Multi-scale Feature Fusion Method Research Articles

Scene classification for remote sensing image of land use and land cover using dual-model architecture with multilevel feature fusion

ABSTRACT Scene classification for remote sensing image (RSI) of land use and land cover (LULC) involves identifying discriminative features of interest in different classes. Spurred by the powerful feature extraction capability of Convolutional Neural Networks (CNNs), LULC classification for RSI has rapidly developed in recent years. Although multi-models have better classification performance than single-models, combining multi-models is still the key to maximizing classification accuracy. Thus, this paper proposes a dual-model architecture with multilevel feature fusion called XE-Net. Specifically, high, middle, and low-level features are extracted by the Xception and EfficientNet-V2, respectively, and through transfer learning, the model’s weighting parameters from the ImageNet data are shared. Moreover, the designed sibling feature fusion algorithm fuses the triple-level features extracted from the dual models sequentially according to the same level. Besides, the proposed multi-scale feature fusion method systematically enhances the fused three-scale features to improve the discriminative feature. Finally, the discriminative feature is input into the classifier to obtain the classification results. The maximum average overall accuracy obtained from sufficient experiments using XE-Net on the RSSCN-7 dataset is 96.84%, while WHU-19, UCM-21, OPTIMAL-31, NWPU-RESISC45, and AID attain 99.58%, 99.37%, 97.07%, 95.03%, and 95.78%, respectively, demonstrating our model’s superiority.

A Serial Multi-Scale Feature Fusion and Enhancement Network for Amur Tiger Re-Identification.

The Amur tiger is an important endangered species in the world, and its re-identification (re-ID) plays an important role in regional biodiversity assessment and wildlife resource statistics. This paper focuses on the task of Amur tiger re-ID based on visible light images from screenshots of surveillance videos or camera traps, aiming to solve the problem of low accuracy caused by camera perspective, noisy background noise, changes in motion posture, and deformation of Amur tiger body patterns during the re-ID process. To overcome this challenge, we propose a serial multi-scale feature fusion and enhancement re-ID network of Amur tiger for this task, in which global and local branches are constructed. Specifically, we design a global inverted pyramid multi-scale feature fusion method in the global branch to effectively fuse multi-scale global features and achieve high-level, fine-grained, and deep semantic feature preservation. We also design a local dual-domain attention feature enhancement method in the local branch, further enhancing local feature extraction and fusion by dividing local feature blocks. Based on the above model structure, we evaluated the effectiveness and feasibility of the model on the public dataset of the Amur Tiger Re-identification in the Wild (ATRW), and achieved good results on mAP, Rank-1, and Rank-5, demonstrating a certain competitiveness. In addition, since our proposed model does not require the introduction of additional expensive annotation information and does not incorporate other pre-training modules, it has important advantages such as strong transferability and simple training.

A lightweight multi-feature fusion network for unmanned aerial vehicle infrared ray image object detection

UAV (Unmanned Aerial Vehicle) infrared object detection is crucial in pedestrian monitoring and traffic dispatch, which detects and locates objects in infrared images. In light of issues such as unnoticeable texture features and limited resolution of infrared image objects, a lightweight multi-scale feature fusion method for UAV infrared object detection is presented to enhance the performance of UAVs carrying intelligent devices to detect infrared objects. By changing the anchorless frame strategy of the YOLOX method, a lightweight Multi-Feature Fusion Network (MFFNet) for UAV infrared ray (IR) image object detection is proposed. First, a lightweight backbone network is built using ShuffleNetv2_block, spatial pyramid pooling, and other modules to reduce the network's number of parameters and inference time while maintaining its capacity to extract features. Second, we develop a multi-feature fusion module to improve the detection capabilities of the model for IR objects by fusing the local features and the overall characteristics of IR objects since the texture features of IR objects are challenging to employ, but the boundary information is evident. The boundary frame regression loss is then optimized using SCYLLA-IoU (SIoU) by comparing the predicted frame to the actual frame in terms of angle, distance, shape, and IoU (Intersection over Union), which forces the model to reach the optimum predicted box more quickly. The experimental results demonstrate that our method achieves an 81.5% mean average precision (mAP) with 4.21M parameters and an inference time of only 4.84ms per image, outperforming most networks in speed and accuracy.

