Ground Objects Research Articles

Unmanned aerial vehicle image stitching based on multi‐region segmentation

AbstractUnmanned aerial vehicle (UAV) image stitching is a key technology for aerial remote sensing applications. Most existing image stitching methods based on optimal seamline searching algorithms can eliminate defects such as ghosting and distortion in stitched images, which unfortunately suffer from the problem that the seamline may cross those regions with significant geometric misalignment between different images. Therefore, a novel image stitching method based on multi‐region image segmentation is proposed. The algorithm starts by performing a multi‐scale morphological reconstruction in the overlapping regions between UAV images to obtain superpixel images with precise contours. Then, the fast density peaks clustering based on K‐nearest neighbours is applied to automatically determine the clustering centres and the number of clusters. By constructing a cost function, an energy map is generated in the overlapping regions between UAV images. Finally, the optimal seamline can be determined with a graph‐cut method. Compared to several popular image stitching algorithms in real experiments, the proposed method can essentially prevent the seamline from crossing significant ground objects to ensure the integrity of structural objects while achieving satisfactory accuracy and efficiency during the UAV image stitching process.

RSPS-SAM: A Remote Sensing Image Panoptic Segmentation Method Based on SAM

Satellite remote sensing images contain complex and diverse ground object information and the images exhibit spatial multi-scale characteristics, making the panoptic segmentation of satellite remote sensing images a highly challenging task. Due to the lack of large-scale annotated datasets for panoramic segmentation, existing methods still suffer from weak model generalization capabilities. To mitigate this issue, this paper leverages the advantages of the Segment Anything Model (SAM), which can segment any object in remote sensing images without requiring any annotations and proposes a high-resolution remote sensing image panoptic segmentation method called Remote Sensing Panoptic Segmentation SAM (RSPS-SAM). Firstly, to address the problem of global information loss caused by cropping large remote sensing images for training, a Batch Attention Pyramid was designed to extract multi-scale features from remote sensing images and capture long-range contextual information between cropped patches, thereby enhancing the semantic understanding of remote sensing images. Secondly, we constructed a Mask Decoder to address the limitation of SAM requiring manual input prompts and its inability to output category information. This decoder utilized mask-based attention for mask segmentation, enabling automatic prompt generation and category prediction of segmented objects. Finally, the effectiveness of the proposed method was validated on the high-resolution remote sensing image airport scene dataset RSAPS-ASD. The results demonstrate that the proposed method achieves segmentation and recognition of foreground instances and background regions in high-resolution remote sensing images without the need for prompt input, while providing smooth segmentation boundaries with a panoptic segmentation quality (PQ) of 57.2, outperforming current mainstream methods.

Extraction of Canal Distribution Information Based on UAV Remote Sensing System and Object-Oriented Method

At present, the extraction of irrigation canal network distribution information is of great significance for developing a digital twin irrigation district. However, due to the low resolution of remote sensing images, it is difficult to effectively identify the canal networks, especially for channels with a width of less than 1 m, where recognition is insufficient. Therefore, the purpose of this study is to extract canal networks of different widths in an irrigation district in Shaanxi Province as the research area. A rule-based object-oriented classification method was employed, utilizing image data collected by the DJI Mavic 3 multispectral UAV (Unmanned Aerial Vehicle) to explore the accuracy of this method in extracting canal distribution information. Based on UAV multispectral remote sensing imagery, the segmentation parameters for the remote sensing imagery were determined using ENVI 5.6 software, with the segmentation threshold set at 60 and the merging threshold set at 80. By combining the spectral and spatial differences between the canals and other ground objects, rules for extracting canal network distribution information were established, and the information on the distribution of channels in this irrigation area was finally obtained. The experimental results showed a maximum recall rate of 91.88% and a maximum precision rate of 57.59%. The overall recall precision rates for the irrigation district were 85.74% and 55.08%, respectively. This method provides a new solution for identifying and extracting canal systems in irrigation districts, offering valuable insights for acquiring canal distribution information and providing a scientific basis for precision irrigation.

