Segment Anything Model Combined with Multi-Scale Segmentation for Extracting Complex Cultivated Land Parcels in High-Resolution Remote Sensing Images

Remote Sensing, Год журнала: 2024, Номер 16(18), С. 3489 - 3489

Опубликована: Сен. 20, 2024

Accurate cultivated land parcel data are an essential analytical unit for further agricultural monitoring, yield estimation, and precision agriculture management. However, the high degree of landscape fragmentation irregular shapes parcels, influenced by topography human activities, limit effectiveness extraction. The visual semantic segmentation model based on Segment Anything Model (SAM) provides opportunities extracting multi-form parcels from high-resolution images; however, performance SAM in requires exploration. To address difficulty obtaining extraction that closely matches true boundaries complex large-area this study used patches with boundary information obtained unsupervised as constraints, which were then incorporated into subsequent multi-scale segmentation. A combined method was proposed, it evaluated different scenarios. In plain areas, precision, recall, IoU improved 6.57%, 10.28%, 9.82%, respectively, compared to basic extraction, confirming proposed method. comparison point-prompt conditional segmentation, achieved considerable improvements parcels. This confirms that, under zero-shot conditions, demonstrates strong cross-region cross-data source transferability across large areas.

Язык: Английский

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Improving crop type mapping by integrating LSTM with temporal random masking and pixel-set spatial information

ISPRS Journal of Photogrammetry and Remote Sensing, Год журнала: 2024, Номер 218, С. 87 - 101

Опубликована: Окт. 19, 2024

Язык: Английский

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Crop Mapping Based on Temporal and Spatial Sample Migrations: A Case Study Over Three Counties in Heilongjiang Province, Northeast China

IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing, Год журнала: 2024, Номер 17, С. 14630 - 14639

Опубликована: Янв. 1, 2024

Crop mapping is crucial for agricultural management and yield prediction. Currently, remote sensing-based crop over a large region still challenging due to the requirement of sufficient in-season samples, which commonly costly time-consuming. To address this challenge, spatial temporal sample migration method was proposed evaluated in three typical counties Heilongjiang province, Northeast China. On one hand, ground samples collected from previous two years (2020 2021) Nenjiang County were temporally migrated target year (2022). other 2022 Fujin spatially adjacent Tongjiang crops same year. This enabled absence current samples. In method, Sentinel-2 data primarily used obtain curves reference addition, by balancing quantity quality an optimal rule designed using Dynamic Time Warping algorithm study area. Finally, distribution region. The results indicated that overall accuracy can reach 95.7% with Nenjiang, whereas approximately 75.6%. approach reveals significant potential without knowledge especially use historical migration.

Язык: Английский

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Irregular agricultural field delineation using a dual-branch architecture from high-resolution remote sensing images

IEEE Geoscience and Remote Sensing Letters, Год журнала: 2024, Номер 21, С. 1 - 5

Опубликована: Янв. 1, 2024

Язык: Английский

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Taking it further: Leveraging pseudo-labels for field delineation across label-scarce smallholder regions

International Journal of Applied Earth Observation and Geoinformation, Год журнала: 2024, Номер 134, С. 104149 - 104149

Опубликована: Сен. 13, 2024

Язык: Английский

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Estimating rice leaf area index at multiple growth stages with Sentinel-2 data: An evaluation of different retrieval algorithms

European Journal of Agronomy, Год журнала: 2024, Номер 161, С. 127362 - 127362

Опубликована: Сен. 16, 2024

Язык: Английский

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A cost-effective and robust mapping method for diverse crop types using weakly supervised semantic segmentation with sparse point samples

ISPRS Journal of Photogrammetry and Remote Sensing, Год журнала: 2024, Номер 218, С. 260 - 276

Опубликована: Сен. 20, 2024

Язык: Английский

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Temporal segmentation method for 30-meter long-term mapping of abandoned and reclaimed croplands in Inner Mongolia, China

International Journal of Applied Earth Observation and Geoinformation, Год журнала: 2025, Номер 136, С. 104399 - 104399

Опубликована: Фев. 1, 2025

Язык: Английский

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Recent Advances in Deep Learning-Based Spatiotemporal Fusion Methods for Remote Sensing Images

Sensors, Год журнала: 2025, Номер 25(4), С. 1093 - 1093

Опубликована: Фев. 12, 2025

Remote sensing images captured by satellites play a critical role in Earth observation (EO). With the advancement of satellite technology, number and variety remote have increased, which provide abundant data for precise environmental monitoring effective resource management. However, existing imagery often faces trade-off between spatial temporal resolutions. It is challenging single to simultaneously capture with high Consequently, spatiotemporal fusion techniques, integrate from different sensors, garnered significant attention. Over past decade, research on has achieved remarkable progress. Nevertheless, traditional methods encounter difficulties when dealing complicated scenarios. development computer science, deep learning models, such as convolutional neural networks (CNNs), generative adversarial (GANs), Transformers, diffusion recently been introduced into field fusion, resulting efficient accurate algorithms. These algorithms exhibit various strengths limitations, require further analysis comparison. Therefore, this paper reviews literature learning-based methods, analyzes compares algorithms, summarizes current challenges field, proposes possible directions future studies.

Язык: Английский

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FieldSeg: A scalable agricultural field extraction framework based on the Segment Anything Model and 10-m Sentinel-2 imagery

Computers and Electronics in Agriculture, Год журнала: 2025, Номер 232, С. 110086 - 110086

Опубликована: Фев. 15, 2025

Язык: Английский

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