Object Detection in Remote Sensing Images Based on Adaptive Multi-Scale Feature Fusion Method

Multi-scale object detection is critical for analyzing remote sensing images. Traditional feature pyramid networks, which are aimed at accommodating objects of varying sizes through multi-level feature extraction, face significant challenges due to the diverse scale variations present in remote sensing images. This situation often forces single-level features to span a broad spectrum of object sizes, complicating accurate localization and classification. To tackle these challenges, this paper proposes an innovative algorithm that incorporates an adaptive multi-scale feature enhancement and fusion module (ASEM), which enhances remote sensing image object detection through sophisticated multi-scale feature fusion. Our method begins by employing a feature pyramid to gather coarse multi-scale features. Subsequently, it integrates a fine-grained feature extraction module at each level, utilizing atrous convolutions with varied dilation rates to refine multi-scale features, which markedly improves the information capture from widely varied object scales. Furthermore, an adaptive enhancement module is applied to the features of each level by employing an attention mechanism for feature fusion. This strategy concentrates on the features of critical scale, which significantly enhance the effectiveness of capturing essential feature information. Compared with the baseline method, namely, Rotated FasterRCNN, our method achieved an mAP of 74.21% ( 0.81%) on the DOTA-v1.0 dataset and an mAP of 84.90% (+9.2%) on the HRSC2016 dataset. These results validated the effectiveness and practicality of our method and demonstrated its significant application value in multi-scale remote sensing object detection tasks.

Research on Face Attribute Recognition Technology Based on Fine-Grained Features

Abstract This study explores fine-grained feature-based face attribute recognition techniques to enhance the accuracy of face recognition in low-resolution and complex environments. The article proposes a global feature extraction method and a local texture feature extraction method to extract global and regional features by enhancing feature reuse and information flow through dense connectivity and ShuffleNet V2 framework. Then, a multiscale feature exchange method is used to fuse different scale features to enhance the capture of detail information. Finally, efficient feature integration is achieved by the multiscale feature fusion method. Experimental results on the CK+ and FER2013 datasets show that the accuracy of this method on face expression recognition reaches 97.24% and 95.93%, respectively, and the average recognition accuracy in the face attribute recognition experiments on the CelebA dataset is 97.11%, which is significantly better than the comparison algorithm. In addition, the analysis of the recognition effect on low-resolution faces shows that this paper’s method achieves a recognition accuracy of 54.03% at a resolution of 15 × 15 and a high accuracy of over 99% at resolutions of 70 × 70 and above. These results show that the face attribute recognition technique based on fine-grained features proposed in this paper significantly improves recognition accuracy.

PVNet: A Used Vehicle Pedestrian Detection Tracking and Counting Method

Advances in technology have made people’s lives more prosperous. However, the increase in the number of cars and the emergence of autonomous driving technology have led to frequent road accidents. Manual observation of traffic conditions requires high labor intensity, low work efficiency, and poses safety risks. The paper proposes a deep learning-based pedestrian-vehicle detection model to replace manual observation, overcoming human resource limitations and safety concerns. The model optimizes the darknet53 backbone feature extraction network, reducing parameters and improving feature extraction capabilities, making it more suitable for pedestrian-vehicle scenarios. In addition, the PVFPN multi-scale feature fusion method is used to facilitate information exchange between different feature layers. Finally, the Bytetrack method is used for target counting and tracking. The paper model shows excellent performance in pedestrian-vehicle detection and tracking in traffic scenarios. The experimental results show that the improved model achieves a mAP@.5 of 0.952 with only 32% of the parameters compared to YOLOv8s. Furthermore, the proposed PVNet model, combined with the Bytetrack method, maintains high detection accuracy and is applicable to pedestrian-vehicle detection and tracking in traffic scenarios. In summary, this section discusses the traffic issues arising from technological development and presents the optimization and performance of the deep learning-based pedestrian-vehicle detection model, along with its potential applications in traffic scenarios.

A spine segmentation method based on scene aware fusion network

BackgroundIntervertebral disc herniation, degenerative lumbar spinal stenosis, and other lumbar spine diseases can occur across most age groups. MRI examination is the most commonly used detection method for lumbar spine lesions with its good soft tissue image resolution. However, the diagnosis accuracy is highly dependent on the experience of the diagnostician, leading to subjective errors caused by diagnosticians or differences in diagnostic criteria for multi-center studies in different hospitals, and inefficient diagnosis. These factors necessitate the standardized interpretation and automated classification of lumbar spine MRI to achieve objective consistency. In this research, a deep learning network based on SAFNet is proposed to solve the above challenges.MethodsIn this research, low-level features, mid-level features, and high-level features of spine MRI are extracted. ASPP is used to process the high-level features. The multi-scale feature fusion method is used to increase the scene perception ability of the low-level features and mid-level features. The high-level features are further processed using global adaptive pooling and Sigmoid function to obtain new high-level features. The processed high-level features are then point-multiplied with the mid-level features and low-level features to obtain new high-level features. The new high-level features, low-level features, and mid-level features are all sampled to the same size and concatenated in the channel dimension to output the final result.ResultsThe DSC of SAFNet for segmenting 17 vertebral structures among 5 folds are 79.46 ± 4.63%, 78.82 ± 7.97%, 81.32 ± 3.45%, 80.56 ± 5.47%, and 80.83 ± 3.48%, with an average DSC of 80.32 ± 5.00%. The average DSC was 80.32 ± 5.00%. Compared to existing methods, our SAFNet provides better segmentation results and has important implications for the diagnosis of spinal and lumbar diseases.ConclusionsThis research proposes SAFNet, a highly accurate and robust spine segmentation deep learning network capable of providing effective anatomical segmentation for diagnostic purposes. The results demonstrate the effectiveness of the proposed method and its potential for improving radiological diagnosis accuracy.

