Search Results (38)

The accurate acquisition of agricultural parcels from remote sensing images is crucial for agricultural management and crop production monitoring. Most of the existing agricultural parcel extraction methods comprise semantic segmentation through remote sensing images, pixel-level classification, and then vectorized raster data. However, this approach faces challenges such as internal cavities, unclosed boundaries, and fuzzy edges, which hinder the accurate extraction of complete agricultural parcels. Therefore, this paper proposes a vector contour segmentation network based on the hybrid backbone and multiscale edge feature extraction module (HEVNet). We use the extraction of vector polygons of agricultural parcels by predicting the location of contour points, which avoids the above problems that may occur when raster data is converted to vector data. Simultaneously, this paper proposes a hybrid backbone for feature extraction. A hybrid backbone combines the respective advantages of the Resnet and Transformer backbone networks to balance local features and global features in feature extraction. In addition, we propose a multiscale edge feature extraction module, which can extract and enhance the edge features of different scales to prevent the possible loss of edge details in down sampling. This paper uses the datasets of Denmark, the Netherlands, iFLYTEK, and Hengyang in China to evaluate our model. The obtained IOU indexes were 67.92%, 81.35%, 78.02%, and 66.35%, which are higher than previous IOU indexes based on the optimal model (DBBANet). The results demonstrate that the proposed model significantly enhances the integrity and edge accuracy of agricultural parcel extraction. Full article

Accurate land parcel segmentation in remote sensing imagery is critical for applications such as land use analysis, agricultural monitoring, and urban planning. However, existing methods often underperform in complex scenes due to small-object segmentation challenges, blurred boundaries, and background interference, often influenced by sensor resolution and atmospheric variation. To address these limitations, we propose a dual-stage framework that combines an enhanced YOLOv5s detector with the Segment Anything Model (SAM) to improve segmentation accuracy and robustness. The improved YOLOv5s module integrates Efficient Channel Attention (ECA) and BiFPN to boost feature extraction and small-object recognition, while Soft-NMS is used to reduce missed detections. The SAM module receives bounding-box prompts from YOLOv5s and incorporates morphological refinement and mask stability scoring for improved boundary continuity and mask quality. A composite Focal-Dice loss is applied to mitigate class imbalance. In addition to the publicly available CCF BDCI dataset, we constructed a new WuJiang dataset to evaluate cross-domain performance. Experimental results demonstrate that our method achieves an IoU of 89.8% and a precision of 90.2%, outperforming baseline models and showing strong generalizability across diverse remote sensing conditions. Full article

Prompt and precise cropland mapping is indispensable for safeguarding food security, enhancing land resource utilization, and advancing sustainable agricultural practices. Conventional approaches faced difficulties in complex terrain marked by fragmented plots, pronounced elevation differences, and non-uniform field borders. To address these challenges, we propose DAENet, a novel deep learning framework designed for accurate cropland extraction from high-resolution GaoFen-1 (GF-1) satellite imagery. DAENet employs a novel Geometric-Optimized and Boundary-Restrained (GOBR) Block, which combines channel attention, multi-scale spatial attention, and boundary supervision mechanisms to effectively mitigate challenges arising from disjointed cropland parcels, topography-cast shadows, and indistinct edges. We conducted comparative experiments using 8 mainstream semantic segmentation models. The results demonstrate that DAENet achieves superior performance, with an Intersection over Union (IoU) of 0.9636, representing a 4% improvement over the best-performing baseline, and an F1-score of 0.9811, marking a 2% increase. Ablation analysis further validated the indispensable contribution of GOBR modules in improving segmentation precision. Using our approach, we successfully extracted 25,556.98 hectares of cropland within the study area, encompassing a total of 67,850 individual blocks. Additionally, the proposed method exhibits robust generalization across varying spatial resolutions, underscoring its effectiveness as a high-accuracy solution for agricultural monitoring and sustainable land management in complex terrain. Full article

