Crop Type Classification Research Articles

Self-Supervised Learning across the Spectrum

Satellite image time series (SITS) segmentation is crucial for many applications, like environmental monitoring, land cover mapping, and agricultural crop type classification. However, training models for SITS segmentation remains a challenging task due to the lack of abundant training data, which requires fine-grained annotation. We propose S4, a new self-supervised pretraining approach that significantly reduces the requirement for labeled training data by utilizing two key insights of satellite imagery: (a) Satellites capture images in different parts of the spectrum, such as radio frequencies and visible frequencies. (b) Satellite imagery is geo-registered, allowing for fine-grained spatial alignment. We use these insights to formulate pretraining tasks in S4. To the best of our knowledge, S4 is the first multimodal and temporal approach for SITS segmentation. S4’s novelty stems from leveraging multiple properties required for SITS self-supervision: (1) multiple modalities, (2) temporal information, and (3) pixel-level feature extraction. We also curate m2s2-SITS, a large-scale dataset of unlabeled, spatially aligned, multimodal, and geographic-specific SITS that serves as representative pretraining data for S4. Finally, we evaluate S4 on multiple SITS segmentation datasets and demonstrate its efficacy against competing baselines while using limited labeled data. Through a series of extensive comparisons and ablation studies, we demonstrate S4’s ability as an effective feature extractor for downstream semantic segmentation.

Hierarchical classification for improving parcel-scale crop mapping using time-series Sentinel-1 data

Parcel-scale crop classification utilizing time-series satellite observations is of significant importance in precision agriculture. The prior knowledge that crop types can be organized in a hierarchical tree structure is beneficial for improving crop classification. Moreover, the crop hierarchy aligns with the coarse-to-fine cognitive process of geographic scenes. Based on the crop hierarchy, this study developed a general hierarchical classification framework for enhancing crop mapping using time-series Sentinel-1 data. Central to this method is a deep-learning-based hierarchical classification model that explores and makes use of crop hierarchical knowledge. First, preprocessed Sentinel-1 data were geometrically overlaid onto farmland parcel maps to derive parcel-scale time-series features. Second, we constructed a hierarchical crop type system for study areas based on the crop phenology of labeled crop-type samples. Third, we developed a deep-learning-based hierarchical classification model to identify crop types for each parcel, to generate final crop-type classification maps. The proposed approach was further discussed and verified through the implementation of parcel-scale time-series crop hierarchical classifications in a study area in France with farmland parcel maps and time-series Sentinel-1 data. The classification results, indicating significant improvements greater than 4.0% in overall accuracy and 5.0% in F1 score over comparative methods, demonstrated the effectiveness of the proposed method in learning multi-scale time-series features for hierarchical crop classification utilizing Sentinel-1 data sequences.

Enhancing food crop classification in agriculture through dipper throat optimization and deep learning with remote sensing

Remote sensing images (RSIs), a keystone of modern agricultural technology, refer to spectral or visual data captured from drones, satellites, or aircraft without direct physical contact with the Earth's surface. These images provide a wide-ranging view of agricultural landscapes, providing valuable insights into land use, crop health, and environmental conditions. Agricultural food crop classification, a vital application within precision agriculture, includes the detection and classification of different crops cultivated in a certain region. Traditionally reliant on manual techniques, the development of technologies, particularly the incorporation of RSIs, has revolutionized this process. Agricultural food crop classification has become more sophisticated and automated by harnessing the wealth of data received from RS, which facilitates precise management and monitoring of crops on a large scale. Deep learning (DL), a branch of artificial intelligence, plays a more effective role in these synergies. The incorporation of DL into the RSI analysis enables high-precision and efficient detection of various crop types, assisting more informed decision-making in agriculture. This study proposes a new Dipper Throat Optimization Algorithm with Deep Learning based Food Crop Classification (DTOADL-FCC) algorithm using Remote Sensing Imaging for Agricultural Resource Management. The DTOADL-FCC method aims to apply DL algorithms for the classification of different crop types. In the DTOADL-FCC method, fully convolutional network (FCN) based segmentation process is performed. Next, the DTOADL-FCC method exploits the SE-ResNet model for learning intrinsic and complex features. The DTOADL-FCC method makes use of DTOA for the hyperparameter tuning process. Lastly, the classification of crop types takes place using the extreme learning machine (ELM) model. The study utilizes mathematical formulations including activation functions, loss functions, fitness calculations, and iterative update processes. A brief set of simulations showcases that the DTOADL-FCC method achieves remarkable performance over other techniques with much improved results.