Improving Real-Scene 3D Model Quality of Unmanned Aerial Vehicle Oblique-Photogrammetry with a Ground Camera

In UAV (unmanned aerial vehicle) oblique photogrammetry, the occlusion of ground objects, particularly at their base, often results in low-quality real-scene 3D models. To address this issue, we propose a method to enhance model quality by integrating ground-based camera images. This innovative image acquisition method allows the rephotographing of areas in the 3D model that exhibit poor quality. Three critical parameters for reshooting are the reshooting distance and the front- and side-overlap ratios of reshooting images. The proposed method for improving 3D model quality focuses on point accuracy, dimensional accuracy, texture details, and the triangular mesh structure. Validated by a case study involving a complex building, this method demonstrates that integrating ground camera photos significantly improves the overall quality of the 3D model. The findings show that optimal settings for reshooting include a distance (in meter units) of 1.5–1.6 times the camera’s focal length (in millimeter units), a front overlap ratio of 30%, and a side overlap ratio of 20%. Furthermore, we conclude that an overlap rate of 20–30% in reshooting is a usable value.

Remote Sensing Image Segmentation Based on Probabilistic Fuzzy Local Information Clustering

Abstract. In this paper, a remote sensing image segmentation based on probabilistic fuzzy local information clustering algorithm is proposed. First, assuming that the spectral measure within the same ground object follows the same probability distribution. The dissimilarity between pixel and object is modeled by the negative logarithm of the Gaussian probability density function. It can improve the noise sensitive problem caused by the Euclidean distance which can only describe the data with isotropic distribution. Then, in order to consider the effect of local spatial constraint, on the one hand, the probability measure is used to modify the local fuzzy factor to establish the dissimilarity measure with spatial constraints. On the other hand, the hidden Markov random field is used to model the prior probability model of pixel membership. Next, the entropy regularization term of the objective function is built by combining the Kullback-Leibler(KL) maximum entropy model to further improve the robustness and noise resistance. The qualitative and quantitative analysis of simulated image and different types of real remote sensing images show that the proposed algorithm can effectively overcome the above problems and further improve the accuracy of image segmentation to over 95%.

UNeXt: An Efficient Network for the Semantic Segmentation of High-Resolution Remote Sensing Images

The application of deep neural networks for the semantic segmentation of remote sensing images is a significant research area within the field of the intelligent interpretation of remote sensing data. The semantic segmentation of remote sensing images holds great practical value in urban planning, disaster assessment, the estimation of carbon sinks, and other related fields. With the continuous advancement of remote sensing technology, the spatial resolution of remote sensing images is gradually increasing. This increase in resolution brings about challenges such as significant changes in the scale of ground objects, redundant information, and irregular shapes within remote sensing images. Current methods leverage Transformers to capture global long-range dependencies. However, the use of Transformers introduces higher computational complexity and is prone to losing local details. In this paper, we propose UNeXt (UNet+ConvNeXt+Transformer), a real-time semantic segmentation model tailored for high-resolution remote sensing images. To achieve efficient segmentation, UNeXt uses the lightweight ConvNeXt-T as the encoder and a lightweight decoder, Transnext, which combines a Transformer and CNN (Convolutional Neural Networks) to capture global information while avoiding the loss of local details. Furthermore, in order to more effectively utilize spatial and channel information, we propose a SCFB (SC Feature Fuse Block) to reduce computational complexity while enhancing the model’s recognition of complex scenes. A series of ablation experiments and comprehensive comparative experiments demonstrate that our method not only runs faster than state-of-the-art (SOTA) lightweight models but also achieves higher accuracy. Specifically, our proposed UNeXt achieves 85.2% and 82.9% mIoUs on the Vaihingen and Gaofen5 (GID5) datasets, respectively, while maintaining 97 fps for 512 × 512 inputs on a single NVIDIA GTX 4090 GPU, outperforming other SOTA methods.

Research on coastline extraction and dynamic change from remote sensing images based on deep learning

Accurate coastline extraction is crucial for the scientific management and protection of coastal zones. Due to the diversity of ground object details and the complexity of terrain in remote sensing images, the segmentation of sea and land faces challenges such as unclear segmentation boundaries and discontinuous coastline contours. To address these issues, this study improve the accuracy and efficiency of coastline extraction by improving the DeepLabv3+ model. Specifically, this study constructs a sea-land segmentation network, DeepSA-Net, based on strip pooling and coordinate attention mechanisms. By introducing dynamic feature connections and strip pooling, the connection between different branches is enhanced, capturing a broader context. The introduction of coordinate attention allows the model to integrate coordinate information during feature extraction, thereby allowing the model to capture longer-distance spatial dependencies. Experimental results has shown that the model can achieves a land-sea segmentation mean intersection over union (mIoU) ration and Recall of over 99% on all datasets. Visual assessment results show more complete edge details of sea-land segmentation, confirming the model’s effectiveness in complex coastal environments. Finally, using remote sensing data from a coastal area in China as an application instance, coastline extraction and dynamic change analysis were implemented, providing new methods for the scientific management and protection of coastal zones.

