ePrints@IIScePrints@IISc Home | About | Browse | Latest Additions | Advanced Search | Contact | Help

Tree Crown Detection, Delineation and Counting in UAV Remote Sensed Images: A Neural Network Based Spectral-Spatial Method

Kestur, Ramesh and Angural, Akanksha and Bashir, Bazila and Omkar, S N and Anand, Gautham and Meenavathi, M B (2018) Tree Crown Detection, Delineation and Counting in UAV Remote Sensed Images: A Neural Network Based Spectral-Spatial Method. In: JOURNAL OF THE INDIAN SOCIETY OF REMOTE SENSING, 46 (6). pp. 991-1004.

[img] PDF
Jou_Ind_Soc_Rem_Sen_46-6_991_2018.pdf - Published Version
Restricted to Registered users only

Download (2MB) | Request a copy
Official URL: https://dx.doi.org/10.1007/s12524-018-0756-4


UAVs are fast emerging as a remote sensing platform to complement satellite based remote sensing. Agriculture and ecology is one of the important applications of UAV remote sensing, also known as low altitude remote sensing (LARS). This work demonstrates the use and potential of LARS in agriculture, particularly small holder open field agriculture. Two UAVs are used for remote sensing. The first UAV is a fixed wing aircraft with a high spatial resolution visible spectrum also known as RGB camera as a payload. The second UAV is a quadrotor UAV with an RGB camera interfaced to an on-board single board computer as the payload. LARS was carried out to acquire aerial high spatial resolution RGB images of different farms. Spectral-spatial classification of high spatial resolution RGB images for detection, delineation and counting of tree crowns in the image is presented. Supervised classification is carried out using extreme learning machine (ELM), a single hidden layer feed forward network neural network classifier. ELM was modelled for RGB values as input feature vectors and binary (tree and non-tree pixels) output class. Due to similarities in spectral intensities, some of the non-tree pixels were classified as tree pixels and in order to remove them, spatial classification was performed on the image. Spatial classification was carried out using thresholded geometrical property filtering techniques. Threshold values chosen for carrying out spatial classification were analysed to obtain optimal values. Finally in the delineation and counting, the connected tree crowns were segmented using Watershed algorithm performed on the image after marking individual tree crowns using Distance Transform method. Five representative UAV images captured at different altitudes with different crowns of banana plant, mango trees and coconut trees were used to demonstrate the performance of the proposed method. The performance was compared with the traditional KMeans spectral-spatial method of clustering. Results and comparison of performance parameters of KMeans spectral-spatial and ELM spectral-spatial classification methods are presented. Results indicate that ELM performed better than KMeans.

Item Type: Journal Article
Publisher: SPRINGER, 233 SPRING ST, NEW YORK, NY 10013 USA
Additional Information: Copyright of this article belong to SPRINGER, 233 SPRING ST, NEW YORK, NY 10013 USA
Department/Centre: Division of Mechanical Sciences > Aerospace Engineering(Formerly Aeronautical Engineering)
Date Deposited: 02 Aug 2018 15:47
Last Modified: 02 Aug 2018 15:47
URI: http://eprints.iisc.ac.in/id/eprint/60348

Actions (login required)

View Item View Item