Fine extraction of cropland parcels is an essential prerequisite for achieving precision agriculture. Remote sensing technology, due to its large-scale and multi-dimensional characteristics, can effectively enhance the efficiency of collecting information on agricultural land parcels. Currently, semantic segmentation models based on high-resolution remote sensing imagery utilize limited spectral information and rely heavily on a large amount of fine data annotation, while pixel classification models based on medium-to-low-resolution multi-temporal remote sensing imagery are limited by the mixed pixel problem. To address this, the study utilizes GF-2 high-resolution imagery and Sentinel-2 multi-temporal data, in conjunction with the basic image segmentation model SAM, by additionally introducing a prompt generation module (Box module and Auto module) to achieve automatic fine extraction of cropland parcels. The research results indicate the following: (1) The mIoU of SAM with the Box module is 0.711, and the OA is 0.831, showing better performance, while the mIoU of SAM with the Auto module is 0.679, and the OA is 0.81, yielding higher-quality cropland masks; (2) The combination of various prompts (box, point, and mask), along with the hierarchical extraction strategy, can effectively improve the performance of Box module SAM; (3) Employing a more accurate prompt data source can significantly boost model performance. The mIoU of the superior-performing Box module SAM is increased to 0.920, and the OA is raised to 0.958. Overall, the improved SAM, while reducing the demand for mask annotation and model training, can achieve high-precision extraction results for cropland parcels. Full article

Accurately extracting parcels from satellite images is crucial in precision agriculture. Traditional edge detection fails in complex scenes with difficult post-processing, and deep learning models are time-consuming in terms of sample preparation and less transferable. Based on this, we designed a method combining multi-resolution remote sensing images based on the Segment Anything Model (SAM). Using cropland masking, overlap prediction and post-processing, we achieved 10 m-resolution parcel extraction with SAM, with performance in plain areas comparable to existing deep learning models (P: 0.89, R: 0.91, F1: 0.91, IoU: 0.87). Notably, in hilly regions with fragmented cultivated land, our approach even outperformed these models (P: 0.88, R: 0.76, F1: 0.81, IoU: 0.69). Subsequently, the 10 m parcels results were utilized to crop the high-resolution image. Based on the histogram features and internal edge features of the parcels, used to determine whether to segment downward or not, and at the same time, by setting the adaptive parameters of SAM, sub-meter parcel extraction was finally realized. Farmland boundaries extracted from high-resolution images can more accurately characterize the actual parcels, which is meaningful for farmland production and management. This study extended the application of deep learning large models in remote sensing, and provided a simple and fast method for accurate extraction of parcels boundaries. Full article

The accurate extraction of cultivated land information is crucial for optimizing regional farmland layouts and enhancing food supply. To address the problem of low accuracy in existing cultivated land products and the poor applicability of cultivated land extraction methods in fragmented, small parcel agricultural landscapes and complex terrain mapping, this study develops an advanced cultivated land extraction model for the western part of Henan Province, China, utilizing Gaofen-2 (GF-2) imagery and an improved U-Net architecture to achieve a 1 m resolution regional mapping in complex terrain. We obtained optimal input data for the U-Net model by fusing spectral features and vegetation index features from remote sensing images. We evaluated and validated the effectiveness of the proposed method from multiple perspectives and conducted a cultivated land change detection and agricultural landscape fragmentation assessment in the study area. The experimental results show that the proposed method achieved an F1 score of 89.55% for the entire study area, with an F1 score ranging from 83.84% to 90.44% in the hilly or transitional zones. Compared to models that solely rely on spectral features, the feature selection-based model demonstrates superior performance in hilly and adjacent mountainous regions, with improvements of 4.5% in Intersection over Union (IoU). Cultivated land mapping results show that 83.84% of the cultivated land parcels are smaller than 0.64 hectares. From 2017 to 2022, the overall cultivated land area decreased by 15.26 km², with the most significant reduction occurring in the adjacent hilly areas, where the land parcels are small and fragmented. This trend highlights the urgent need for effective land management strategies to address fragmentation and prevent further loss of cultivated land in these areas. We anticipate that the findings can contribute to precision agriculture management and agricultural modernization in complex terrains of the world. Full article