Annotated Dataset for Training Cloud Segmentation Neural Networks Using High-Resolution Satellite Remote Sensing Imagery

The integration of satellite data with deep learning has revolutionized various tasks in remote sensing, including classification, object detection, and semantic segmentation. Cloud segmentation in high-resolution satellite imagery is a critical application within this domain, yet progress in developing advanced algorithms for this task has been hindered by the scarcity of specialized datasets and annotation tools. This study addresses this challenge by introducing CloudLabel, a semi-automatic annotation technique leveraging region growing and morphological algorithms including flood fill, connected components, and guided filter. CloudLabel v1.0 streamlines the annotation process for high-resolution satellite images, thereby addressing the limitations of existing annotation platforms which are not specifically adapted to cloud segmentation, and enabling the efficient creation of high-quality cloud segmentation datasets. Notably, we have curated the Annotated Dataset for Training Cloud Segmentation (ADTCS) comprising 32,065 images (512 × 512) for cloud segmentation based on CloudLabel. The ADTCS dataset facilitates algorithmic advancement in cloud segmentation, characterized by uniform cloud coverage distribution and high image entropy (mainly 5–7). These features enable deep learning models to capture comprehensive cloud characteristics, enhancing recognition accuracy and reducing ground object misclassification. This contribution significantly advances remote sensing applications and cloud segmentation algorithms.

Remote sensing image fusion method based on depth feature extraction

Abstract Remote sensing image fusion involves combining data from various remote sensing sensors or merging multiple images acquired by the same sensor but in different spectral bands to create a superior quality composite image, high resolution, multi-band, and multi-temporal remote sensing image through certain algorithms and technologies. By utilizing fusion techniques, remote sensing images of varying bands, resolutions, and phases can be combined to enhance the accuracy of ground object information. This paper introduces a remote sensing image fusion method that leverages deep learning for depth feature extraction. Swin Transformer makes full use of the characteristics of the relationship between adjacent pixels, and adaptively extracts panchromatic image (PAN) and multispectral image (MS) features through two adaptive feature extraction channels. High-quality satellite data is employed to confirm the efficacy of the suggested approach, with the findings demonstrating strong performance in ERGAS, SCC, SSIM, and PSNR, and other evaluation indexes and has good spectral information fidelity and spatial information integration degree.

Blind Deblurring Method for CASEarth Multispectral Images Based on Inter-Band Gradient Similarity Prior.

Multispectral remote sensing images contain abundant information about the distribution and reflectance of ground objects, playing a crucial role in target detection, environmental monitoring, and resource exploration. However, due to the complexity of the imaging process in multispectral remote sensing, image blur is inevitable, and the blur kernel is typically unknown. In recent years, many researchers have focused on blind image deblurring, but most of these methods are based on single-band images. When applied to CASEarth satellite multispectral images, the spectral correlation is unutilized. To address this limitation, this paper proposes a novel approach that leverages the characteristics of multispectral data more effectively. We introduce an inter-band gradient similarity prior and incorporate it into the patch-wise minimal pixel (PMP)-based deblurring model. This approach aims to utilize the spectral correlation across bands to improve deblurring performance. A solution algorithm is established by combining the half-quadratic splitting method with alternating minimization. Subjectively, the final experiments on CASEarth multispectral images demonstrate that the proposed method offers good visual effects while enhancing edge sharpness. Objectively, our method leads to an average improvement in point sharpness by a factor of 1.6, an increase in edge strength level by a factor of 1.17, and an enhancement in RMS contrast by a factor of 1.11.