Crop type classification, trends, and patterns of central California agricultural fields from 2005 to 2020

AbstractCalifornia produces many key agricultural products in the United States. Current geospatial agricultural datasets are limited in mapping accuracy, spatial context, or observation period. This study uses machine learning and high‐resolution imagery to produce a time series of crop maps to assess crop type trends and patterns across central California from 2005 to 2020. National Agriculture Imagery Program and Landsat imagery were used to classify nine crop types that are common in the study region: grain crops, field crops, rice, citrus and subtropical, deciduous fruit and nut, vineyard, berry and vegetable, pasture, and fallow/young perennial crop types. To create labeled data, we sampled 1253 fields and manually identified crop types for each examined year using high‐resolution imagery and Landsat normalized difference vegetation index time series. We applied a random forest machine learning algorithm in Google Earth Engine. Results show that the mean overall classification accuracy of the nine‐class map was 93.1%, with individual accuracies ranging from 99.3% (rice) to 89.5% (fallow/young perennial). Mann–Kendall trend tests showed significant (p < 0.05) declines in field crop and pasture area during the study period, while deciduous fruit and nut, citrus and subtropical, and fallow/young perennial crop types experienced significant increases. At an aggregate level, there was a general shift from annual crop types to perennial crop types. These data provide a 16‐year time span of spatially explicit crop type classifications, trends, and patterns in central California that can be used to aid managers and decision makers for resource planning or hazard mitigation.

Exploring optimal features and image analysis methods for crop type classification from the perspective of crop landscape heterogeneity

Agricultural landscape structure (e.g., the shape of fields, crop diversity, and landscape heterogeneity) greatly influences the selection of methods for large-scale crop mapping using remote sensing data. However, in-depth assessments of its impacts on crop mapping remain infrequent in the existing literature. This study investigated the optimal crop identification features and image analysis methods including pixel- and object-based approaches on crop classification, through the integration of spectral and textural features across various quantitative agricultural landscapes. In the experiments, crop fields were initially delineated into four distinct landscapes using the K-means clustering algorithm based on analyzing 13 selected landscape metrics such as PLAND, LSI and SHDI. Both pixel- and object-based approaches were then employed to conduct crop classification was then conducted using 48 selected features including 9 band reflectance, 23 vegetation indices (VIs), and 16 textures) and two image analysis methods. Specifically, five classification schemes for the different combinations of feature datasets and image analysis methods were explored to assess the impacts of crop heterogeneity on crop classification. Results indicated the five landscape metrics (e.g., SPLIT, SHEI, Average distance, etc.) performed best in assessing crop heterogeneity. In general, spectral bands and VIs had a higher contribution in the compositional heterogeneity, while textural features and VIs played a more important role in the configurational heterogeneity. VIs in the object-based approach and texture features in the pixel-based approach can improved crop classification accuracy in configurational landscapes. The findings provide a theoretical basis on selecting optimal features and image analysis methods for crop classification in complex agricultural landscapes.

Correction: Osouli, M.; F. Samavati, F. Evaluation of Data Sufficiency for Interannual Knowledge Transfer of Crop Type Classification Models. Remote Sens. 2024, 16, 2007

The authors would like to add an acknowledgment to their paper [...]

