Deep Extraction of Cropland Parcels from Very High-Resolution Remotely Sensed Imagery

Xia, Liegang; Luo, Jiancheng; Sun, Yingwei; Yang, Haiping

doi:10.1109/agro-geoinformatics.2018.8476002

Cited by 29 publications

(16 citation statements)

References 17 publications

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“…Optical remote sensing images provide rich information content to recognize objects in a fundamental and challenging way, and the task of recognition from aerial images is attaining significant attention [62]. OBIA is an important type of these methods and is used for accurate and timely recognition of weeds [63], vegetation mapping [64], extracting cropland parcels for precision agriculture and other fields [65], mapping small-scale agriculture [66], and mapping of marine life [67].…”

Section: Prior Artmentioning

confidence: 99%

Recognition of Ziziphus lotus through Aerial Imaging and Deep Transfer Learning Approach

Tufail

Ullah

Khan

et al. 2021

Mobile Information Systems

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There is a growing demand for the detection of endangered plant species through machine learning approaches. Ziziphus lotus is an endangered deciduous plant species in the buckthorn family (Rhamnaceae) native to Southern Europe. Traditional methods such as object-based image analysis have achieved good recognition rates. However, they are slow and require high human intervention. Transfer learning-based methods have several applications for data analysis in a variety of Internet of Things systems. In this work, we have analyzed the potential of convolutional neural networks to recognize and detect the Ziziphus lotus plant in remote sensing images. We fine-tuned Inception version 3, Xception, and Inception ResNet version 2 architectures for binary classification into plant species class and bare soil and vegetation class. The achieved results are promising and effectively demonstrate the better performance of deep learning algorithms over their counterparts.

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Section: Prior Artmentioning

confidence: 99%

Recognition of Ziziphus lotus through Aerial Imaging and Deep Transfer Learning Approach

Tufail

Ullah

Khan

et al. 2021

Mobile Information Systems

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“…As a result, it is potential to extract parcels boundaries through CNN-based methods. Nevertheless, parcels boundaries present different features in VHR images, which is the major obstacle to parcels extraction [18]. But the existing methods pay less attention to the fuzzy boundaries and thus making it difficult to extract the fuzzy boundaries of parcels.…”

Section: > Replace This Line With Your Manuscript Id Number (Double-c...mentioning

confidence: 99%

A Fuzzy-Boundary Enhanced Trident Network for Parcel Extraction in the Urban–Rural Area

Zhu

Chai

Song

et al. 2023

IEEE J. Sel. Top. Appl. Earth Observations Remote Sensing

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As the basic unit of farmland, parcel is crucial for remote sensing tasks, such as urban management. Previous studies of farmland parcels extraction are based on boundary detection and instance segmentation methods. However, these methods perform poorly in the parcels with complex shape and fuzzy boundary due to the insufficient feature extraction capability. Moreover, for the lack of multi-scale features extraction and fusion, they are difficult to extract different scale farmland parcels accurately. Based on these issues, we propose a Fuzzy-Boundary Enhanced Trident Network, named FBETNet, to enhance the feature of fuzzy boundary and generate multi-scale parcels. First, a semantic-guided multi-task strategy is introduced in order to enhance the feature of fuzzy boundary. Second, we design a multiscale trident module to further improve the performance of multiscale feature extraction. Finally, a adversarial data augmentation strategy is employed in the training phase to strengthen the robustness and stability of out proposed method. Experiments show that our proposed method improves significantly in both accuracy and visualization, especially for the parcels with fuzzy boundary and complex shape.

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“…As far as the authors know, there is little literature available dealing with the automatic/semi-automatic delineation of agricultural plots using CNNs. Xia et al [31] proposed a workflow for the extraction of deep edges of crop plots from very high spatial resolution images. The workflow combined RCF [32] and U-Net [33] models respectively to detect soft edges (rivers and roads) and to detect regions such as hard edges and types of farmland.…”

Section: Introductionmentioning

confidence: 99%

Deep Learning for Automatic Outlining Agricultural Parcels: Exploiting the Land Parcel Identification System

et al. 2019

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Accurate and up-to-date information on the spatial and geographical characteristics of agricultural areas is an indispensable value for the various activities related to agriculture and research. Most agricultural studies and policies are carried out at the field level, for which precise boundaries are required. Today, high-resolution remote sensing images provide useful spatial information for plot delineation; however, manual processing is time-consuming and prone to human error. The objective of this paper is to explore the potential of deep learning (DL) approach, in particular a convolutional neural network (CNN) model, for the automatic outlining of agricultural plot boundaries from orthophotos over large areas with a heterogeneous landscape. Since DL approaches require a large amount of labeled data to learn, we have exploited the open data from the Land Parcel Identification System (LPIS) from the Chartered Community of Navarre, Spain. The boundaries of the agricultural plots obtained from our methodology were compared with those obtained using a state-of-the-art methodology known as gPb-UCM (global probability of boundary followed by ultrametric contour map) through an error measurement called the boundary displacement error index (BDE). In BDE terms, the results obtained by our method outperform those obtained from the gPb-UCM method. In this regard, CNN models trained with LPIS data are a useful and powerful tool that would reduce intensive manual labor in outlining agricultural plots. INDEX TERMS Convolutional neural network, deep learning, edge extraction, land parcel identification system, parcels delineation.

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Deep Extraction of Cropland Parcels from Very High-Resolution Remotely Sensed Imagery

Cited by 29 publications

References 17 publications

Recognition of Ziziphus lotus through Aerial Imaging and Deep Transfer Learning Approach

Recognition of Ziziphus lotus through Aerial Imaging and Deep Transfer Learning Approach

A Fuzzy-Boundary Enhanced Trident Network for Parcel Extraction in the Urban–Rural Area

Deep Learning for Automatic Outlining Agricultural Parcels: Exploiting the Land Parcel Identification System

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