Moderate Red-Edge vegetation index for High-Resolution multispectral remote sensing images in urban areas

Urban green space (UGS) monitoring is significant in optimizing urban planning, protecting the ecological environment, and improving residents’ quality of life. However, in urban environments, shadow interference and the emergence of new construction materials pose challenges to monitoring green vegetation in high-resolution imagery. This study found that existing vegetation indices (VIs), such as normalized difference vegetation index (NDVI) and enhanced vegetation index (EVI), perform inadequately in extracting vegetation in urban areas, resulting in significant omissions and errors. By conducting in-depth analysis and quantitative experiments on the reflectance of typical urban ground objects, this study developed a new vegetation extraction method, the moderate red-edge vegetation index (MREVI), to enhance the extraction accuracy of UGS vegetation from unmanned aerial vehicle (UAV) high-resolution multispectral remote sensing (RS) images. Experimental results demonstrate that MREVI performs exceptionally well in complex urban environments, significantly suppressing non-vegetation areas, achieving an overall accuracy (OA) of 98.6% and a Kappa coefficient of 0.97. This study supports for urban planning, UGS monitoring, and the evaluation of urban plant carbon sequestration capacity.

Advancing robots with greater dynamic dexterity: A large-scale multi-view and multi-modal dataset of human-human throw&catch of arbitrary objects

Learning and imitating behavioral intelligence from human demonstrations is a promising approach towards the intuitive programming of robots for enhanced dynamic dexterity. However, there has been no publicly available dataset in this domain. To address this gap, we introduce the first large-scale dataset and recording framework specifically designed for studying human collaborative dynamic dexterity in throw&catch tasks. The dataset, named H2TC, contains 15,000 multi-view and multi-modal synchronized recordings of diverse Human-Human Throw-and-Catch activities. It involves 34 human subjects with typical motor abilities and a variety of 52 objects frequently manipulated through throw&catch in domestic and/or industrial scenarios. The dataset is supplemented with a hierarchy of manually annotated semantic and dense labels, such as the ground truth human body, hand and object motions captured with specialized high-precision motion tracking systems. These rich annotations make the dataset well-suited for a wide range of robot studies, including both low-level motor skill learning and high-level cognitive planning and recognition. We envision that the proposed dataset and recording framework will facilitate learning pipelines to extract insights on how humans coordinate both intra- and interpersonally to throw and catch objects, ultimately leading to the development of more capable and collaborative robots. The dataset, along with a suite of utility tools, such as those for visualization and annotation, can be accessed from our project page at https://h2tc-roboticsx.github.io/ .

Class incremental learning with analytic learning for hyperspectral image classification

Hyperspectral image classification (HSIC) is crucial for applications in agriculture and environmental monitoring. As ground objects evolve and remote sensing technology progresses, there is a growing need for HSIC models that can adapt to new data classes without requiring to be retrained from scratch. Throughout the continual learning procedure, the model is expected to not only effectively extract spatial–spectral features from the hyperspectral image but also alleviate the issue of catastrophic forgetting, i.e., the model forgets the learned classes’ knowledge when accessing novel classes during the training process. In this paper, for the HSIC task, we propose a class incremental learning method (HSI-CIL) that is based on analytic learning, a technique that converts network training into linear problems. Specifically, The HSI-CIL model consists of a lightweight feature extractor, a Feature Processing Module (FPM) and an Analytic Linear Classifier (ALC). This model does not need data storage for old and new classes and has only one epoch in the incremental learning stage, so it has lower consumption of resources and training time than several attempts have been proposed for addressing catastrophic forgetting. We perform abundant experiments with the proposed HSI-CIL on three publicly available hyperspectral datasets including Indian Pines, Pavia University, and Salinas. The experiments demonstrate that our HSI-CIL exceeds the state-of-the-art class incremental learning (CIL) techniques applied in HSIC with a certain gap.

MLFA-Net: multi-level feature-aggregated network for semantic change detection in remote sensing images

ABSTRACT The rapid advancement in Earth observation technologies has improved the acquisition of precise data on terrestrial changes. However, traditional binary change detection (BCD) fails to satisfy the complex demands of contemporary applications. In contrast, semantic change detection (SCD) can identify changed areas and further detect the types of changes in ground objects. Nonetheless, current SCD approaches pose significant challenges, including the inadequate extraction and utilization of multi-level features and limited synergistic integration of related tasks. This paper proposes a multi-level feature-aggregated network (MLFA-Net), which decomposes the SCD task into two related subtasks: ground object classification and BCD of bi-temporal images. To optimize the classification performance, MLFA-Net incorporates feature alignment and cross-level feature aggregation modules that operate between the encoders and decoders, achieving effective extraction, aggregation, and utilization of multi-level image features. MLFA-Net also integrates symmetric transform feature aggregation modules between classification and BCD branches, leveraging prior knowledge to facilitate cross-task transmission of multi-level information and further optimize SCD results. MLFA-Net achieved Scores of 0.6214 and 0.3581 on Landsat-SCD and SECOND datasets, respectively, with improvements of at least 0.0402 and 0.0035 compared to other methods. Quantitative and qualitative experiments validate the efficacy and superiority of the proposed MLFA-Net in SCD tasks.