Change detection of cultivated land parcels is critical for achieving refined management of farmland. However, existing change detection methods based on high-resolution remote sensing imagery focus primarily on cultivation type changes, neglecting the importance of detecting parcel pattern changes. To address the issue of detecting diverse types of changes in cultivated land parcels, this study constructs an automated workflow framework for change detection, based on the unsupervised segmentation method of the SAM (Segment Anything Model). By performing spatial connection analysis on cultivated land parcel units extracted by the SAM for two phases and combining multiple features such as texture features (GLCM), multi-scale structural similarity (MS-SSIM), and normalized difference vegetation index (NDVI), precise identification of cultivation type and pattern change areas was achieved. The study results show that the proposed method achieved the highest accuracy in detecting parcel pattern changes in plain areas (precision: 78.79%, recall: 79.45%, IOU: 78.44%), confirming the effectiveness of the proposed method. This study provides an efficient and low-cost detection and distinction method for analyzing changes in cultivated land patterns and types using high-resolution remote sensing images, which can be directly applied in real-world scenarios. The method significantly enhances the automation and timeliness of parcel unit change detection, offering important applications for advancing precision agriculture and sustainable land resource management. Full article

Analyzing irrigation patterns to promote efficient water use in urban areas is challenging. Analysis of irrigation by remote sensing (AIRS) combines multispectral aerial imagery, evapotranspiration data, and ground-truth measurements to overcome these challenges. We demonstrate AIRS on eight neighborhoods in Weber County, Utah, using 0.6 m National Agriculture Imagery Program (NAIP) and 0.07 m drone imagery, reference evapotranspiration (ET), and water use records. We calculate the difference between the actual and hypothetical water required for each parcel and compare water use over three time periods (2018, 2021, and 2023). We find that the quantity of overwatering, as well as the number of customers overwatering, is decreasing over time. AIRS provides repeatable estimates of irrigated area and irrigation demand that allow water utilities to track water user habits and landscape changes over time and, when controlling for other variables, see if water conservation efforts are effective. In terms of image analysis, we find that (1) both NAIP and drone imagery are sufficient to measure irrigated area in urban settings, (2) the selection of a threshold value for the normalized difference vegetation index (NDVI) becomes less critical for higher-resolution imagery, and (3) irrigated area measurement can be enhanced by combining NDVI with other tools such as building footprint extraction, object classification, and deep learning. Full article

Effective management of agricultural water resources in arid regions relies on precise estimation of irrigation-water demand. Most previous studies have adopted pixel-level mapping methods to estimate irrigation-water demand, often leading to inaccuracies when applied in arid areas where land salinization is severe and where poorly growing crops cause the growing area to be smaller than the sown area. To address this issue and improve the accuracy of irrigation-water demand estimation, this study utilizes parcel-aggregated cropping structure mapping. We conducted a case study in the Weigan River Basin, Xinjiang, China. Deep learning techniques, the Richer Convolutional Features model, and the bilayer Long Short-Term Memory model were applied to extract parcel-aggregated cropping structures. By analyzing the cropping patterns, we estimated the irrigation-water demand and calculated the supply using statistical data and the water balance approach. The results indicated that in 2020, the cultivated area in the Weigan River Basin was 5.29 × 10⁵ hectares, distributed over 853,404 parcels with an average size of 6202 m². Based on the parcel-aggregated cropping structure, the estimated irrigation-water demand ranges from 25.1 × 10⁸ m³ to 30.0 × 10⁸ m³, representing a 5.57% increase compared to the pixel-level estimates. This increase highlights the effectiveness of the parcel-aggregated cropping structure in capturing the actual irrigation-water requirements, particularly in areas with severe soil salinization and patchy crop growth. The supply was calculated at 24.4 × 10⁸ m³ according to the water balance approach, resulting in a minimal water deficit of 0.64 × 10⁸ m³, underscoring the challenges in managing agricultural water resources in arid regions. Overall, the use of parcel-aggregated cropping structure mapping addresses the issue of irrigation-water demand underestimation associated with pixel-level mapping in arid regions. This study provides a methodological framework for efficient agricultural water resource management and sustainable development in arid regions. Full article