Early-Season Crop Mapping by PRISMA Images Using Machine/Deep Learning Approaches: Italy and Iran Test Cases

Despite its high importance for crop yield prediction and monitoring, early-season crop mapping is severely hampered by the absence of timely ground truth. To cope with this issue, this study aims at evaluating the capability of PRISMA hyperspectral satellite images compared with Sentinel-2 multispectral imagery to produce early- and in-season crop maps using consolidated machine and deep learning algorithms. Results show that the accuracy of crop type classification using Sentinel-2 images is meaningfully poor compared with PRISMA (14% in overall accuracy (OA)). The 1D-CNN algorithm, with 89%, 91%, and 92% OA for winter, summer, and perennial cultivations, respectively, shows for the PRISMA images the highest accuracy in the in-season crop mapping and the fastest algorithm that achieves acceptable accuracy (OA 80%) for the winter, summer, and perennial cultivations early-season mapping using PRISMA images. Moreover, the 1D-CNN algorithm shows a limited reduction (6%) in performance, appearing to be the best algorithm for crop mapping within operational use in cross-farm applications. Machine/deep learning classification algorithms applied on the test fields cross-scene demonstrate that PRISMA hyperspectral time series images can provide good results for early- and in-season crop mapping.

Crop Type Classification using Sentinel 2A-Derived Normalized Difference Red Edge Index (NDRE) and Machine Learning Approach

Satellite remote sensing (RS) enables the extraction of vital information on land cover and crop type. Land cover and crop type classification using RS data and machine learning (ML) techniques have recently gained considerable attention in the scientific community. This study aimed to enhance remote sensing research using high-resolution satellite imagery and a ML approach. To achieve this objective, ML algorithms were employed to demonstrate whether it was possible to accurately classify various crop types within agricultural areas using the Sentinel 2A-derived Normalized Difference Red Edge Index (NDRE). Five ML classifiers, namely Support Vector Machines (SVM), Random Forest (RF), Decision Tree (DT), K-Nearest Neighbors (KNN), and Multi-Layer Perceptron (MLP), were implemented using Python programming on Google Colaboratory. The target land cover classes included cereals, fallow, forage, fruits, grassland-pasture, legumes, maize, sugar beet, onion-garlic, sunflower, and watermelon-melon. The classification models exhibited strong performance, evidenced by their robust overall accuracy (OA). The RF model outperformed, with an OA rate of 95% and a Kappa score of 92%. It was followed by DT (88%), KNN (87%), SVM (85%), and MLP (82%). These findings showed the possibility of achieving high classification accuracy using NDRE from a few Sentinel 2A images. This study demonstrated the potential enhancement of the application of high-resolution satellite RS data and ML for crop type classification in regions that have received less attention in previous studies.

Use of Optical and Radar Imagery for Crop Type Classification in Africa: A Review.

Multi-source remote sensing-derived information on crops contributes significantly to agricultural monitoring, assessment, and management. In Africa, some challenges (i.e., small-scale farming practices associated with diverse crop types and agricultural system complexity, and cloud coverage during the growing season) can imped agricultural monitoring using multi-source remote sensing. The combination of optical remote sensing and synthetic aperture radar (SAR) data has emerged as an opportune strategy for improving the precision and reliability of crop type mapping and monitoring. This work aims to conduct an extensive review of the challenges of agricultural monitoring and mapping in Africa in great detail as well as the current research progress of agricultural monitoring based on optical and Radar satellites. In this context optical data may provide high spatial resolution and detailed spectral information, which allows for the differentiation of different crop types based on their spectral signatures. However, synthetic aperture radar (SAR) satellites can provide important contributions given the ability of this technology to penetrate cloud cover, particularly in African tropical regions, as opposed to optical data. This review explores various combination techniques employed to integrate optical and SAR data for crop type classification and their applicability and limitations in the context of African countries. Furthermore, challenges are discussed in this review as well as and the limitations associated with optical and SAR data combination, such as the data availability, sensor compatibility, and the need for accurate ground truth data for model training and validation. This study also highlights the potential of advanced modelling (i.e., machine learning algorithms, such as support vector machines, random forests, and convolutional neural networks) in improving the accuracy and automation of crop type classification using combined data. Finally, this review concludes with future research directions and recommendations for utilizing optical and SAR data combination techniques in crop type classification for African agricultural systems. Furthermore, it emphasizes the importance of developing robust and scalable classification models that can accommodate the diversity of crop types, farming practices, and environmental conditions prevalent in Africa. Through the utilization of combined remote sensing technologies, informed decisions can be made to support sustainable agricultural practices, strengthen nutritional security, and contribute to the socioeconomic development of the continent.