Urban traffic tiny object detection via attention and multi-scale feature driven in UAV-vision

The unmanned aerial vehicle (UAV) city patrol is of great significance in ensuring the safety of residents’ lives and properties, as well as maintaining the normal operation of the city. However, the detection of UAV images faces challenges such as numerous small-scale objects, complex backgrounds, and high requirements for detection speed. In response to these issues, we introduce a Real-time Small Object Detection network in UAV-vision (RTS-Net), tailored for UAV patrols. Initially, we introduce a multiscale feature fusion module (MFFM) designed to augment the expressiveness of features across scales, thereby enhancing the detection of smaller objects. Subsequently, leveraging attention mechanisms, we present the coordinated attention detection module (CADM), which bolsters the detection model’s ability to accurately segregate objects from the background in expansive, complex scenarios. Lastly, a lightweight real-time feature extraction module (RFEM) is crafted to diminish model computational complexity and boost inference speed. On the UAV road patrol image dataset we constructed, our proposed method attains a detection accuracy of 89.9%\\documentclass[12pt]{minimal} \\usepackage{amsmath} \\usepackage{wasysym} \\usepackage{amsfonts} \\usepackage{amssymb} \\usepackage{amsbsy} \\usepackage{mathrsfs} \\usepackage{upgreek} \\setlength{\\oddsidemargin}{-69pt} \\begin{document}$$\\%$$\\end{document} mAP, breaking previous records. It surpasses all prevailing detection methods, particularly for small-scale objects. Simultaneously, it achieves an inference speed of 163.9 FPS. The experimental results show that RTS-Net can satisfy the accurate and efficient detection of ground objects by various different UAV platforms in different complex scenarios.

ConvMambaSR: Leveraging State-Space Models and CNNs in a Dual-Branch Architecture for Remote Sensing Imagery Super-Resolution

Deep learning-based super-resolution (SR) techniques play a crucial role in enhancing the spatial resolution of images. However, remote sensing images present substantial challenges due to their diverse features, complex structures, and significant size variations in ground objects. Moreover, recovering lost details from low-resolution remote sensing images with complex and unknown degradations, such as downsampling, noise, and compression, remains a critical issue. To address these challenges, we propose ConvMambaSR, a novel super-resolution framework that integrates state-space models (SSMs) and Convolutional Neural Networks (CNNs). This framework is specifically designed to handle heterogeneous and complex ground features, as well as unknown degradations in remote sensing imagery. ConvMambaSR leverages SSMs to model global dependencies, activating more pixels in the super-resolution task. Concurrently, it employs CNNs to extract local detail features, enhancing the model’s ability to capture image textures and edges. Furthermore, we have developed a global–detail reconstruction module (GDRM) to integrate diverse levels of global and local information efficiently. We rigorously validated the proposed method on two distinct datasets, RSSCN7 and RSSRD-KQ, and benchmarked its performance against state-of-the-art SR models. Experiments show that our method achieves SOTA PSNR values of 26.06 and 24.29 on these datasets, respectively, and is visually superior, effectively addressing a variety of scenarios and significantly outperforming existing methods.

A new method to extract coal-covered area in open-pit mine based on remote sensing

ABSTRACT Coal is a prominent energy resource in China. Therefore, it is crucial to accurately extract the coal-covered areas. Taking Xinjiang’s Zhundong mining region as the study area, Landsat 8 OLI data were employed to assess the spectral curves of four typical ground objects, including vegetation, bare land, water body and bare coal. The Normalized Difference Bare Coal Index (NDBCI) and the Normalized Difference Coal Index (NDCI) were developed to extract coal-covered area. Higher-resolution Sentinel-2B data for the same period were used for verification, with extraction accuracy evaluated by five metrics including Kappa coefficient, Overall accuracy, Checking accuracy, Checking completeness and F1-score. The results of extracting coal-covered areas showed that (1) the NDBCI showed ‘internal fragmentation’ and the NDCI demonstrated ‘pixel overflow’ during the extraction process. Therefore, we determined the optimal thresholds −0.03 for NDBCI and 0.04 for NDCI. (2) NDBCI distinguished pixels with lower grey-scale values, such as water body, road and gangue. However, some dump zones and shed patches were misclassified. (3) NDCI generated clear boundaries and more complete interiors, and the dump zone and shed could be distinguished. However, some water body parts were misclassified as coal-covered areas. (4) Combined application of NDBCI and NDCI generated a ‘complementary’ effect better than both individual modes. Kappa coefficient, Overall accuracy and F1-score reached 0.95, 98.76% and 0.75, respectively. This study successfully extracted coal-covered areas by developing a remote sensing index based on spectral traits and a priori knowledge of the study area. The proposed combined extraction mode achieved high accuracy for rapid and reliable identification of coal-covered areas.

