Comparing several pixel-based classification methods for vegetation structural composition mapping using Sentinel 2A imagery in Salatiga area, Central Java

Hadi, Haeydar Anggara; Danoedoro, Projo

doi:10.1117/12.2621965

Cited by 3 publications

(2 citation statements)

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“…For applying remote sensing technology to urban vegetation research, a critical aspect is to extract ground truth vegetation information from images swiftly and effectively. Traditional methods can be categorized into three kinds: visual interpretation, pixel-based classification [27], and object-oriented classification [28][29][30]. Manual visual interpretation is usually considered the most accurate extraction method, but there are some disadvantages such as human error, low efficiency, and high cost.…”

Section: Introductionmentioning

confidence: 99%

“…Shen proposed a 3D Dabor-wavelet-based method to identify each pixel and finally achieved hyperspectral image classification [32]. Hadi found that maximum likelihood algorithms could achieve higher accuracy by using a combination of original bands and the Ratio Vegetation Index [27]. Although relative research demonstrates that pixel-based classification methods are more efficient than manual visual interpretation, they are limited by "the different objects with the same spectrum" and "the same object with different spectrum" problems.…”

Section: Introductionmentioning

confidence: 99%

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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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