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

Plantation distribution information is of great significance to the government’s macro-control, optimization of planting layout, and realization of efficient agricultural production. Existing studies primarily relied on high spatiotemporal resolution remote sensing data to address same-spectrum, different-object classification by extracting phenological information from temporal imagery. However, the classification problem of orchard or artificial forest, where the spectral and textural features are similar and their phenological characteristics are alike, still presents a substantial challenge. To address this challenge, we innovatively proposed a multi-index entropy weighting DTW method (ETW-DTW), building upon the traditional DTW method with single-feature inputs. In contrast to previous DTW classification approaches, this method introduces multi-band information and utilizes entropy weighting to increase the inter-class distances. This allowed for accurate classification of orchard categories, even in scenarios where the spectral textures were similar and the phenology was alike. We also investigated the impact of fusing optical and Synthetic Aperture Radar (SAR) data on the classification accuracy. By combining Sentinel-1 and Sentinel-2 time series imagery, we validated the enhanced classification effectiveness with the inclusion of SAR data. The experimental results demonstrated a noticeable improvement in orchard classification accuracy under conditions of similar spectral characteristics and phenological patterns, providing comprehensive information for orchard mapping. Additionally, we further explored the improvement in results based on two different parcel-based classification strategies compared to pixel-based classification methods. By comparing the classification results, we found that the parcel-based averaging method has advantages in clearly defining orchard boundaries and reducing noise interference. In conclusion, the introduction of the ETW-DTW method is of significant practical importance in addressing the challenge of same-spectrum, different-object classification. The obtained orchard distribution can provide valuable information for the government to optimize the planting structure and layout and regulate the macroeconomic benefits of the fruit industry. Full article

The accurate extraction of agricultural parcels from remote sensing images is crucial for advanced agricultural management and monitoring systems. Existing methods primarily emphasize regional accuracy over boundary quality, often resulting in fragmented outputs due to uniform crop types, diverse agricultural practices, and environmental variations. To address these issues, this paper proposes DSTBA-Net, an end-to-end encoder–decoder architecture. Initially, we introduce a Dual-Stream Feature Extraction (DSFE) mechanism within the encoder, which consists of Residual Blocks and Boundary Feature Guidance (BFG) to separately process image and boundary data. The extracted features are then fused in the Global Feature Fusion Module (GFFM), utilizing Transformer technology to further integrate global and detailed information. In the decoder, we employ Feature Compensation Recovery (FCR) to restore critical information lost during the encoding process. Additionally, the network is optimized using a boundary-aware weighted loss strategy. DSTBA-Net aims to achieve high precision in agricultural parcel segmentation and accurate boundary extraction. To evaluate the model’s effectiveness, we conducted experiments on agricultural parcel extraction in Denmark (Europe) and Shandong (Asia). Both quantitative and qualitative analyses show that DSTBA-Net outperforms comparative methods, offering significant advantages in agricultural parcel extraction. Full article

