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An Accurate Vegetation and Non-Vegetation Differentiation Approach Based on Land Cover Classification

Journal Article · · Remote Sensing
DOI:https://doi.org/10.3390/rs12233880· OSTI ID:1853408
 [1];  [2];  [2];  [3];  [4];  [5]
  1. Applied Research LLC, Rockville, MD (United States); Applied Research LLC, Rockville, MD (United States)
  2. Applied Research LLC, Rockville, MD (United States)
  3. Old Dominion Univ., Norfolk, VA (United States)
  4. Complutense Univ. of Madrid (Spain)
  5. Univ. of Extremadura, Caceres (Spain)
+ Show Author Affiliations
Accurate vegetation detection is important for many applications, such as crop yield estimation, land cover land use monitoring, urban growth monitoring, drought monitoring, etc. Popular conventional approaches to vegetation detection incorporate the normalized difference vegetation index (NDVI), which uses the red and near infrared (NIR) bands, and enhanced vegetation index (EVI), which uses red, NIR, and the blue bands. Although NDVI and EVI are efficient, their accuracies still have room for further improvement. In this paper, we propose a new approach to vegetation detection based on land cover classification. That is, we first perform an accurate classification of 15 or more land cover types. The land covers such as grass, shrub, and trees are then grouped into vegetation and other land cover types such as roads, buildings, etc. are grouped into non-vegetation. Similar to NDVI and EVI, only RGB and NIR bands are needed in our proposed approach. If Laser imaging, Detection, and Ranging (LiDAR) data are available, our approach can also incorporate LiDAR in the detection process. Results using a well-known dataset demonstrated that the proposed approach is feasible and achieves more accurate vegetation detection than both NDVI and EVI. In particular, a Support Vector Machine (SVM) approach performed 6% better than NDVI and 50% better than EVI in terms of overall accuracy (OA).
Research Organization:
Applied Research LLC, Rockville, MD (United States)
Sponsoring Organization:
USDOE Office of Science (SC)
Grant/Contract Number:
SC0019936
OSTI ID:
1853408
Journal Information:
Remote Sensing, Journal Name: Remote Sensing Journal Issue: 23 Vol. 12; ISSN 2072-4292
Publisher:
MDPICopyright Statement
Country of Publication:
United States
Language:
English

References (21)

The Impact of Land Use/Land Cover Changes on Land Degradation Dynamics: A Mediterranean Case Study journal March 2012
Modelling the phenological niche of large fires with remotely sensed NDVI profiles journal March 2012
A hybrid MLP-CNN classifier for very fine resolution remotely sensed image classification journal June 2018
Deep learning classifiers for hyperspectral imaging: A review journal December 2019
Extended profiles with morphological attribute filters for the analysis of hyperspectral data journal December 2010
Stacked Autoencoder-based deep learning for remote-sensing image classification: a case study of African land-cover mapping journal October 2016
Satellite remote sensing, biodiversity research and conservation of the future journal May 2014
Hyperspectral Unmixing on GPUs and Multi-Core Processors: A Comparison journal June 2013
Fusion of Hyperspectral and LiDAR Remote Sensing Data Using Multiple Feature Learning journal June 2015
Spectral–Spatial Classification of Multispectral Images Using Kernel Feature Space Representation journal January 2014
A Joint Sparsity Approach to Soil Detection Using Expanded Bands of WV-2 Images journal December 2019
Deep Learning in Remote Sensing: A Comprehensive Review and List of Resources journal December 2017
A feedback based modification of the NDVI to minimize canopy background and atmospheric noise journal March 1995
Morphological Attribute Profiles for the Analysis of Very High Resolution Images journal October 2010
A Novel Cluster Kernel RX Algorithm for Anomaly and Change Detection Using Hyperspectral Images journal November 2016
Biophysical Properties as Determinants for Soil Organic Carbon and Total Nitrogen in Grassland Salinization journal January 2013
Performance of Change Detection Algorithms Using Heterogeneous Images and Extended Multi-attribute Profiles (EMAPs) journal October 2019
Tree, Shrub, and Grass Classification Using Only RGB Images journal April 2020
Improving Land Cover Classification Using Extended Multi-Attribute Profiles (EMAP) Enhanced Color, Near Infrared, and LiDAR Data journal April 2020
Deep Learning for Land Cover Classification Using Only a Few Bands journal June 2020
Sensitivity of the Enhanced Vegetation Index (EVI) and Normalized Difference Vegetation Index (NDVI) to Topographic Effects: A Case Study in High-density Cypress Forest journal November 2007

Cited By (1)

Influence of Spatial Resolution for Vegetation Indices’ Extraction Using Visible Bands from Unmanned Aerial Vehicles’ Orthomosaics Datasets journal August 2021

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Related Subjects

54 ENVIRONMENTAL SCIENCES
97 MATHEMATICS AND COMPUTING
EMAP
LiDAR
NDVI
hyperspectral
land cover classification
synthetic bands