Authors:
Sara Sharifzadeh
1
;
Jagati Tata
1
and
Bo Tan
2
Affiliations:
1
Faculty of Engineering, Environment and Computing, Coventry University, Gulson Rd, Coventry CV1 2JH and U.K.
;
2
Faculty of Information Technology and Communication Sciences, Tampere University, Tampere and Finland
Keyword(s):
Classification, Supervised Feature Extraction, Convolutional Neural Nets (CNNs), Satellite Image, Digital Agriculture.
Related
Ontology
Subjects/Areas/Topics:
Artificial Intelligence
;
Biomedical Engineering
;
Biomedical Signal Processing
;
Business Analytics
;
Computational Intelligence
;
Data Analytics
;
Data Engineering
;
Data Management and Quality
;
Data Manipulation
;
Data Modeling and Visualization
;
Health Engineering and Technology Applications
;
Human-Computer Interaction
;
Knowledge Discovery and Information Retrieval
;
Knowledge-Based Systems
;
Methodologies and Methods
;
Neural Networks
;
Neurocomputing
;
Neurotechnology, Electronics and Informatics
;
Pattern Recognition
;
Physiological Computing Systems
;
Predictive Modeling
;
Sensor Networks
;
Signal Processing
;
Soft Computing
;
Support Vector Machines and Applications
;
Symbolic Systems
;
Theory and Methods
Abstract:
Farm detection using low resolution satellite images is an important topic in digital agriculture. However, it has not received enough attention compared to high-resolution images. Although high resolution images are more efficient for detection of land cover components, the analysis of low-resolution images are yet important due to the low-resolution repositories of the past satellite images used for timeseries analysis, free availability and economic concerns. The current paper addresses the problem of farm detection using low resolution satellite images. In digital agriculture, farm detection has significant role for key applications such as crop yield monitoring. Two main categories of object detection strategies are studied and compared in this paper; First, a two-step semi-supervised methodology is developed using traditional manual feature extraction and modelling techniques; the developed methodology uses the Normalized Difference Moisture Index (NDMI), Grey Level Co-occurren
ce Matrix (GLCM), 2-D Discrete Cosine Transform (DCT) and morphological features and Support Vector Machine (SVM) for classifier modelling. In the second strategy, high-level features learnt from the massive filter banks of deep Convolutional Neural Networks (CNNs) are utilised. Transfer learning strategies are employed for pretrained Visual Geometry Group Network (VGG-16) networks. Results show the superiority of the high-level features for classification of farm regions.
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