scholarly journals RCSANet: A Full Convolutional Network for Extracting Inland Aquaculture Ponds from High-Spatial-Resolution Images

2020 ◽  
Vol 13 (1) ◽  
pp. 92
Author(s):  
Zhe Zeng ◽  
Di Wang ◽  
Wenxia Tan ◽  
Gongliang Yu ◽  
Jiacheng You ◽  
...  
Keyword(s):  
Remote Sensing ◽  
Spatial Resolution ◽  
Large Scale ◽  
High Spatial Resolution ◽  
Convolutional Network ◽  
Water Index ◽  
Inland Lakes ◽  
Natural Lakes ◽  
Aquaculture Ponds ◽  
The Impact

Numerous aquaculture ponds are intensively distributed around inland natural lakes and mixed with cropland, especially in areas with high population density in Asia. Information about the distribution of aquaculture ponds is essential for monitoring the impact of human activities on inland lakes. Accurate and efficient mapping of inland aquaculture ponds using high-spatial-resolution remote-sensing images is a challenging task because aquaculture ponds are mingled with other land cover types. Considering that aquaculture ponds have intertwining regular embankments and that these salient features are prominent at different scales, a Row-wise and Column-wise Self-Attention (RCSA) mechanism that adaptively exploits the identical directional dependency among pixels is proposed. Then a fully convolutional network (FCN) combined with the RCSA mechanism (RCSANet) is proposed for large-scale extraction of aquaculture ponds from high-spatial-resolution remote-sensing imagery. In addition, a fusion strategy is implemented using a water index and the RCSANet prediction to further improve extraction quality. Experiments on high-spatial-resolution images using pansharpened multispectral and 2 m panchromatic images show that the proposed methods gain at least 2–4% overall accuracy over other state-of-the-art methods regardless of regions and achieve an overall accuracy of 85% at Lake Hong region and 83% at Lake Liangzi region in aquaculture pond extraction.

scholarly journals The developement of red mud flood environmental information system and the methodology for the spatial analysis of the degraded area

2015 ◽  
Vol 9 (1) ◽  
pp. 1-11
Author(s):  
Gábor Bakó ◽  
Gábor Kovács ◽  
Zsolt Molnár ◽  
Judit Kirisics ◽  
Eszter Góber ◽  
...  
Keyword(s):  
Remote Sensing ◽  
Spatial Resolution ◽  
Red Mud ◽  
High Spatial Resolution ◽  
Three Dimensional ◽  
Environmental Information ◽  
Evaluation Methodology ◽  
Aerial Photogrammetry ◽  
Impact Area ◽  
The Impact

The red mud disaster occurred on 4th October 2010 in Hungary has raised the necessity of rapid intervention and drew attention to the long-term monitoring of such threat. Both the condition assessment and the change monitoring indispensably required the prompt and detailed spatial survey of the impact area. It was conducted by several research groups - independently - with different recent surveying methods. The high spatial resolution multispectral aerial photogrammetry is the spatially detailed (high resolution) and accurate type of remote sensing. The hyperspectral remote sensing provides more information about material quality of pollutants, with less spatial details and lower spatial accuracy, while LIDAR ensures the three-dimensional shape and terrain models. The article focuses on the high spatial resolution, multispectral electrooptical method and the evaluation methodology of the deriving high spatial resolution ortho image map, presenting the derived environmental information database

scholarly journals Generating High-Quality and High-Resolution Seamless Satellite Imagery for Large-Scale Urban Regions

2019 ◽  
Vol 12 (1) ◽  
pp. 81 ◽  
Author(s):  
Xinghua Li ◽  
Zhiwei Li ◽  
Ruitao Feng ◽  
Shuang Luo ◽  
Chi Zhang ◽  
...  
Keyword(s):  
Remote Sensing ◽  
High Resolution ◽  
Spatial Resolution ◽  
Satellite Imagery ◽  
Satellite Remote Sensing ◽  
Large Scale ◽  
High Spatial Resolution ◽  
Remote Sensing Images ◽  
High Quality ◽  
Geographical Maps

Urban geographical maps are important to urban planning, urban construction, land-use studies, disaster control and relief, touring and sightseeing, and so on. Satellite remote sensing images are the most important data source for urban geographical maps. However, for optical satellite remote sensing images with high spatial resolution, certain inevitable factors, including cloud, haze, and cloud shadow, severely degrade the image quality. Moreover, the geometrical and radiometric differences amongst multiple high-spatial-resolution images are difficult to eliminate. In this study, we propose a robust and efficient procedure for generating high-resolution and high-quality seamless satellite imagery for large-scale urban regions. This procedure consists of image registration, cloud detection, thin/thick cloud removal, pansharpening, and mosaicking processes. Methodologically, a spatially adaptive method considering the variation of atmospheric scattering, and a stepwise replacement method based on local moment matching are proposed for removing thin and thick clouds, respectively. The effectiveness is demonstrated by a successful case of generating a 0.91-m-resolution image of the main city zone in Nanning, Guangxi Zhuang Autonomous Region, China, using images obtained from the Chinese Beijing-2 and Gaofen-2 high-resolution satellites.

