Kernel low-rank representation with elastic net for China coastal wetland land cover classification using GF-5 hyperspectral imagery

Su, Hongjun; Yao, Wei; Wu, Zhaoyue; Pan, Zheng; Du, Qian

doi:10.1016/j.isprsjprs.2020.11.018

Cited by 66 publications

(27 citation statements)

References 53 publications

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“…Wetlands are regions that are shallow open waters (lakes, ponds, rivers, and coastal fringes) and any land that is regularly or intermittently covered or saturated by water (marshes, bogs, swamps, and flood plains) [1][2][3][4]. Wetlands play a significant role in ecological systems, which can adjust the regional climate, supply an abundant water resource, regulate hydrographic and carbon cycles, filter contaminants and sediment to improve the water quality, and provide habitats for wildlife [5][6][7]. Moreover, wetlands are viewed as worthy places to carry out anthropogenic activities, such as agricultural irrigation, hydropower, and industrial production [7][8][9].…”

Section: Introductionmentioning

confidence: 99%

Object-Based Multigrained Cascade Forest Method for Wetland Classification Using Sentinel-2 and Radarsat-2 Imagery

Liu

Jiang

et al. 2022

Water

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The development of advanced and efficient methods for mapping and monitoring wetland regions is essential for wetland resources conservation, management, and sustainable development. Although remote sensing technology has been widely used for detecting wetlands information, it remains a challenge for wetlands classification due to the extremely complex spatial patterns and fuzzy boundaries. This study aims to implement a comprehensive and effective classification scheme for wetland land covers. To achieve this goal, a novel object-based multigrained cascade forest (OGCF) method with multisensor data (including Sentinel-2 and Radarsat-2 remote sensing imagery) was proposed to classify the wetlands and their adjacent land cover classes in the wetland National Natural Reserve. Moreover, a hybrid selection method (ReliefF-RF) was proposed to optimize the feature set in which the spectral and polarimetric decomposition features are contained. We obtained six spectral features from visible and shortwave infrared bands and 10 polarimetric decomposition features from the H/A/Alpha, Pauli, and Krogager decomposition methods. The experimental results showed that the OGCF method with multisource features for land cover classification in wetland regions achieved the overall accuracy and kappa coefficient of 88.20% and 0.86, respectively, which outperformed the support vector machine (SVM), extreme gradient boosting (XGBoost), random forest (RF), and deep neural network (DNN). The accuracy of the wetland classes ranged from 75.00% to 97.53%. The proposed OGCF method exhibits a good application potential for wetland land cover classification. The classification scheme in this study will make a positive contribution to wetland inventory and monitoring and be able to provide technical support for protecting and developing natural resources.

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Section: Introductionmentioning

confidence: 99%

Object-Based Multigrained Cascade Forest Method for Wetland Classification Using Sentinel-2 and Radarsat-2 Imagery

Liu

Jiang

et al. 2022

Water

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“…Remote sensing parsing aims to execute meticulous image parsing to assist environmental monitoring [1,2], urban planning [3,4], agricultural and forestry change detection [5][6][7]. As a fundamental task in computer vision, semantic segmentation is proposed to assign an accurate label to each pixel in an image, which embraces the core of remote sensing parsing.…”

Section: Introductionmentioning

confidence: 99%

Controllable Fused Semantic Segmentation with Adaptive Edge Loss for Remote Sensing Parsing

Sun¹,

Xia

Dai³

2022

Remote Sensing

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High-resolution remote sensing images have been put into the application in remote sensing parsing. General remote sensing parsing methods based on semantic segmentation still have limitations, which include frequent neglect of tiny objects, high complexity in image understanding and sample imbalance. Therefore, a controllable fusion module (CFM) is proposed to alleviate the problem of implicit understanding of complicated categories. Moreover, an adaptive edge loss function (AEL) was proposed to alleviate the problem of the recognition of tiny objects and sample imbalance. Our proposed method combining CFM and AEL optimizes edge features and body features in a coupled mode. The verification on Potsdam and Vaihingen datasets shows that our method can significantly improve the parsing effect of satellite images in terms of mIoU and MPA.

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“…Due to the complex ecological conditions of wetlands and the spatial and temporal limitations of field investigations, remote sensing technology has become an important means of wetland mapping and monitoring. Despite the success of optical satellite data in applications such as wetland detection and water level monitoring [3][4][5], optical images are less useful in coastal areas due to cloud cover [6]. Synthetic Aperture Radar (SAR), which provides valuable geophysical parameters over intertidal zones in all-weather and daylight-independent conditions [7][8][9], has emerged as a promising tool for wetland monitoring.…”

Section: Introductionmentioning

confidence: 99%

Coastal Wetland Classification with GF-3 Polarimetric SAR Imagery by Using Object-Oriented Random Forest Algorithm

Zhang

Chen

et al. 2021

Sensors

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When the use of optical images is not practical due to cloud cover, Synthetic Aperture Radar (SAR) imagery is a preferred alternative for monitoring coastal wetlands because it is unaffected by weather conditions. Polarimetric SAR (PolSAR) enables the detection of different backscattering mechanisms and thus has potential applications in land cover classification. Gaofen-3 (GF-3) is the first Chinese civilian satellite with multi-polarized C-band SAR imaging capability. Coastal wetland classification with GF-3 polarimetric SAR imagery has attracted increased attention in recent years, but it remains challenging. The aim of this study was to classify land cover in coastal wetlands using an object-oriented random forest algorithm on the basis of GF-3 polarimetric SAR imagery. First, a set of 16 commonly used SAR features was extracted. Second, the importance of each SAR feature was calculated, and the optimal polarimetric features were selected for wetland classification by combining random forest (RF) with sequential backward selection (SBS). Finally, the proposed algorithm was utilized to classify different land cover types in the Yancheng Coastal Wetlands. The results show that the most important parameters for wetland classification in this study were Shannon entropy, Span and orientation randomness, combined with features derived from Yamaguchi decomposition, namely, volume scattering, double scattering, surface scattering and helix scattering. When the object-oriented RF classification approach was used with the optimal feature combination, different land cover types in the study area were classified, with an overall accuracy of up to 92%.

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Kernel low-rank representation with elastic net for China coastal wetland land cover classification using GF-5 hyperspectral imagery

Cited by 66 publications

References 53 publications

Object-Based Multigrained Cascade Forest Method for Wetland Classification Using Sentinel-2 and Radarsat-2 Imagery

Object-Based Multigrained Cascade Forest Method for Wetland Classification Using Sentinel-2 and Radarsat-2 Imagery

Controllable Fused Semantic Segmentation with Adaptive Edge Loss for Remote Sensing Parsing

Coastal Wetland Classification with GF-3 Polarimetric SAR Imagery by Using Object-Oriented Random Forest Algorithm

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