Two Dimensional Histogram based on Relative Entropy Thresholding for Crop Segmentation Using UAV Images

Priyanka, G and Rajalakshmi, P and Kholova, J (2023) Two Dimensional Histogram based on Relative Entropy Thresholding for Crop Segmentation Using UAV Images. In: IEEE International Symposium on Geoscience and Remote Sensing (IGARSS), 16-21 July 2023, Pasadena, CA, USA.

Full text not available from this repository. (Request a copy)

Abstract

Recently, Unmanned aerial vehicle (UAV) based remote sensing has become a promising way in precision agriculture. Crop or plant segmentation from UAV images plays a vital role in monitoring crop growth. However, the extraction of crops under various illumination conditions is onerous. Numerous methods on segmentation were presented in the literature, out of which threshold-based methods are simple and easy to implement. Previous methods used for crop segmentation utilized complete information of pixels in an image resulting in improper segmentation. The use of local information about pixels can give accurate segmentation. In this work, we constructed a two-dimensional histogram utilizing the gray level of pixels and relative entropy of its neighboring pixels of an contrast enhanced image. The optimal threshold was obtained by minimizing relative entropy criteria. The crops were extracted using logical AND operator on segmented image and a * channel of CIELAB color space. The proposed method was evaluated on Sorghum and Pearl Millet datasets. The misclassification error, Dice coefficient, Jaccard Index were used to compare the performance of the proposed method, Otsu, and Kapur method. The performance analysis shows that the proposed approach achieved more accurate segmentation than other threshold-based methods.

Item Type: Conference or Workshop Item (Paper)
Divisions: Global Research Program - Accelerated Crop Improvement
CRP: UNSPECIFIED
Uncontrolled Keywords: Image segmentation, Histograms, Crops, Lighting, Autonomous aerial vehicles, Entropy, Performance analysis, Computer vision, Phenotyping, Point cloud, Smart agriculture
Subjects: Others > Agriculture
Others > Crop Physiology
Depositing User: Mr Nagaraju T
Date Deposited: 28 Feb 2024 11:08
Last Modified: 28 Feb 2024 11:12
URI: http://oar.icrisat.org/id/eprint/12519
Acknowledgement: UNSPECIFIED
Links:
View Statistics

Actions (login required)

View Item View Item