Crop species classification: A phenology based approach

Luciani, Roberto; Laneve, Giovanni; Jahjah, Munzer; Collins, Mike

doi:10.1109/igarss.2017.8127974

Cited by 3 publications

(1 citation statement)

References 7 publications

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“…In Luciani et al (2017) , an analysis on crop classification is presented in which Landsat-8 OLI is used to capture the multispectral imagery at a coarse spatial resolution of 30 m. The acquired images are resampled to 15 m spatial resolution using the pan-sharpening technique. The phenological profile of crops is extracted by processing NDVI of time series spectral imagery data.…”

Section: Related Workmentioning

confidence: 99%

Gray level co-occurrence matrix (GLCM) texture based crop classification using low altitude remote sensing platforms

Iqbal

Mumtaz

Shafi

et al. 2021

PeerJ Computer Science

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Crop classification in early phenological stages has been a difficult task due to spectrum similarity of different crops. For this purpose, low altitude platforms such as drones have great potential to provide high resolution optical imagery where Machine Learning (ML) applied to classify different types of crops. In this research work, crop classification is performed at different phenological stages using optical images which are obtained from drone. For this purpose, gray level co-occurrence matrix (GLCM) based features are extracted from underlying gray scale images collected by the drone. To classify the different types of crops, different ML algorithms including Random Forest (RF), Naive Bayes (NB), Neural Network (NN) and Support Vector Machine (SVM) are applied. The results showed that the ML algorithms performed much better on GLCM features as compared to gray scale images with a margin of 13.65% in overall accuracy.

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Section: Related Workmentioning

confidence: 99%

Gray level co-occurrence matrix (GLCM) texture based crop classification using low altitude remote sensing platforms

Iqbal

Mumtaz

Shafi

et al. 2021

PeerJ Computer Science

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Crop Classification using Multi-spectral and Multitemporal Satellite Imagery with Machine Learning

Viskovic

Kosović

Mastelić

2019

2019 International Conference on Software, Telecommunications and Computer Networks (SoftCOM)

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Effectiveness of Common Preprocessing Methods of Time Series for Monitoring Crop Distribution in Kenya

Xiao-hui

Lei

et al. 2022

Agriculture

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Accurate crop identification and spatial distribution mapping are important for crop production estimation and famine early warning, especially for food-deficit African agricultural countries. By evaluating existing preprocessing methods for classification using satellite image time series (SITS) in Kenya, this study aimed to provide a low-cost method for cultivated land monitoring in sub-Saharan Africa that lacks financial support. SITS were composed of a set of MODIS Vegetation Indices (MOD13Q1) in 2018, and the classification method included the Support Vector Machine (SVM) and Random Forest (RF) classifier. Eight datasets obtained at three levels of preprocessing from MOD13Q1 were used in the classification: (1) raw SITS of vegetation indices (R-NDVI, R-EVI, and R-NDVI + R-EVI); (2) smoothed SITS of vegetation indices (S-NDVI); and (3) vegetation phenological data (P-NDVI, P-EVI, R-NDVI + P-NDVI, and P-NDVI-1). Both SVM and RF classification results showed that the “R-NDVI + R-EVI” dataset achieved the highest performance, while the three pure phenological datasets produced the lowest accuracy. Correlation analysis between variable importance and rainfall time series demonstrated that the vegetation index SITS during rainfall periods showed higher importance in RF classifiers, thus revealing the potential of saving computational costs. Considering the preprocessing cost of SITS and its negative impact on the classification accuracy, we recommend overlaying the original NDVI with the original EVI time series to map the crop distribution in Kenya.

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Crop species classification: A phenology based approach

Cited by 3 publications

References 7 publications

Gray level co-occurrence matrix (GLCM) texture based crop classification using low altitude remote sensing platforms

Gray level co-occurrence matrix (GLCM) texture based crop classification using low altitude remote sensing platforms

Crop Classification using Multi-spectral and Multitemporal Satellite Imagery with Machine Learning

Effectiveness of Common Preprocessing Methods of Time Series for Monitoring Crop Distribution in Kenya

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