Research on a novel extraction method using Deep Learning based on GF-2 images for aquaculture areas

Cheng, Bo; Liang, Chenbin; Xunan, Liu; Liu, Yueming; Ma, Xiaoteng; Wang, Guizhou

doi:10.1080/01431161.2019.1706009

Cited by 56 publications

(37 citation statements)

References 14 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…We made comparative experiments with FCN8s, UNet, SegNet, and HDCUNet [6] based on 8000 labeled GF-2 samples (Table 9). Note that the validation accuracy and test accuracy of our baseline model are slightly inferior to HDCUNet, but they are effectively improved with the addition of the adversarial loss.…”

Section: Algorithm Validation 431 Comparison With Other Methodsmentioning

confidence: 99%

“…Coastal aquaculture areas, as a typical area for remote sensing, are vulnerable to storm-tide disasters, and are important for the government's scientific management and planning of aquaculture resources. In order to obtain the information of aquaculture areas, there are more and more researchers paying attention to using remote sensing technology and machine learning, and a series of research works has ensued [1][2][3][4][5][6][7]. At present, researchers use expert experience [8][9][10], characteristic learning [11][12][13][14], threshold segmentation [15,16], and semantic segmentation networks [6] to extract aquaculture areas, and the practice has proven that these methods work well in this field.…”

Section: Introductionmentioning

confidence: 99%

“…In order to obtain the information of aquaculture areas, there are more and more researchers paying attention to using remote sensing technology and machine learning, and a series of research works has ensued [1][2][3][4][5][6][7]. At present, researchers use expert experience [8][9][10], characteristic learning [11][12][13][14], threshold segmentation [15,16], and semantic segmentation networks [6] to extract aquaculture areas, and the practice has proven that these methods work well in this field. Reference [6] adopted a semantic segmentation network based on hybrid dilated convolution (HDC) [17] to extract aquaculture areas and summed up its four improvements compared to traditional machine learning: (1) the extraction results have clearer boundaries;…”

Section: Introductionmentioning

confidence: 99%

See 2 more Smart Citations

Semi-/Weakly-Supervised Semantic Segmentation Method and Its Application for Coastal Aquaculture Areas Based on Multi-Source Remote Sensing Images—Taking the Fujian Coastal Area (Mainly Sanduo) as an Example

et al. 2021

Self Cite

View full text Add to dashboard Cite

Coastal aquaculture areas are some of the main areas to obtain marine fishery resources and are vulnerable to storm-tide disasters. Obtaining the information of coastal aquaculture areas quickly and accurately is important for the scientific management and planning of aquaculture resources. Recently, deep neural networks have been widely used in remote sensing to deal with many problems, such as scene classification and object detection, and there are many data sources with different spatial resolutions and different uses with the development of remote sensing technology. Thus, using deep learning networks to extract coastal aquaculture areas often encounters the following problems: (1) the difficulty in labeling; (2) the poor robustness of the model; (3) the spatial resolution of the image to be processed is inconsistent with that of the existing samples. In order to fix these problems, this paper proposes a novel semi-/weakly-supervised method, the semi-/weakly-supervised semantic segmentation network (Semi-SSN), and adopts 3 data sources: GaoFen-2 image, GaoFen-1(PMS)image, and GanFen-1(WFV)image with a 0.8 m, 2 m, and 16 m spatial resolution, respectively, and through experiments, we analyze the extraction effect of the model comprehensively. After comparing with other the-state-of-art methods and verifying on an open remote sensing dataset, we take the Fujian coastal area (mainly Sanduo) as the experimental area and employ our method to detect the effect of storm-tide disasters on coastal aquaculture areas, monitor the production, and make the distribution map of coastal aquaculture areas.

show abstract

Section: Algorithm Validation 431 Comparison With Other Methodsmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

See 1 more Smart Citation

Semi-/Weakly-Supervised Semantic Segmentation Method and Its Application for Coastal Aquaculture Areas Based on Multi-Source Remote Sensing Images—Taking the Fujian Coastal Area (Mainly Sanduo) as an Example

et al. 2021

Self Cite

View full text Add to dashboard Cite

show abstract

“…In fact, volcanic ash cloud is affected by meteorological factors at the time of the eruption, such as the geological conditions, wind direction and wind speed, etc. The location, height and coverage range of volcanic ash cloud are often very uncertain [3][4][5]. As an important space-to-ground observation technology, satellite remote sensing can obtain dynamic change information of volcanic ash cloud in a timely, accurate and efficient manner [6][7][8][9][10].…”

Section: Introductionmentioning

confidence: 99%

Identification of Sequential Feature for Volcanic Ash Cloud Using FNN-LSTM Collaborative Computing

