequacies associated with rapid urbanization within the study area. On this basis, we propose a shift from the modernist to the communicative planning strategy that strongly integrates the urban social, economic, and environmental imperatives, while being adaptable to evolving realities. This plan should also aim to curtail urban sprawl and create a viable city system and economically prosperous city structure for MMC.

show abstract

Section: = −mentioning

confidence: 99%

A Geospatial Approach to Sustainable Urban Planning: Lessons for Morogoro Municipal Council, Tanzania

Sumari

Ujoh

et al. 2019

Sustainability

View full text Add to dashboard Cite

show abstract

“…Most studies that estimate AGB at a small scale use medium and high spatial resolution remote sensing data, such as Landsat TM [27], SPOT [28], [29] or Quickbird [30]; however, these data have low temporal resolution. Remote sensing data are easily affected by meteorological factors such as clouds [31], [32]. Processing the data requires significant computing power, and data acquisition is expensive, hence, the AGB estimation at a large scale is not a straightforward task.…”

Section: Introductionmentioning

confidence: 99%

Spatial Scaling of Forest Aboveground Biomass Using Multi-Source Remote Sensing Data

Wang

Jiao

2020

IEEE Access

View full text Add to dashboard Cite

Accurate estimation of aboveground forest biomass (AGB) at a large scale is important in global carbon cycle, forest productivity, and climate change. Coarse resolution remote sensing data of long time series are often used to estimate large scale AGB, but the result is inaccurate due to the scaling effect caused by nonlinearity in data representation and the existence of mixed pixels containing different forest types and land uses. Improvement in the accuracy of AGB estimated from coarse resolution remote sensing data is urgently needed. Research on spatial scaling of AGB is still lacking, therefore, this paper proposed an approach based on structural analysis of mixed pixels and the Random Forest model (SMPRF) to increase the accuracy of AGB estimated from coarse resolution data. MODIS and SPOT 5 data were used to create forest biomass distribution maps of the study area at two scales. The scaling effect on estimating forest biomass based on remote sensing was analyzed by comparing data from these two datasets. SMPRF, which included a correction factor for the scaling effect on AGB estimated from coarse resolution MODIS data, was used to create a model that scaled from the fine resolution data (SPOT 5) to the coarse resolution data (MODIS). The results showed that the accuracy of AGB estimated from MODIS data was increased using this method. The Pearson correlation coefficient (r) for data verification increased from 0.63 to 0.89 and the root mean squared error decreased from 51.6 Mg•ha-1 to 26.8 Mg•ha-1. The difference tests showed that the changes were extremely significant (p = 0). Thus, SMPRF can significantly improve the accuracy of large scale AGB estimation based on coarse resolution remote sensing data and the feasibility of applying the method proposed in this study to related fields is verified.

show abstract

“…Many studies have been dedicated to coping with the problem of cloud removal, to reduce or eliminate the influence caused by clouds. To some extent, it is equivalent to the image inpainting problem [ 1 ] as long as clouds are accurately detected (see [ 2 , 3 , 4 , 5 ] for more details on cloud detection). Removing clouds is essentially a process of recovering the missing information, and the existing methods can fall into three classes [ 6 , 7 , 8 ]: one class is multispectral complementation based; the second is multitemporal complementation based; and spatial-complementation based methods.…”

Section: Introductionmentioning

confidence: 99%

A Sparse Dictionary Learning-Based Adaptive Patch Inpainting Method for Thick Clouds Removal from High-Spatial Resolution Remote Sensing Imagery

Fan

Yang

Zhou

et al. 2017

Sensors

View full text Add to dashboard Cite

Cloud cover is inevitable in optical remote sensing (RS) imagery on account of the influence of observation conditions, which limits the availability of RS data. Therefore, it is of great significance to be able to reconstruct the cloud-contaminated ground information. This paper presents a sparse dictionary learning-based image inpainting method for adaptively recovering the missing information corrupted by thick clouds patch-by-patch. A feature dictionary was learned from exemplars in the cloud-free regions, which was later utilized to infer the missing patches via sparse representation. To maintain the coherence of structures, structure sparsity was brought in to encourage first filling-in of missing patches on image structures. The optimization model of patch inpainting was formulated under the adaptive neighborhood-consistency constraint, which was solved by a modified orthogonal matching pursuit (OMP) algorithm. In light of these ideas, the thick-cloud removal scheme was designed and applied to images with simulated and true clouds. Comparisons and experiments show that our method can not only keep structures and textures consistent with the surrounding ground information, but also yield rare smoothing effect and block effect, which is more suitable for the removal of clouds from high-spatial resolution RS imagery with salient structures and abundant textured features.

show abstract

Fuzzy AutoEncode Based Cloud Detection for Remote Sensing Imagery

Cited by 47 publications

References 42 publications

A Geospatial Approach to Sustainable Urban Planning: Lessons for Morogoro Municipal Council, Tanzania

A Geospatial Approach to Sustainable Urban Planning: Lessons for Morogoro Municipal Council, Tanzania

Spatial Scaling of Forest Aboveground Biomass Using Multi-Source Remote Sensing Data

A Sparse Dictionary Learning-Based Adaptive Patch Inpainting Method for Thick Clouds Removal from High-Spatial Resolution Remote Sensing Imagery

Contact Info

Product

Resources

About