Vegetation coverage detection from very high resolution satellite imagery

Fan, Jianchao; Chen, Tao; Lu, Shijian

doi:10.1109/vcip.2015.7457846

Cited by 7 publications

(4 citation statements)

References 11 publications

(23 reference statements)

Supporting

Mentioning

Contrasting

Order By: Relevance

“…ML algorithms were also implemented to detect vegetation cover in satellite images. [24] introduced a method to detect vegetation coverage by implementing both supervised and unsupervised ML algorithm by dividing the detection step into two parts: the supervised patch level classification step using Support Vector Regression (SVR) and unsupervised pixel-level classification. The method performs 98% accuracy.…”

Section: Machine Learning Methodsmentioning

confidence: 99%

A Review of Vegetation Encroachment Detection in Power Transmission Lines using Optical Sensing Satellite Imagery

Haroun¹

2020

IJATCSE

View full text Add to dashboard Cite

Vegetation encroachment in power transmission lines can cause outages, which may result in severe impact on economic of power utilities companies as well as the consumer. Vegetation detection and monitoring along the power line corridor right-of-way (ROW) are implemented to protect power transmission lines from vegetation penetration. There were various methods used to monitor the vegetation penetration, however, most of them were too expensive and time consuming. Satellite images can play a major role in vegetation monitoring, because it can cover high spatial area with relatively low cost. In this paper, the current techniques used to detect the vegetation encroachment using satellite images are reviewed and categorized into four sectors; Vegetation Index based method, object-based detection method, stereo matching based and other current techniques. However, the current methods depend usually on setting manually serval threshold values and parameters which make the detection process very static. Machine Learning (ML) and deep learning (DL) algorithms can provide a very high accuracy with flexibility in the detection process. Hence, in addition to review the current technique of vegetation penetration monitoring in power transmission, the potential of using Machine Learning based algorithms are also included.

show abstract

Section: Machine Learning Methodsmentioning

confidence: 99%

A Review of Vegetation Encroachment Detection in Power Transmission Lines using Optical Sensing Satellite Imagery

Haroun¹

2020

IJATCSE

View full text Add to dashboard Cite

show abstract

“…16 Furthermore, vegetation area delimited with support vector regression in the first level of supervised patch level scoring and supervised pixel-level vegetation from multispectral images. 17 Apart from multispectral images, radar image as an input image, the estimation of vegetation and water content done through RVI. 18 In vegetation delineation, resolution of the images improves with dual-tree complex wavelet transform.…”

Section: Related Workmentioning

confidence: 99%

“…In addition, with vegetation delineation, the height of vegetation area obtained with buildings through texture analysis 16 . Furthermore, vegetation area delimited with support vector regression in the first level of supervised patch level scoring and supervised pixel‐level vegetation from multispectral images 17 . Apart from multispectral images, radar image as an input image, the estimation of vegetation and water content done through RVI 18 .…”

Section: Related Workmentioning

confidence: 99%

Mixed pixel removal in north Tamil Nadu region for accurate area measurement

Mani¹,

Nagalingam²,

Aruldoss³

et al. 2021

Computational Intelligence

View full text Add to dashboard Cite

In remote sensing, vegetation and water areas delineation from satellite image plays a vital role for urban and rural planning. Delineation of vegetation and water area is a challenging task due to mixed pixels and geometric distortion over the boundary region. Geometric distortion arises due to change in velocity and speed of satellite during image acquisition, and mixed pixels arises due to different surfaces in a particular area. Traditional methods apply classifier algorithms such as support vector machine, neural network, and fuzzy for vegetation and water area delineation. The traditional methods require more training dataset and consume more interpretation time for delineation. In this article, we propose transverse dyadic wavelet transform (TDyWT) to delineate vegetation and water area from Landsat 8 images. The TDyWT method enhances the boundary and curvature area of satellite image for accurate delineation. From the experimental results, the proposed TDyWT approach delineates the area of subclass for vegetation and water areas with 95% of accuracy with respect to the ground truth.

show abstract

“…Pictures with different sizes, corner and magnitude are detected and expelled while testing for coordinating. Pictures acquired for satellite picture combination may be taken from different sensors, angles, clips and depths[18,19]. So it is necessary to alter diverse arrangements of data into one coordinate framework.…”

mentioning

confidence: 99%

A Segmentation Method of Fused Multispectral and Panchromatic Images using Quick Shift Algorithm and Brovey Transform

Vanmathi¹

2019

IJEAT

View full text Add to dashboard Cite

Satellite Image Processing has always been a good domain to carry out researches. This is mainly due to the probability of the vast nature of the domain. There are many Image fusions and segmentation methods are available in the area of Satellite Image Processing. The presented research work proposes the image fusion method to inter twined the high determination panchromatic (PAN) picture and the low-determination multispectral picture to gain the multispectral image of high determination. For picture fusion the Brovey transform is used. The derived inter twined picture is granted as an input to the further segmentation process. Segmentation of the image obtained after the fusion is done and the recognition of land, marine, plantation and residential ranges are shown in the picture of the particular location gained from the satellite. For the segmentation of the fused satellite picture the Quick Shift Segmentation clustering technique is used. Utilizing Quick Shift Segmentation on the combined image the various sections can easily be identified as compared to the unprocessed picture gain from the satellite. Scientists utilize Brovey transform to intertwined RGB colour pictures to high determination panchromatic (PAN) pictures. In the proposed work, the main focus is to degrade spectral irregularity and enhancement of spatial data on merging the pictures of satellite Limitation of the current technique of the combination of images for example restricted directionality, shift invariant, absence of the data are amended by using Brovey transformation. The outcomes of the current system are generally unsatisfied. To overcome this Quick Shift Segmentation technique is used with the Brovey transform in the proposed system. The exactness of segmented image is checked with respect to reference information. The fused image segmentation outcomes have been ascertained and it shows that Brovey transform with interaction of Quick Shift Segmentation technique provides more successive outcomes.

show abstract

Vegetation coverage detection from very high resolution satellite imagery

Cited by 7 publications

References 11 publications

A Review of Vegetation Encroachment Detection in Power Transmission Lines using Optical Sensing Satellite Imagery

A Review of Vegetation Encroachment Detection in Power Transmission Lines using Optical Sensing Satellite Imagery

Mixed pixel removal in north Tamil Nadu region for accurate area measurement

A Segmentation Method of Fused Multispectral and Panchromatic Images using Quick Shift Algorithm and Brovey Transform

Contact Info

Product

Resources

About