Evaluation of Data Sufficiency for Interannual Knowledge Transfer of Crop Type Classification Models

We present a study on the effectiveness of using varying data sizes to transfer crop type classification models from one year to the next, emphasizing the balance between data sufficiency and model accuracy. The significance of crop detection through satellite imaging lies in its potential to enhance agricultural productivity and resource management. Machine learning, particularly techniques like long short-term memory (LSTM) models, has become instrumental in interpreting these satellite data due to its predictive accuracy and adaptability. However, the direct application of models trained in one year to subsequent years poses challenges due to variations in environmental conditions and agricultural practices. Fine-tuning pre-existing models is a prevalent strategy to overcome these temporal discrepancies, though it necessitates a careful evaluation of the quantity and relevance of new data. This study explores the cost–benefit of fine-tuning existing models versus developing new ones based on the quantity of new data, utilizing LSTM models for their transferability and practicality in agricultural applications. Experiments conducted using satellite data from farms in southern Alberta reveal that smaller datasets, with fewer than 25 fields per class, can effectively fine-tune models for accurate interannual classification, while larger datasets are more conducive to training new models. This poses a key challenge in optimizing data usage for crop classification, straddling the line between data sufficiency and computational efficiency. The findings offer valuable insights for optimizing data use in crop classification, benefiting both academic research and practical agricultural applications.

Using AI to Empower Norwegian Agriculture: Attention-Based Multiple-Instance Learning Implementation

Agricultural development is one of the most essential needs worldwide. In Norway, the primary foundation of grain production is based on geological and biological features. Existing research is limited to regional-scale yield predictions using artificial intelligence (AI) models, which provide a holistic overview of crop growth. In this paper, the authors propose detecting several field-scale crop types and use this analysis to predict yield production early in the growing season. In this study, the authors utilise a multi-temporal satellite image, meteorological, geographical, and grain production data corpus. The authors extract relevant vegetation indices from satellite images. Furthermore, the authors use field-area-specific features to build a field-based crop type classification model. The proposed model, consisting of a time-distributed network and a gated recurrent unit, can efficiently classify crop types with an accuracy of 70%. In addition, the authors justified that the attention-based multiple-instance learning models could learn semi-labelled agricultural data, and thus, allow realistic early in-season predictions for farmers.

HYPERSPECTRAL IMAGE (HSI)-ASSISTED MULTI-LEVEL CROP CLASSIFICATION WITH SPINALNET-ENABLED FRACTIONAL LIGHT SPECTRUM OPTIMIZER

The accurate categorization of crop kinds with hyperspectral remote sensing imaging (HRSI) represents an imperative adaption in agriculture and is of huge emergence in estimating the yield of crops and monitoring growth. Amidst the deep models, convolutional neural networks (CNNs) tend to be the best technique for hyperspectral image (HSI) classification. However, the classical CNN poses a fixed shape, which poses simulation complexity in modeling huge ranges. To handle the problem, a two-level classification technique is developed for crop classification using HSI. The aim is to design a Spinal Net-assisted multi-level crop classification using HSI. Here, a hyperspectral satellite crop image is taken as input, and the anisotropic diffusion is used for pre-processing. U-Net is applied to segment crops. The feature mining is executed considering spectral-spatial features, and vegetation index features. The hyperspectral crop identification process is engaged with Squeeze Net, which is trained by fractional light spectrum optimizer (FLSO). In this step, if the hyperspectral crop is identified, then the multi-level crop type classification process is carried out using FLSO-based Spinal Net. The FLSO-based Spinal Net provided better accuracy of 92.2%, false positive rate (FPR) of 0.083, and true positive rate (TPR) of 91.7%.