scholarly journals Comparing Fully Deep Convolutional Neural Networks for Land Cover Classification with High-Spatial-Resolution Gaofen-2 Images

2020 ◽  
Vol 9 (8) ◽  
pp. 478 ◽  
Author(s):  
Zemin Han ◽  
Yuanyong Dian ◽  
Hao Xia ◽  
Jingjing Zhou ◽  
Yongfeng Jian ◽  
...  
Keyword(s):  
Remote Sensing ◽  
Land Cover ◽  
Spatial Resolution ◽  
Satellite Imagery ◽  
Large Scale ◽  
Near Infrared ◽  
High Spatial Resolution ◽  
Land Cover Classification ◽  
Quantitative Comparison ◽  
Remote Sensing Imagery

Land cover is an important variable of the terrestrial ecosystem that provides information for natural resources management, urban sprawl detection, and environment research. To classify land cover with high-spatial-resolution multispectral remote sensing imagery is a difficult problem due to heterogeneous spectral values of the same object on the ground. Fully convolutional networks (FCNs) are a state-of-the-art method that has been increasingly used in image segmentation and classification. However, a systematic quantitative comparison of FCNs on high-spatial-multispectral remote imagery was not yet performed. In this paper, we adopted the three FCNs (FCN-8s, Segnet, and Unet) for Gaofen-2 (GF2) satellite imagery classification. Two scenes of GF2 with a total of 3329 polygon samples were used in the study area and a systematic quantitative comparison of FCNs was conducted with red, green, blue (RGB) and RGB+near infrared (NIR) inputs for GF2 satellite imagery. The results showed that: (1) The FCN methods perform well in land cover classification with GF2 imagery, and yet, different FCNs architectures exhibited different results in mapping accuracy. The FCN-8s model performed best among the Segnet and Unet architectures due to the multiscale feature channels in the upsampling stage. Averaged across the models, the overall accuracy (OA) and Kappa coefficient (Kappa) were 5% and 0.06 higher, respectively, in FCN-8s when compared with the other two models. (2) High-spatial-resolution remote sensing imagery with RGB+NIR bands performed better than RGB input at mapping land cover, and yet the advantage was limited; the OA and Kappa only increased an average of 0.4% and 0.01 in the RGB+NIR bands. (3) The GF2 imagery provided an encouraging result in estimating land cover based on the FCN-8s method, which can be exploited for large-scale land cover mapping in the future.

scholarly journals Classification of Very-High-Spatial-Resolution Aerial Images Based on Multiscale Features with Limited Semantic Information

2021 ◽  
Vol 13 (3) ◽  
pp. 364
Author(s):  
Han Gao ◽  
Jinhui Guo ◽  
Peng Guo ◽  
Xiuwan Chen
Keyword(s):  
Deep Learning ◽  
Land Cover ◽  
Spatial Resolution ◽  
Large Scale ◽  
High Spatial Resolution ◽  
Training Data ◽  
Aerial Images ◽  
Rural Landscapes ◽  
Feature Representations ◽  
Object Based

Recently, deep learning has become the most innovative trend for a variety of high-spatial-resolution remote sensing imaging applications. However, large-scale land cover classification via traditional convolutional neural networks (CNNs) with sliding windows is computationally expensive and produces coarse results. Additionally, although such supervised learning approaches have performed well, collecting and annotating datasets for every task are extremely laborious, especially for those fully supervised cases where the pixel-level ground-truth labels are dense. In this work, we propose a new object-oriented deep learning framework that leverages residual networks with different depths to learn adjacent feature representations by embedding a multibranch architecture in the deep learning pipeline. The idea is to exploit limited training data at different neighboring scales to make a tradeoff between weak semantics and strong feature representations for operational land cover mapping tasks. We draw from established geographic object-based image analysis (GEOBIA) as an auxiliary module to reduce the computational burden of spatial reasoning and optimize the classification boundaries. We evaluated the proposed approach on two subdecimeter-resolution datasets involving both urban and rural landscapes. It presented better classification accuracy (88.9%) compared to traditional object-based deep learning methods and achieves an excellent inference time (11.3 s/ha).

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