Vegetation Land Use/Land Cover Extraction From High-Resolution Satellite Images Based on Adaptive Context Inference

Zhi-Ming, Zhan; Zhang, Xiaomeng; Liu, Yi; Sun, Xiao; Pang, Chao; Zhao, Chenbo

doi:10.1109/access.2020.2969812

Cited by 21 publications

(7 citation statements)

References 64 publications

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“…Automated land cover mapping is an active field of research with many machine learning approaches suggested, solving tasks such as vegetation extraction [9] or land cover change detection [10]. In creating a global land cover map, CORINE [11] uses several data fusion and pre-processing steps together with ancillary data sources to generate a training dataset.…”

Section: Related Workmentioning

confidence: 99%

Segmentation of Satellite Imagery using U-Net Models for Land Cover Classification

Ulmas,

Liiv

2020

Preprint

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The focus of this paper is using a convolutional machine learning model with a modified U-Net structure for creating land cover classification mapping based on satellite imagery. The aim of the research is to train and test convolutional models for automatic land cover mapping and to assess their usability in increasing land cover mapping accuracy and change detection. To solve these tasks, authors prepared a dataset and trained machine learning models for land cover classification and semantic segmentation from satellite images. The results were analysed on three different land classification levels. BigEarthNet satellite image archive was selected for the research as one of two main datasets. This novel and recent dataset was published in 2019 and includes Sentinel-2 satellite photos from 10 European countries made in 2017 and 2018. As a second dataset the authors composed an original set containing a Sentinel-2 image and a CORINE land cover map of Estonia. The developed classification model shows a high overall F 1 score of 0.749 on multiclass land cover classification with 43 possible image labels. The model also highlights noisy data in the BigEarthNet dataset, where images seem to have incorrect labels. The segmentation models offer a solution for generating automatic land cover mappings based on Sentinel-2 satellite images and show a high IoU score for land cover classes such as forests, inland waters and arable land. The models show a capability of increasing the accuracy of existing land classification maps and in land cover change detection.

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Section: Related Workmentioning

confidence: 99%

Segmentation of Satellite Imagery using U-Net Models for Land Cover Classification

Ulmas,

Liiv

2020

Preprint

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“…In recent years, the rapid advancements in space optical remote sensing technology have greatly promoted the Earth Observation System [15] and it has emerged as a potent and impactful tool for acquiring regional and global vegetation information [16][17][18][19]. Currently, a variety of optical, hyperspectral, and radar remote sensing data have been widely utilized for vegetation extraction, such as Gaofen and ZY series data [20][21][22] from China, Sentinel series data from the European Space Agency [23][24][25], and Landsat series data from the United States [26]. For applying remote sensing technology to urban vegetation research, a critical aspect is to extract ground truth vegetation information from images swiftly and effectively.…”

Section: Introductionmentioning

confidence: 99%

Urban Vegetation Extraction from High-Resolution Remote Sensing Imagery on SD-UNet and Vegetation Spectral Features

Lin,

Quan,

et al. 2023

Remote Sensing

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Urban vegetation plays a crucial role in the urban ecological system. Efficient and accurate extraction of urban vegetation information has been a pressing task. Although the development of deep learning brings great advantages for vegetation extraction, there are still problems, such as ultra-fine vegetation omissions, heavy computational burden, and unstable model performance. Therefore, a Separable Dense U-Net (SD-UNet) was proposed by introducing dense connections, separable convolutions, batch normalization layers, and Tanh activation function into U-Net. Furthermore, the Fake sample set (NIR-RG), NDVI sample set (NDVI-RG), and True sample set (RGB) were established to train SD-UNet. The obtained models were validated and applied to four scenes (high-density buildings area, cloud and misty conditions area, park, and suburb) and two administrative divisions. The experimental results show that the Fake sample set can effectively improve the model’s vegetation extraction accuracy. The SD-UNet achieves the highest accuracy compared to other methods (U-Net, SegNet, NDVI, RF) on the Fake sample set, whose ACC, IOU, and Recall reached 0.9581, 0.8977, and 0.9577, respectively. It can be concluded that the SD-UNet trained on the Fake sample set not only is beneficial for vegetation extraction but also has better generalization ability and transferability.

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“…In remote sensing object recognition, [4], [5], and [6] use DeepLab [7] [8]. Fully Convolutional Networks (FCNs) [9] are used in [10], [11], and [12].…”

Section: Introductionmentioning

confidence: 99%

SWRNet: A Deep Learning Approach for Small Surface Water Area Recognition Onboard Satellite

Bui,

Lee

2023

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

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This paper proposes a deep learning approach for small surface water recognition (SWRNet) using multispectral satellite imaging, which reduces the computational complexity by 18.66 times and increases the accuracy of surface water recognition by up to 14.1%. The proposed model uses near infrared (NIR) combined with RGB spectral imagery to increase the accuracy of surface water recognition. In addition, since surface water only accounts for a small percentage of the remote sensing dataset, thus creating an imbalance problem, a proposed loss function is introduced to combine region-based and distribution-based loss. This paper introduces an adaptive factor that automatically adjusts the weighting between distribution-and region-based loss functions. The proposed adaptive factor is determined based on the loss value of the previous training step. The mean intersection over union (mIoU) of surface water between predicted and ground truth regions is recorded as 0.80.

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Vegetation Land Use/Land Cover Extraction From High-Resolution Satellite Images Based on Adaptive Context Inference

Cited by 21 publications

References 64 publications

Segmentation of Satellite Imagery using U-Net Models for Land Cover Classification

Segmentation of Satellite Imagery using U-Net Models for Land Cover Classification

Urban Vegetation Extraction from High-Resolution Remote Sensing Imagery on SD-UNet and Vegetation Spectral Features

SWRNet: A Deep Learning Approach for Small Surface Water Area Recognition Onboard Satellite

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