Crop type classification and spatial mapping in River Nile and Northern State, Sudan, using Sentinel-2 satellite data and field observation

Maintaining productive farmland necessitates precise crop mapping and identification. While satellite remote sensing makes it possible to generate such maps, there are still issues to resolve, such as how to choose input data and the best classifier algorithm, especially in areas with scarce field data. Accurate assessments of the land used for farming are a crucial part of national food supply and production accounting in many African countries, and to this end, remote sensing tools are being increasingly put to use. The aim of this study was to assess the potentiality of Sentinel-2 to distinguish and discriminate crop species in the study area and constraints on accurately mapping cropping patterns in the winter season in River Nile and Northern State, Sudan. The research utilized Sentinel-2 Normalized Different Vegetation Index (NDVI) at 10 m resolution, unsupervised and supervised classification method with ground sample and accuracy assessment. The results of the study found that the signatures of grain sorghum, wheat, okra, Vicia faba, alfalfa, corn, haricot, onion, potato, tomato, lupine, tree cover, and garlic have clear distinctions, permitting an overall accuracy of 87.38%, with trees cover, onion, wheat, potato, garlic, alfalfa, tomato, lupine and Vicia faba achieving more than 87% accuracy. Major mislabeling problems occurred primarily in irrigated areas for grain sorghum, okra, corn, and haricot, in wooded areas comprised of small parcels of land. The research found that high-resolution temporal images combined with ground data had potential and utility for mapping cropland at the field scale in the winter.

Combining Sentinel 1, Sentinel 2 and MODIS data for major winter crop type classification over the Murray Darling Basin in Australia

Crop type classification is an essential task in agriculture that has been studied widely since the emergence of remote sensing technologies. Accurate crop mapping can help assist decision-making related to storage, marketing, and other production tasks. This study has designed robust crop classification models to classify two major crop types (cereals and canola) in the Murray Darling Basin (MDB) in Australia. These models combined Sentinel 1 and 2 and Moderate Resolution Imaging Spectroradiometer (MODIS) data. Three methods were applied to test classification quality: the holdout method, leave-one-season-out cross-validation (LOSOCV), and leave-one-cluster-out-cross-validation (LOCOCV). The holdout method evaluated the model's performance on data representing the entire population. The LOSOCV method was used to test the model's ability to extrapolate over time (unseen data from new seasons), and the LOCOCV method was used to test the ability to extrapolate over space (data from a new site). Crop type labelled data (n = 193 for cereals and n = 113 for canola) were extracted from high-resolution yield maps derived from grain yield monitors mounted on harvesters, and this was used to train and validate the classification models. The results showed that the holdout validation approach achieved the highest accuracy (overall and Kappa scores >0.98), but this might be caused by spatial autocorrelation of the sampling strategy implemented, which leads to an overoptimistic scenario. The overall accuracies and Kappa scores achieved from the LOSOCV method varied depending upon the difference in crop phenology from season to season, with overall accuracy ranging from 0.70 to 0.92 and Kappa scores from 0.61 to 0.90. The overall accuracies and Kappa scores for the LOCOCV ranged from 0.87 to 0.99 and 0.84 to 0.98, respectively. The gross primary productivity (GPP) and the chlorophyll red edge index (CIr) were the most important features across the three models. The outputs of this research can potentially complement models that require crop type information, such as carbon cycling and crop yield models.

Spatially Explicit Active Learning for Crop-Type Mapping from Satellite Image Time Series.

The availability of a sufficient number of annotated samples is one of the main challenges of the supervised methods used to classify crop types from remote sensing images. Creating these samples is time-consuming and costly. Active Learning (AL) offers a solution by streamlining sample annotation, resulting in more efficient training with less effort. Unfortunately, most of the developed AL methods overlook spatial information inherent in remote sensing images. We propose a novel spatially explicit AL that uses the semi-variogram to identify and discard redundant, spatially adjacent samples. It was evaluated using Random Forest (RF) and Sentinel-2 Satellite Image Time Series in two study areas from the Netherlands and Belgium. In the Netherlands, the spatially explicit AL selected 97 samples achieving an overall accuracy of 80%, compared to traditional AL selecting 169 samples with 82% overall accuracy. In Belgium, spatially explicit AL selected 223 samples and obtained 60% overall accuracy, while traditional AL selected 327 samples and obtained an overall accuracy of 63%. We concluded that the developed AL method helped RF achieve a good performance mostly for the classes consisting of individual crops with a relatively distinctive growth pattern such as sugar beets or cereals. Aggregated classes such as 'fruits and nuts' posed, however, a challenge.

