Object-Oriented LULC Classification in Google Earth Engine Combining SNIC, GLCM, and Machine Learning Algorithms

Google Earth Engine (GEE) is a versatile cloud platform in which pixel-based (PB) and object-oriented (OO) Land Use–Land Cover (LULC) classification approaches can be implemented, thanks to the availability of the many state-of-art functions comprising various Machine Learning (ML) algorithms. OO ap...

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Vydáno v:Remote sensing (Basel, Switzerland) Ročník 12; číslo 22; s. 3776
Hlavní autoři: Tassi, Andrea, Vizzari, Marco
Médium: Journal Article
Jazyk:angličtina
Vydáno: Basel MDPI AG 17.11.2020
Témata:
Accuracy
Algorithms
area
Classification
Cloud computing
Clustering
Computer applications
crops
data collection
decision support systems
environment
Google Earth Engine (GEE)
information
Internet
Italy
Iterative methods
Lakes
Land cover
Land use
land use and land cover maps
land use land cover
Landsat
Landsat 8
Landsat satellites
Learning algorithms
Machine learning
Mathematical analysis
Object oriented programming
Parameters
PlanetScope
principal component analysis
Principal components analysis
reliability
Remote sensing
Satellites
Segmentation
Sentinel 2
SNIC
Software
Statistical analysis
Statistics
Support vector machines
surfaces
testing
Tuning
Italy
ISSN:2072-4292, 2072-4292
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Popis
Shrnutí:Google Earth Engine (GEE) is a versatile cloud platform in which pixel-based (PB) and object-oriented (OO) Land Use–Land Cover (LULC) classification approaches can be implemented, thanks to the availability of the many state-of-art functions comprising various Machine Learning (ML) algorithms. OO approaches, including both object segmentation and object textural analysis, are still not common in the GEE environment, probably due to the difficulties existing in concatenating the proper functions, and in tuning the various parameters to overcome the GEE computational limits. In this context, this work is aimed at developing and testing an OO classification approach combining the Simple Non-Iterative Clustering (SNIC) algorithm to identify spatial clusters, the Gray-Level Co-occurrence Matrix (GLCM) to calculate cluster textural indices, and two ML algorithms (Random Forest (RF) or Support Vector Machine (SVM)) to perform the final classification. A Principal Components Analysis (PCA) is applied to the main seven GLCM indices to synthesize in one band the textural information used for the OO classification. The proposed approach is implemented in a user-friendly, freely available GEE code useful to perform the OO classification, tuning various parameters (e.g., choose the input bands, select the classification algorithm, test various segmentation scales) and compare it with a PB approach. The accuracy of OO and PB classifications can be assessed both visually and through two confusion matrices that can be used to calculate the relevant statistics (producer’s, user’s, overall accuracy (OA)). The proposed methodology was broadly tested in a 154 km2 study area, located in the Lake Trasimeno area (central Italy), using Landsat 8 (L8), Sentinel 2 (S2), and PlanetScope (PS) data. The area was selected considering its complex LULC mosaic mainly composed of artificial surfaces, annual and permanent crops, small lakes, and wooded areas. In the study area, the various tests produced interesting results on the different datasets (OA: PB RF (L8 = 72.7%, S2 = 82%, PS = 74.2), PB SVM (L8 = 79.1%, S2 = 80.2%, PS = 74.8%), OO RF (L8 = 64%, S2 = 89.3%, PS = 77.9), OO SVM (L8 = 70.4, S2 = 86.9%, PS = 73.9)). The broad code application demonstrated very good reliability of the whole process, even though the OO classification process resulted, sometimes, too demanding on higher resolution data, considering the available computational GEE resources.
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ISSN:2072-4292
2072-4292
DOI:10.3390/rs12223776