The timely and accurate acquisition of information on the distribution of the crop planting structure in the Loess Plateau of eastern Gansu Province, one of the most important agricultural areas in Western China, is crucial for promoting fine management of agriculture and ensuring food security. This study uses multi-temporal high-resolution remote sensing images to determine optimal segmentation scales for various crops, employing the estimation of scale parameter 2 (ESP2) tool and the Ratio of Mean Absolute Deviation to Standard Deviation (RMAS) model. The Canny edge detection algorithm is then applied for multi-scale image segmentation. By incorporating crop phenological factors and using the L1-regularized logistic regression model, we optimized 39 spatial feature factors—including spectral, textural, geometric, and index features. Within a multi-level classification framework, the Random Forest (RF) classifier and Convolutional Neural Network (CNN) model are used to classify the cropping patterns in four test areas based on the multi-scale segmented images. The results indicate that integrating the Canny edge detection algorithm with the optimal segmentation scales calculated using the ESP2 tool and RMAS model produces crop parcels with more complete boundaries and better separability. Additionally, optimizing spatial features using the L1-regularized logistic regression model, combined with phenological information, enhances classification accuracy. Within the OBIC framework, the RF classifier achieves higher accuracy in classifying cropping patterns. The overall classification accuracies for the four test areas are 91.93%, 94.92%, 89.37%, and 90.68%, respectively. This paper introduced crop phenological factors, effectively improving the extraction precision of the shattered agricultural planting structure in the Loess Plateau of eastern Gansu Province. Its findings have important application value in crop monitoring, management, food security and other related fields. Full article

In order to meet the growing demand for food and achieve food security development goals, contemporary agriculture increasingly depends on plastic coverings such as agricultural plastic films. The remote sensing-based identification of these plastic films has gradually become a necessary tool for agricultural production management and soil pollution prevention. Addressing the challenges posed by the complex terrain and fragmented land parcels in karst mountainous regions, as well as the frequent presence of cloudy and foggy weather conditions, the extraction efficacy of mulching films is compromised. This study utilized a DJI Mavic 2 Pro UAV to capture visible light images in an area with complex terrain features such as peaks and valleys. A plastic film sample dataset was constructed, and the U-Net deep learning model parameters integrated into ArcGIS Pro were continuously modified and optimized to achieve precise plastic film identification. The results are as follows: (1) Sample quantity significantly affects recognition performance. When the sample size is 800, the accuracy of plastic film extraction notably improves, with area accuracy reaching 91%, a patch quantity accuracy of 96.38%, and an IOU and F1-score of 85.89% and 94.20%, respectively, compared to the precision achieved with a sample size of 300; (2) Different learning rates, batch sizes, and iteration numbers have a certain impact on the training effectiveness of the U-Net model. The most suitable model parameters improved the training effectiveness, with the highest training accuracy achieved at a learning rate of 0.001, a batch size of 10, and 25 iterations; (3) Comparative experiments with the Support Vector Machine (SVM) model validate the suitability of U-Net model parameters and sample datasets for precise identification in rugged terrains with fragmented spatial distribution, particularly in karst mountainous regions. This underscores the applicability of the U-Net model in recognizing plastic film coverings in karst mountainous regions, offering valuable insights for agricultural environmental health assessment and green planting management in farmlands. Full article

Accurate delineation of individual agricultural plots, the foundational units for agriculture-based activities, is crucial for effective government oversight of agricultural productivity and land utilization. To improve the accuracy of plot segmentation in high-resolution remote sensing images, the paper collects GF-2 satellite remote sensing images, uses ArcGIS10.3.1 software to establish datasets, and builds UNet, SegNet, DeeplabV3+, and TransUNet neural network frameworks, respectively, for experimental analysis. Then, the TransUNet network with the best segmentation effects is optimized in both the residual module and the skip connection to further improve its performance for plot segmentation in high-resolution remote sensing images. This article introduces Deformable ConvNets in the residual module to improve the original ResNet50 feature extraction network and combines the convolutional block attention module (CBAM) at the skip connection to calculate and improve the skip connection steps. Experimental results indicate that the optimized remote sensing plot segmentation algorithm based on the TransUNet network achieves an Accuracy of 86.02%, a Recall of 83.32%, an F1-score of 84.67%, and an Intersection over Union (IOU) of 86.90%. Compared to the original TransUNet network for remote sensing land parcel segmentation, whose F1-S is 81.94% and whose IoU is 69.41%, the optimized TransUNet network has significantly improved the performance of remote sensing land parcel segmentation, which verifies the effectiveness and reliability of the plot segmentation algorithm. Full article