Classification of land cover based on deep belief networks using polarimetric RADARSAT-2 data

Lv, Qi; Dou, Yong; Niu, Xiamu; Xu, Jiaqing; Li, Baoliang

doi:10.1109/igarss.2014.6947537

Cited by 10 publications

(4 citation statements)

References 14 publications

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“…Through the DBN model, successful relevant mapping features can be extracted automatically from the PolSAR information to improve the classification accuracy. Lv et al (2014) proposed an approach for land cover classification dependent on Deep Belief Network(DBN) for extensive land spread use and urban classification. By applying the DBN model, successful spatio-temporal mapping features can be consequently detected to improve the accuracy of the classification.…”

Section: Related Workmentioning

confidence: 99%

RETRACTED ARTICLE: A novel approach for scene classification from remote sensing images using deep learning methods

Yin-rong

Zhang

et al. 2020

European Journal of Remote Sensing

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Deep learning plays a major task in classification of unsupervised data, which utilises network enhanced learning. This technique proves to be powerful in remote sensing (RS) field for spatial data classification. In the existing environment, huge amounts of data from the earth observation satellites known as satellite images time series (SITS) are gathered, which can be utilised for observing the areas related to geography over through time. In this proposed model the time series model utilised is based on geography. There exists a challenge on how these types of information can be analysed in the field of remote sensing. Notable, techniques related to deep learning substantiated in dealing with remote sensing usually for classification of scene. In this paper, we propose an enhanced classification method involving Recurrent Neural Network (RNN) along with Random forest (RF) for land classification using satellite images, which are publicly available for various research purposes. We utilised spatial data gathered from the satellite images (i.e. time series). Our experimental classification is based on pixel and objectbased classification. The attained analysis illustrates that the proposed model outperforms the other present day remote sensing classification techniques by producing 87% target accuracy of classification scene from satellite images.

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Section: Related Workmentioning

confidence: 99%

RETRACTED ARTICLE: A novel approach for scene classification from remote sensing images using deep learning methods

Yin-rong

Zhang

et al. 2020

European Journal of Remote Sensing

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“…There is further existence of variation within a pixel [9]. Most studies on satellite datasets highlighted the performance of object-based classification approaches for different regions such as agriculture areas, urban areas, forests, and wetlands [47]. In the past various years, different studies have been carried out using different emerging classifiers in remote sensing-based agriculture applications [91].…”

Section: Discussionmentioning

confidence: 99%

Survey on Machine Learning and Deep Learning Techniques for Agriculture Land

2021

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Agriculture land is playing a vital role in developing the economy of Indian states and contributes ~ 15% of India's gross domestic product (GDP). Moreover, agriculture is a major source of livelihood by engaging two-third (~ 66%) of the nation's population in various activities such as food supply, the raw material to the industries, internal and external trade. Therefore, the continuous monitoring and mapping of agricultural land are crucial for the sustainable life and development of the country. Most of the agriculture monitoring solutions are based on field observations or conventional strategies which are time-consuming and costlier. However, remote sensing delivers a cost-effective solution of acquiring information regarding the healthy or unhealthy vegetation in agricultural land with the help of a diverse range of advanced geospatial techniques such as classification, change detection, and pan-sharpening. In the present paper, we have performed a systematic survey with respect to recent advancements made in the classification algorithm, especially for agricultural land. These emerging methods incorporated in classifiers are machine learning and deep learning to enhance and detect the various features of vegetation parameters. It is expected that such studies will provide effective guidance to the researchers in better understanding the features, limitations, and specific importance of emerging classifiers in the Agriculture domain.

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“…Land cover classification is a hot topic in the sphere of remote sensing due to various issues and challenges [14]- [19]. Furthermore, monitoring land cover (MoLC) types in mining areas is important for ecological, environmental, and social development [20].…”

Section: Introductionmentioning

confidence: 99%

Edge Enhanced Channel Attention-Based Graph Convolution Network for Scene Classification of Complex Landscapes

Wang

Zhou

et al. 2023

IEEE J. Sel. Top. Appl. Earth Observations Remote Sensing

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Monitoring the land covers in complex landscapes is of great significance for the sustainable development of mine geoenvironments. As most existing remote sensing scene datasets are composed of RGB images, there is a lack of multimodal datasets for complex landscapes with mining land covers (MLCs) at a finescale. In this study, a new dataset was created by the China University of Geosciences (CUG), Wuhan (named CUG-MLCs) using ZiYuan-3 imagery-based multispectral and topographic data. Moreover, the characteristics of multi-size objects, irregular or blurred edges, and spectral-spatial-topographic heterogeneity and variability limited the classification accuracy. Therefore, an edge enhanced channel attention-based graph convolution network (ECA-GCN) was proposed and tested. The proposed ECA-GCN includes three key modules: (1) Multiscale and shallow feature fusion, used to fuse the multiscale convolutional features and shallow features, which helps present the MLC features with various scales; (2) edge enhanced channel attention, used to further select effective channels after a spatial edge feature enhancement, which helps identify irregular or blurred MLCs; and (3) edge detection-based GCN, used for edge feature-based adjacency matrix and feature maps from (2) to construct GCN, which can obtain edge node relation and global contextual information. This framework improved the representation of complex landscape characteristics. The proposed ECA-GCN achieved an overall accuracy of 66.60% ± 1.39%, averaged accuracy of 36.25% ± 1.50%, and Kappa of 55.91% ± 2.05%, thus, outperforming other models. In general, the proposed dataset and model were positive for the fine classification of complex landscapes.

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Classification of land cover based on deep belief networks using polarimetric RADARSAT-2 data

Cited by 10 publications

References 14 publications

RETRACTED ARTICLE: A novel approach for scene classification from remote sensing images using deep learning methods

RETRACTED ARTICLE: A novel approach for scene classification from remote sensing images using deep learning methods

Survey on Machine Learning and Deep Learning Techniques for Agriculture Land

Edge Enhanced Channel Attention-Based Graph Convolution Network for Scene Classification of Complex Landscapes

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