Crop monitoring using remote sensing land use and land change data: Comparative analysis of deep learning methods using pre-trained CNN models

In the context of the rapidly evolving climate dynamics of the early twenty-first century, the interplay between climate change and biospheric integrity is becoming increasingly critical. The pervasive impact of climate change on ecosystems is manifested not only through alterations in average environmental conditions and their variability but also through ancillary shifts such as escalated oceanic acidification and heightened atmospheric CO2 levels. These climatic transformations are further compounded by concurrent ecological stressors, including habitat degradation, defaunation, and fragmentation. Against this backdrop, this study delves into the efficacy of advanced deep learning methodologies for the classification of land cover from satellite imagery, with a particular emphasis on agricultural crop monitoring. The study leverages state-of-the-art pre-trained Convolutional Neural Network (CNN) architectures, namely VGG16, MobileNetV2, DenseNet121, and ResNet50, selected for their architectural sophistication and proven competence in image recognition domains. The research framework encompasses a comprehensive data preparation phase incorporating augmentation techniques, a thorough exploratory data analysis to pinpoint and address class imbalances through the computation of class weights, and the strategic fine-tuning of CNN architectures with tailored classification layers to suit the specificities of land cover classification challenges. The models' performance was rigorously evaluated against benchmarks of accuracy and loss, both during the training phase and on validation datasets, with preventative strategies against overfitting, such as early stopping and adaptive learning rate modifications, being integral to the methodology. The findings illuminate the considerable potential of leveraging pre-trained deep learning models for remote sensing in agriculture, demonstrating that advanced CNN architectures, particularly DenseNet121 and ResNet50, are notably effective in enhancing crop type classification accuracy from satellite imagery. This study contributes valuable insights to the field of precision agriculture, advocating for the integration of sophisticated image recognition technologies to bolster crop monitoring efficacy, thereby enabling more nuanced agricultural decision-making and resource allocation.

Boosting crop classification by hierarchically fusing satellite, rotational, and contextual data

Accurate early-season crop type classification is crucial for the crop production estimation and monitoring of agricultural parcels. However, the complexity of the plant growth patterns and their spatio-temporal variability present significant challenges. While current deep learning-based methods show promise in crop type classification from single- and multi-modal time series, most existing methods rely on a single modality, such as satellite optical remote sensing data or crop rotation patterns. We propose a novel approach to fuse multimodal information into a model for improved accuracy and robustness across multiple crop seasons and countries. The approach relies on three modalities used: remote sensing time series from Sentinel-2 and Landsat 8 observations, parcel crop rotation and local crop distribution. To evaluate our approach, we release a new annotated dataset of 7.4 million agricultural parcels in France (FR) and the Netherlands (NL). We associate each parcel with time-series of surface reflectance (Red and NIR) and biophysical variables (LAI, FAPAR). Additionally, we propose a new approach to automatically aggregate crop types into a hierarchical class structure for meaningful model evaluation and a novel data-augmentation technique for early-season classification. Performance of the multimodal approach was assessed at different aggregation levels in the semantic domain, yielding to various ranges of the number of classes spanning from 151 to 8 crop types or groups. It resulted in accuracy ranging from 91% to 95% for the NL dataset and from 85% to 89% for the FR dataset. Pre-training on a dataset improves transferability between countries, allowing for cross- domain and label prediction, and robustness of the performances in a few-shot setting from FR to NL, i.e., when the domain changes as per with significantly new labels. Our proposed approach outperforms comparable methods by enabling deep learning methods to use the often overlooked spatio-temporal context of parcels, resulting in increased precision and generalization capacity.

Cropland and Crop Type Classification with Sentinel-1 and Sentinel-2 Time Series Using Google Earth Engine for Agricultural Monitoring in Ethiopia

