Object- and pixel-based classifications of macroalgae farming area with high spatial resolution imagery

Zheng, Yuhan; Wu, Jiaping; Wang, Anqi; Jiang, Chen Chen

doi:10.1080/10106049.2017.1333531

Cited by 32 publications

(21 citation statements)

References 51 publications

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“…Previous studies show that object-based classification works better than pixel-based classification in high spatial resolution imagery (Dorren et al 2003;Myint et al 2011;Chen et al 2012;Jebur et al 2013). We have compared these two methods in Porphyra farms identification in Dayu Bay, showing that object-based classification received approximately 10% higher accuracy than pixel-based classification (Zheng et al 2017). Hence, we established our CTs based on the segmented objects rather than pixels of all layers and PCs.…”

Section: Traditional Ctmentioning

confidence: 90%

Remote sensing mapping of macroalgal farms by modifying thresholds in the classification tree

Zheng

Duarte

Jiang

et al. 2018

Geocarto International

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Z, et al. (2018) Remote sensing mapping of macroalgal farms by modifying thresholds in the classification tree. Geocarto International: 1-18. Available: http://dx. AbstractRemote sensing is the main approach used to classify and map aquatic vegetation, and classification tree (CT) analysis is superior to various classification methods. Based on previous studies, modified CT can be developed from traditional CT by adjusting the thresholds based on the statistical relationship between spectral features to classify different images without ground-truth data. However, no studies have yet employed this method to resolve marine vegetation. In this study, three Gao-Fen 1 satellite images obtained with the same sensor , and two features were then employed to extract macroalgae from aquaculture farms from the seawater background. Besides, object-based classification and other image analysis methods were adopted to improve the classification accuracy in this study. Results show that the overall accuracies of traditional CTs for three images are 92.0%, 94.2% and 93.9%, respectively, whereas the overall accuracies of the two corresponding modified CTs for images obtained on January 21, 2015 and November 5, 2014 are 93.1% and 89.5%, respectively. This indicates modified CTs can help map macroalgae with multi-date imagery and monitor the spatiotemporal distribution of macroalgae in coastal environments.

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Section: Traditional Ctmentioning

confidence: 90%

Remote sensing mapping of macroalgal farms by modifying thresholds in the classification tree

Zheng

Duarte

Jiang

et al. 2018

Geocarto International

Self Cite

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“…In facing of various spatial and temporal scales in a complex marine environment, remote sensing technology has substantially improved our ability to observe remote and vast areas at a fraction of the cost of traditional surveys [9]. To extract the marine aquaculture areas from remotely sensed images, previous studies have tried various methods including visual interpretation, spatial structure enhanced analyses [10,11], object-based image analysis (OBIA) [12][13][14], and deep convolutional neural networks (CNNs) [15]. Visual interpretation is used less because it is labor-intensive and time-consuming.…”

Section: Introductionmentioning

confidence: 99%

Finer Resolution Mapping of Marine Aquaculture Areas Using WorldView-2 Imagery and a Hierarchical Cascade Convolutional Neural Network

Deng

et al. 2019

Remote Sensing

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Marine aquaculture plays an important role in seafood supplement, economic development, and coastal ecosystem service provision. The precise delineation of marine aquaculture areas from high spatial resolution (HSR) imagery is vital for the sustainable development and management of coastal marine resources. However, various sizes and detailed structures of marine objects make it difficult for accurate mapping from HSR images by using conventional methods. Therefore, this study attempts to extract marine aquaculture areas by using an automatic labeling method based on the convolutional neural network (CNN), i.e., an end-to-end hierarchical cascade network (HCNet). Specifically, for marine objects of various sizes, we propose to improve the classification performance by utilizing multi-scale contextual information. Technically, based on the output of a CNN encoder, we employ atrous convolutions to capture multi-scale contextual information and aggregate them in a hierarchical cascade way. Meanwhile, for marine objects with detailed structures, we propose to refine the detailed information gradually by using a series of long-span connections with fine resolution features from the shallow layers. In addition, to decrease the semantic gaps between features in different levels, we propose to refine the feature space (i.e., channel and spatial dimensions) using an attention-based module. Experimental results show that our proposed HCNet can effectively identify and distinguish different kinds of marine aquaculture, with 98% of overall accuracy. It also achieves better classification performance compared with object-based support vector machine and state-of-the-art CNN-based methods, such as FCN-32s, U-Net, and DeeplabV2. Our developed method lays a solid foundation for the intelligent monitoring and management of coastal marine resources.

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“…Enhanced analyses that incorporate features such as texture or average filtering (Fan et al, 2015;Lu et al, 2015;Xiao et al, 2013) are commonly employed for pixelbased approaches. However, these are subject to noise (the salt-and-pepper effect) and decreased accuracy (Zheng et al, 2017). OBIA has been widely used for the detailed interpretation of marine aquaculture from remote sensing images (Fu et al, 2019a;Wang et al, 2017;Zheng et al, 2017).…”

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confidence: 99%

A new satellite-derived dataset for marine aquaculture areas in the China's coastal region

Fu¹,

Deng²,

Wang³

et al. 2020

Preprint

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Abstract. China has witnessed extensive development of the marine aquaculture industry over recent years. However, such rapid and disordered expansion posed risks to coastal environment, economic development, and biodiversity protection. This study aimed to produce an accurate national-scale marine aquaculture map at a spatial resolution of 16 m, using a proposed deep convolution neural networks (CNNs) based model and applied it to satellite data from China's GF-1 sensor in an end-to-end way. The analyses used homogeneous CNNs to extract high-dimensional features from the input imagery and preserve information at full resolution. Then, a hierarchical cascade architecture was followed to capture multi-scale features and contextual information. This hierarchical cascade homogeneous neural network (HCHNet) was found to achieve better classification performance than current state-of-the-art models (FCN-32s, Deeplab V2, U-Net, and HCNet). The resulting marine aquaculture area map has an overall classification accuracy > 95 % (95.2 %–96.4, 95 % confidence interval). And marine aquaculture was found to cover a total area of ~1100 km2 (1096.8 km2–1110.6 km2, 95 % confidence interval) in China, of which more than 85 % are marine plant culture areas, with 87 % found in the Fujian, Shandong, Liaoning, and Jiangsu provinces. The results confirm the applicability and effectiveness of HCHNet when applied to GF-1 data, identifying notable spatial distributions of different marine aquaculture areas and supporting the sustainable management and ecological assessments of coastal resources at a national scale. The national-scale marine aquaculture map at 16 m spatial resolution is published in the Google Maps kmz File Format with georeferencing information at https://doi.org/10.5281/zenodo.3881612 (Fu et al., 2020).

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Object- and pixel-based classifications of macroalgae farming area with high spatial resolution imagery

Cited by 32 publications

References 51 publications

Remote sensing mapping of macroalgal farms by modifying thresholds in the classification tree

Remote sensing mapping of macroalgal farms by modifying thresholds in the classification tree

Finer Resolution Mapping of Marine Aquaculture Areas Using WorldView-2 Imagery and a Hierarchical Cascade Convolutional Neural Network

A new satellite-derived dataset for marine aquaculture areas in the China's coastal region

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