Liu

Sun

et al. 2021

Lecture Notes of the Institute for Computer Sciences, Social Informatics and Telecommunications Engineering

View full text Add to dashboard Cite

Collaborative computing performs quickly and accurately the task via combining the multimedia, multi-methods, and multi-clients. Analyzing of traditional feedforward neural network (FNN), long short-term memory (LSTM) neural networks and remote sensing data, this paper proposes a new identification method of sequential feature based on FNN-LSTM collaborative calculation in the volcanic ash cloud monitoring. In this method, combining remote sensing data, the FNN network is used firstly to construct the identification model of volcanic ash cloud. Next, the LSTM network is used to identify the sequential feature of dynamic changes in volcanic ash cloud based on the text data of the volcanic ash report. And then the simulation and true volcanic ash cloud case is performed and analyzed. The experimental results show that: 1) the proposed method is high in training accuracy with 76.54% and testing accuracy with 77%, respectively, and has obvious advantages for small-scale data volumes; 2) the total accuracy and RMS of the simulation analysis reached 79.05% and 0.0149, respectively, it verified the feasibility and effectiveness in the prediction of spatiotemporal evolution; 3) the anti-noise property and the image segmentation effect is good, the obtained sequential feature of the volcanic ash cloud are closer to the actual diffusion. It can provide a reference for sequential feature extraction and dynamic monitoring of volcanic ash cloud in complex environments.

show abstract

“…Remote sensing has the ability of repetitive observation and large-scale spatial coverage, which could meet the requirements of accurate and rapid monitoring of aquaculture ponds, and is suitable for mapping the spatial distribution of aquaculture ponds at a large scale [15]. Optical sensors have been widely exploited to map aquaculture ponds at multiple scales such as Landsat TM/ETM+/OLI, ASTER, Rapid-eye, QuickBird, and WorldView-2 [13,[15][16][17][18][19][20][21][22][23][24][25][26][27][28]. Optical satellites have advantages of long time sequences and can be used for long-time dynamic monitoring.…”

Section: Introductionmentioning

confidence: 99%

Nation-Scale Mapping of Coastal Aquaculture Ponds with Sentinel-1 SAR Data Using Google Earth Engine

et al. 2020

View full text Add to dashboard Cite

Global rapid expansion of the coastal aquaculture industry has made great contributions to enhance food security, but has also caused a series of ecological and environmental issues. Sustainable management of coastal areas requires the explicit and efficient mapping of the spatial distribution of aquaculture ponds. In this study, a Google Earth Engine (GEE) application was developed for mapping coastal aquaculture ponds at a national scale with a novel classification scheme using Sentinel-1 time series data. Relevant indices used in the classification mainly include the water index, texture, and geometric metrics derived from radar backscatter, which were then used to segment and classify aquaculture ponds. Using this approach, we classified aquaculture ponds for the full extent of the coastal area in Vietnam with an overall accuracy of 90.16% (based on independent sample evaluation). The approach, enabling wall-to-wall mapping and area estimation, is essential to the efficient monitoring and management of aquaculture ponds. The classification results showed that aquaculture ponds are widely distributed in Vietnam’s coastal area and are concentrated in the Mekong River Delta and Red River delta (85.14% of the total area), which are facing the increasing collective risk of climate change (e.g., sea level rise and salinity intrusion). Further investigation of the classification results also provides significant insights into the stability and deliverability of the approach. The water index derived from annual median radar backscatter intensity was determined to be efficient at mapping water bodies, likely due to its strong response to water bodies regardless of weather. The geometric metrics considering the spatial variation of radar backscatter patterns were effective at distinguishing aquaculture ponds from other water bodies. The primary use of GEE in this approach makes it replicable and transferable by other users. Our approach lays a solid foundation for intelligent monitoring and management of coastal ecosystems.

show abstract

Research on a novel extraction method using Deep Learning based on GF-2 images for aquaculture areas

Cited by 56 publications

References 14 publications

Semi-/Weakly-Supervised Semantic Segmentation Method and Its Application for Coastal Aquaculture Areas Based on Multi-Source Remote Sensing Images—Taking the Fujian Coastal Area (Mainly Sanduo) as an Example

Semi-/Weakly-Supervised Semantic Segmentation Method and Its Application for Coastal Aquaculture Areas Based on Multi-Source Remote Sensing Images—Taking the Fujian Coastal Area (Mainly Sanduo) as an Example

Identification of Sequential Feature for Volcanic Ash Cloud Using FNN-LSTM Collaborative Computing

Nation-Scale Mapping of Coastal Aquaculture Ponds with Sentinel-1 SAR Data Using Google Earth Engine

Contact Info

Product

Resources

About