Cropland monitoring is important for ensuring food security in the context of global climate change and population growth. Freely available satellite data allow for the monitoring of large areas, while cloud-processing platforms enable a wide user community to apply remote sensing techniques. Remote sensing-based estimates of cropped area and crop types can thus assist sustainable land management in developing countries such as Ethiopia. In this study, we developed a method for cropland and crop type classification based on Sentinel-1 and Sentinel-2 time-series data using Google Earth Engine. Field data on 18 different crop types from three study areas in Ethiopia were available as reference for the years 2021 and 2022. First, a land use/land cover classification was performed to identify cropland areas. We then evaluated different input parameters derived from Sentinel-2 and Sentinel-1, and combinations thereof, for crop type classification. We assessed the accuracy and robustness of 33 supervised random forest models for classifying crop types for three study areas and two years. Our results showed that classification accuracies were highest when Sentinel-2 spectral bands were included. The addition of Sentinel-1 parameters only slightly improved the accuracy compared to Sentinel-2 parameters alone. The variant including S2 bands, EVI2, and NDRe2 from Sentinel-2 and VV, VH, and Diff from Sentinel-1 was finally applied for crop type classification. Investigation results of class-specific accuracies reinforced the importance of sufficient reference sample availability. The developed methods and classification results can assist regional experts in Ethiopia to support agricultural monitoring and land management.

Fine classification of crops based on an inductive transfer learning method with compact polarimetric SAR images

ABSTRACT Compact polarimetric synthetic aperture radar (CP SAR) reduces fully polarimetric SAR system complexity and expands the imaging swath. Generally, fine classification of crop types relies on many labeled training samples. However, due to the temporal interval of crop phenology and ground environment variations over time, training samples from one dataset usually perform poorly for another. Therefore, in this study, transfer learning is introduced to crop classification to ensure classification accuracy by improving reusability of training samples. A stable and robust inductive transfer learning method, i.e. the Transfer Bagging-based Ensemble Learning (TBEL) algorithm, is proposed. The main idea is to select an adequate number of representative samples from unlabeled datasets to characterize each class in the target domain based on limited labeled samples and construct a classifier set to classify the target domain. This study investigates CP SAR data performance in transfer learning for crop classification. The proposed algorithm in the experimental study is compared with six typical methods (Subspace Alignment (SA), CORrelation ALignment (CORAL), Joint Distribution Adaptation (JDA), Balanced Distribution Adaptation (BDA), Transfer Bagging (TrBagg), and Bagging-based Ensemble Transfer Learning (BETL)). The experimental results show that the crop classification accuracy based on the TBEL algorithm is more stable, with an improved overall classification accuracy of 2–6%. Classifying the same rice harvest stage in the cross-year domain has the highest overall accuracy of 92.2%. Wheat fields in different scenes are also classified. Based on the TBEL algorithm, the overall classification accuracy improves by 1–10% compared with typical methods, with an accuracy of at least 87.6%. Furthermore, by testing the CP mode classification performance over various crops in transfer learning, we find that the circular CP mode performs better than the linear mode in most cases. This conclusion agrees with single-scene applications and was first verified in transfer learning.

Revolutionizing Unmanned Aerial Vehicle Imagery Classification: A Deep Learning Approach Empowered by Computer Vision

Recently, computer vision, unmanned aerial vehicles (UAV) based remote sensing (RS) and deep learning (DL) technologies have been instrumental in global food productivity and future agriculture. UAV provides several advantages over other possible RS platforms like real-time data acquisition, high flexibility, and the best tradeoff between spatial, low cost, small size, spectral, and temporal resolution. One possible advantage of using UAVs for crop classification is that they can efficiently and quickly cover large areas, and could gather data from different angles and at different times. This might assist in providing detailed knowledge of the crops and their conditions. Earlier research is limited to finding a single crop from the RGB images taken by the UAV and hasn’t explored the possibility of multi-crop classification by carrying out DL algorithms. Thus, this study presents a new Automated Crop Type Classification using Adaptive African Vulture Optimization with Deep Learning (ACCT-AAVODL) technique. The ACCT-AAVODL algorithm aims to investigate the UAV images and determine different types of food crops. To accomplish this, the presented ACCT-AAVODL method uses a densely connected network (DenseNet121) for generating feature vectors. Since the trial and error hyper parameter tuning is a challenging task, the AAVO model is employed for hyper parameter optimization. The ACCT-AAVODL technique involves a sparse auto encoder (SAE) with a Nadam optimizer for crop type classification, the stimulation analysis of the ACCT-AAVODL approach on the drone imagery dataset shows the remarkable performance of the ACCT-AAVODL method over other approaches.