Remote Sensing: An Automated Methodology for Olive Tree Detection and Counting in Satellite Images

Khan, Aftab; Khan, Umair; Waleed, Muhammad; Khan, Ashfaq; Kamal, Tariq; Marwat, Safdar Nawaz Khan; Maqsood, Muazzam; Aadil, Farhan

doi:10.1109/access.2018.2884199

Cited by 38 publications

(17 citation statements)

References 17 publications

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“…Another technique applied a thresholding strategy with different levels to segment and extract olive trees from the foreground [ 17 ]. They proposed a robust and an efficient model for accurately segmenting and detecting olive trees in a variety of environments.…”

Section: Related Workmentioning

confidence: 99%

A Large-Scale Dataset and Deep Learning Model for Detecting and Counting Olive Trees in Satellite Imagery

Abozeid

Rayan

Elhadad

et al. 2022

Computational Intelligence and Neuroscience

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Since the Pre-Roman era, olive trees have a significant economic and cultural value. In 2019, the Al-Jouf region, in the north of the Kingdom of Saudi Arabia, gained a global presence by entering the Guinness World Records, with the largest number of olive trees in the world. Olive tree detecting and counting from a given satellite image are a significant and difficult computer vision problem. Because olive farms are spread out over a large area, manually counting the trees is impossible. Moreover, accurate automatic detection and counting of olive trees in satellite images have many challenges such as scale variations, weather changes, perspective distortions, and orientation changes. Another problem is the lack of a standard database of olive trees available for deep learning applications. To address these problems, we first build a large-scale olive dataset dedicated to deep learning research and applications. The dataset consists of 230 RGB images collected over the territory of Al-Jouf, KSA. We then propose an efficient deep learning model (SwinTUnet) for detecting and counting olive trees from satellite imagery. The proposed SwinTUnet is a Unet-like network which consists of an encoder, a decoder, and skip connections. Swin Transformer block is the fundamental unit of SwinTUnet to learn local and global semantic information. The results of an experimental study on the proposed dataset show that the SwinTUnet model outperforms the related studies in terms of overall detection with a 0.94% estimation error.

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Section: Related Workmentioning

confidence: 99%

A Large-Scale Dataset and Deep Learning Model for Detecting and Counting Olive Trees in Satellite Imagery

Abozeid

Rayan

Elhadad

et al. 2022

Computational Intelligence and Neuroscience

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“…Segments falling within defined parameters of size and area were considered to be olive trees, resulting in an overall mean error of 13%. In 2018, Khan et al [15] proposed a computationally efficient method to detect olive trees over the territory of Spain. They employed basic image processing techniques such as unsharp masking and threshold-based segmentation to detect and count olive trees.…”

Section: To Presentmentioning

confidence: 99%

Automatic Detection System of Olive Trees Using Improved K-Means Algorithm

Waleed

Khan

et al. 2020

Remote Sensing

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Olive cultivation over the past few years has spread across Mediterranean countries with Spain being the world’s largest olive producer among them. Because olives are a major part of the economy for such countries keeping records of their tree count and crop yield is of high significance. Manual counting of trees over such large areas is humanly infeasible. To address this problem, we propose an automatic method for the detection and enumeration of olive trees. The algorithm is a multi-step classification system comprising pre-processing, image segmentation, feature extraction, and classification. RGB satellite images were acquired from the Spanish territory and pre-processed to suppress the additive noise. The region of interest was then segmented from the pre-processed images using K-Means segmentation, through which statistical features were extracted and classified. Promising results were achieved for all classifiers, namely Naive Bayesian, Support Vector Machines (SVMs), Random Forest and Multi-Layer Perceptrons (MLPs), at various division ratios of data samples. In a comparison of all the classification algorithms, Random Forest outperformed the rest by an overall accuracy of 97.5% at the division ratio of 70 to 30 for training to testing.

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“…Karantzalos et al used the basic image processing techniques to detect the blobs created using the Laplacian maxima over the grey-scale images acquired from Quickbird and IKONOS satellites [7]. Juan Moreno Garcia worked on the VHR imagery acquired from the Sigpac viewer covering the Spanish territory [5], [14]. Their work showed the application of fuzzy logic combined with k nearest neighbor technique resulting in the detection of almost all the trees with commission and omission rates of 0 and 1 respectively in the 6 test cases.…”

Section: A Comparative Analysis With Benchmark Schemementioning

confidence: 99%

“…With a simple implementation, the algorithm showed an estimation error of 13%. Khan et al in [14] proposed an image segmentation technique extracting olive trees from foreground using multilevel thresholding technique. In their work, they proposed an efficient and robust model to accurately segment out and detect olive trees from the diverse environments.…”

Section: A Comparative Analysis With Benchmark Schemementioning

confidence: 99%

An Automated Method for Detection and Enumeration of Olive Trees Through Remote Sensing

Waleed

Khan

et al. 2020

IEEE Access

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Country olive forests are one of the major contributors with economic aspects. Spain is the leading country in the world which produced around 45% of olive oil compared to the total production in the world. The value of 2.4 million hectares of land is dedicated to olive cultivation in the country, which made it a huge olive oil producer in the world. Olive crop widely spread over large extensive areas; manual counting of trees is humanly infeasible. To address this problem, we propose an automatic scheme for the detection and enumeration of olive trees. The proposed technique comprises of multi-step image processing techniques applied over a single band of imagery. The single red band once extracted from the color spectrum of acquired images, is then sharpened and edges are detected. The closed edges formed by the tree boundaries are transformed into white blobs using morphological reconstruction. Resulting circular blobs are then filtered out based on their shape and size. Blobs with circular geometry and in-range radius are considered as olive trees, which are then mapped with the existing ground information. Results have been generated over the diverse images capturing the ground truth information with an estimation error of 1.27%.

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Remote Sensing: An Automated Methodology for Olive Tree Detection and Counting in Satellite Images

Cited by 38 publications

References 17 publications

A Large-Scale Dataset and Deep Learning Model for Detecting and Counting Olive Trees in Satellite Imagery

A Large-Scale Dataset and Deep Learning Model for Detecting and Counting Olive Trees in Satellite Imagery

Automatic Detection System of Olive Trees Using Improved K-Means Algorithm

An Automated Method for Detection and Enumeration of Olive Trees Through Remote Sensing

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