Floating raft aquaculture information automatic extraction based on high resolution SAR images

Fan, Jianchao; Chu, Jialan; Geng, Jie; Zhang, Feng-Shou

doi:10.1109/igarss.2015.7326676

Cited by 32 publications

(15 citation statements)

References 7 publications

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“…There are also some related studies with the extraction of RCA from synthetic aperture radar (SAR) images [15,48,49]. Many of them have developed technologies to extract features based on the pixel-level neighborhood relationship, such as gray-level co-occurrence matrix [50], local binary patterns, and Gabor transform [51].…”

Section: Related Methods and Advantagesmentioning

confidence: 99%

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Coastal Aquaculture Mapping from Very High Spatial Resolution Imagery by Combining Object-Based Neighbor Features

Deng

et al. 2019

Sustainability

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Coastal aquaculture plays an important role in the provision of seafood, the sustainable development of regional and global economy, and the protection of coastal ecosystems. Inappropriate planning of disordered and intensive coastal aquaculture may cause serious environmental problems and socioeconomic losses. Precise delineation and classification of different kinds of aquaculture areas are vital for coastal management. It is difficult to extract coastal aquaculture areas using the conventional spectrum, shape, or texture information. Here, we proposed an object-based method combining multi-scale segmentation and object-based neighbor features to delineate existing coastal aquaculture areas. We adopted the multi-scale segmentation to generate semantically meaningful image objects for different land cover classes, and then utilized the object-based neighbor features for classification. Our results show that the proposed approach effectively identified different types of coastal aquaculture areas, with 96% overall accuracy. It also performed much better than other conventional methods (e.g., single-scale based classification with conventional features) with higher classification accuracy. Our results also suggest that the multi-scale segmentation and neighbor features can obviously improve the classification performance for the extraction of cage culture areas and raft culture areas, respectively. Our developed approach lays a solid foundation for intelligent monitoring and management of coastal ecosystems.Environmental Protection Law, overall marine functional zonation, and nature reserve schemes, have been formulated for the management of coastal areas. However, comprehensive coastal management in China is still a big challenge. Thus, the monitoring and management of coastal aquaculture are imperative to ensure sustainable development of marine aquaculture industry.Remote sensing technology has substantially improved our ability to observe remote or inaccessible areas at a fraction of the cost of traditional surveys [9]. Mapping and monitoring of aquaculture facilities provide decision-makers with important baseline data on production, cultivated area boundaries, and environmental impacts [10,11]. Remote sensing can provide consistent and wide-range monitoring using various sensors to support aquaculture management [12][13][14], which means the mapping of aquaculture facilities can be performed accurately and periodically at selected scales.Previous studies have used visual interpretation, spatial structure enhanced analysis, and object based image analysis (OBIA) to extract coastal aquaculture areas from remotely sensed imagery. Although visual interpretation can achieve the highest accuracy, it takes a lot of time and effort. Thus, it is less used presently. Spatial structure enhancement techniques, such as neighborhood and texture analysis, are the most commonly adopted methods in the classification process [15,16]. OBIA has become a widely used method in the past decades, especially for the classification o...

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Section: Related Methods and Advantagesmentioning

confidence: 99%

“…Thus, it is less used presently. Spatial structure enhancement techniques, such as neighborhood and texture analysis, are the most commonly adopted methods in the classification process [15,16]. OBIA has become a widely used method in the past decades, especially for the classification of very high spatial resolution (VHSR) imagery [17].…”

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confidence: 99%

Coastal Aquaculture Mapping from Very High Spatial Resolution Imagery by Combining Object-Based Neighbor Features

Deng

et al. 2019

Sustainability

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“…An investigation on RCA distributions and quantities is necessary for aquaculture planning. However, RCAs are generally widely and sparsely distributed, thereby resulting in expensive, difficult, and imprecise field survey and measurement (Liu et al, 2013;Fan et al, 2015). By contrast, RCA extraction using HSR images has good efficiency and precision, and is the necessary choice for a large-scale survey of sea use.…”

Section: Introductionmentioning

confidence: 97%

“…Existing aquaculture water extraction techniques can be grouped into visual interpretation, texture analysis, and spectrum-dominated image analyses, among which spectrumbased classification methods are common (Fan et al, 2015;Lu et al, 2015). However, because of the complexity of the sea environment, RCAs may have spectral confusion with surrounding waters on HSR images.…”

Section: Introductionmentioning

confidence: 98%

Raft cultivation area extraction from high resolution remote sensing imagery by fusing multi-scale region-line primitive association features

Wang

Cui

Wang

et al. 2017

ISPRS Journal of Photogrammetry and Remote Sensing

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“…Wang et al applied the region-line primitive association framework in raft-culture extraction [2]. For SAR images, Fan et al proposed joint sparse representation classification method based on the wavelet decomposition and gray-level co-occurrence matrix [3]. Geng et al combined the sparse representation classifier and the collaborative representation classifier with residual-based fusion strategy [4].…”

Section: Introductionmentioning

confidence: 99%

Automatic Raft Labeling for Remote Sensing Images via Dual-Scale Homogeneous Convolutional Neural Network

Shi

Zou

et al. 2018

Remote Sensing

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Raft-culture is a way of utilizing water for farming aquatic product. Automatic raft-culture monitoring by remote sensing technique is an important way to control the crop's growth and implement effective management. This paper presents an automatic pixel-wise raft labeling method based on fully convolutional network (FCN). As rafts are always tiny and neatly arranged in images, traditional FCN method fails to extract the clear boundary and other detailed information. Therefore, a homogeneous convolutional neural network (HCN) is designed, which only consists of convolutions and activations to retain all details. We further design a dual-scale structure (DS-HCN) to integrate higher-level contextual information for accomplishing sea-land segmentation and raft labeling at the same time in a uniform framework. A dataset with Gaofen-1 satellite images was collected to verify the effectiveness of our method. DS-HCN shows a satisfactory performance with a better interpretability and a more accurate labeling result.

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Floating raft aquaculture information automatic extraction based on high resolution SAR images

Cited by 32 publications

References 7 publications

Coastal Aquaculture Mapping from Very High Spatial Resolution Imagery by Combining Object-Based Neighbor Features

Coastal Aquaculture Mapping from Very High Spatial Resolution Imagery by Combining Object-Based Neighbor Features

Raft cultivation area extraction from high resolution remote sensing imagery by fusing multi-scale region-line primitive association features

Automatic Raft Labeling for Remote Sensing Images via Dual-Scale Homogeneous Convolutional Neural Network

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