Hyperspectral Anomaly Detection Based on Spectral Similarity Variability Feature.

In the traditional method for hyperspectral anomaly detection, spectral feature mapping is used to map hyperspectral data to a high-level feature space to make features more easily distinguishable between different features. However, the uncertainty in the mapping direction makes the mapped features ineffective in distinguishing anomalous targets from the background. To address this problem, a hyperspectral anomaly detection algorithm based on the spectral similarity variability feature (SSVF) is proposed. First, the high-dimensional similar neighborhoods are fused into similar features using AE networks, and then the SSVF are obtained using residual autoencoder. Finally, the final detection of SSVF was obtained using Reed and Xiaoli (RX) detectors. Compared with other comparison algorithms with the highest accuracy, the overall detection accuracy (AUCODP) of the SSVFRX algorithm is increased by 0.2106. The experimental results show that SSVF has great advantages in both highlighting anomalous targets and improving separability between different ground objects.

Multiobjective sparse unmixing based hyperspectral change detection

Hyperspectral unmixing can provide the composition of ground objects, while change detection can identify the changes of the same region over time. Therefore, unmixing based hyperspectral change detection can investigate changes in a subpixel-level. It can provide not only whether the change happens or not but also how to change. This paper will gradually establish an unconstrained sparse unmixing based change detection model and design a multiobjective optimization sparse unmixing approach (termed MoSU-CD) to solve it for more subpixel-level change details. Compared with the existing sparse unmixing based change detection approaches, MoSU-CD directly unmixes the difference map of multi-temporal images rather than multi-temporal images themselves respectively. Then it explores the change details of one substance in each pixel based on the unmixing results, i.e., endmembers and abundances. In addition, in order to improve the change detection accuracy, an ensemble decision strategy is designed for the Pareto optimal solutions obtained by multiobjective optimization method. Based on this strategy, the subpixel-level changes can be aggregated to generate an aggressive pixel-level change map. The experimental results on synthetic and real data sets demonstrate that the proposed MoSU-CD not only outperforms the benchmark and state-of-art approaches in terms of pixel-level change detection but also provides additional subpixel-level change information.

Using the MSFNet Model to Explore the Temporal and Spatial Evolution of Crop Planting Area and Increase Its Contribution to the Application of UAV Remote Sensing

Remote sensing technology can be used to monitor changes in crop planting areas to guide agricultural production management and help achieve regional carbon neutrality. Agricultural UAV remote sensing technology is efficient, accurate, and flexible, which can quickly collect and transmit high-resolution data in real time to help precision agriculture management. It is widely used in crop monitoring, yield prediction, and irrigation management. However, the application of remote sensing technology faces challenges such as a high imbalance of land cover types, scarcity of labeled samples, and complex and changeable coverage types of long-term remote sensing images, which have brought great limitations to the monitoring of cultivated land cover changes. In order to solve the abovementioned problems, this paper proposed a multi-scale fusion network (MSFNet) model based on multi-scale input and feature fusion based on cultivated land time series images, and further combined MSFNet and Model Diagnostic Meta Learning (MAML) methods, using particle swarm optimization (PSO) to optimize the parameters of the neural network. The proposed method is applied to remote sensing of crops and tomatoes. The experimental results showed that the average accuracy, F1-score, and average IoU of the MSFNet model optimized by PSO + MAML (PSML) were 94.902%, 91.901%, and 90.557%, respectively. Compared with other schemes such as U-Net, PSPNet, and DeepLabv3+, this method has a better effect in solving the problem of complex ground objects and the scarcity of remote sensing image samples and provides technical support for the application of subsequent agricultural UAV remote sensing technology. The study found that the change in different crop planting areas was closely related to different climatic conditions and regional policies, which helps to guide the management of cultivated land use and provides technical support for the realization of regional carbon neutrality.