Fittings Detection Method Based on Multi-Scale Geometric Transformation and Attention-Masking Mechanism

Overhead transmission lines are important lifelines in power systems, and the research and application of their intelligent patrol technology is one of the key technologies for building smart grids. The main reason for the low detection performance of fittings is the wide range of some fittings’ scale and large geometric changes. In this paper, we propose a fittings detection method based on multi-scale geometric transformation and attention-masking mechanism. Firstly, we design a multi-view geometric transformation enhancement strategy, which models geometric transformation as a combination of multiple homomorphic images to obtain image features from multiple views. Then, we introduce an efficient multiscale feature fusion method to improve the detection performance of the model for targets with different scales. Finally, we introduce an attention-masking mechanism to reduce the computational burden of model-learning multiscale features, thereby further improving model performance. In this paper, experiments have been conducted on different datasets, and the experimental results show that the proposed method greatly improves the detection accuracy of transmission line fittings.

Hepatocellular carcinoma segmentation and pathological differentiation degree prediction method based on multi-task learning

Hepatocellular carcinoma (HCC) is the most common liver malignancy, where HCC segmentation and prediction of the degree of pathological differentiation are two important tasks in surgical treatment and prognosis evaluation. Existing methods usually solve these two problems independently without considering the correlation of the two tasks. In this paper, we propose a multi-task learning model that aims to accomplish the segmentation task and classification task simultaneously. The model consists of a segmentation subnet and a classification subnet. A multi-scale feature fusion method is proposed in the classification subnet to improve the classification accuracy, and a boundary-aware attention is designed in the segmentation subnet to solve the problem of tumor over-segmentation. A dynamic weighted average multi-task loss is used to make the model achieve optimal performance in both tasks simultaneously. The experimental results of this method on 295 HCC patients are superior to other multi-task learning methods, with a Dice similarity coefficient (Dice) of (83.9 ± 0.88)% on the segmentation task, while the average recall is (86.08 ± 0.83)% and an F1 score is (80.05 ± 1.7)% on the classification task. The results show that the multi-task learning method proposed in this paper can perform the classification task and segmentation task well at the same time, which can provide theoretical reference for clinical diagnosis and treatment of HCC patients.

Multiscale feature fusion method for lane line detection based on time series

In order to solve lane line detection in difficult traffic conditions, such as shadow occlusion, signpost degradation, curves, and tunnels, numerous models have been proposed. However, most of the existing models conduct independent single-frame image detection, which makes it difficult to utilize the continuity of driving images and is ineffective in challenging scenes. To this end, we suggest a spatiotemporal information processing model for lane line recognition that enhances critical features. In order to properly learn the correlation between continuous images, we first employ a convolutional gated recurrent unit to process spatiotemporal driving information on the basis of U-Net. Second, the pyramid split attention (PSA) module is used to enhance or suppress the obtained feature expressions. Finally, the skip connection is used to fuse the features of different scales encoded by each stage with the features processed by PSA and gradually restore to the original image size. Experiments on the TuSimple dataset demonstrate that our model outperforms representative lane line detection networks in challenging driving scenes, with an F1-measure of up to 94.302%.

BreastMultiNet: A multi-scale feature fusion method using deep neural network to detect breast cancer

Breast cancer is predominantly seen in women and is the leading cause of death in females worldwide. Diagnosis of breast cancer using biopsy tissue images is expensive, time-intensive, and fraught with conflicts among doctors. Pathologists can now diagnose breast cancer more consistently and promptly because of advances in the Computer-Aided Diagnosis (CAD) system. As a result, there has been a surge in demand for CAD-based machine learning techniques. This study describes a “BreastMultiNet” framework that focuses on the transfer learning concept for identifying distinct types of breast cancer by utilizing two publicly available datasets. The suggested “BreastMultiNet” architecture allows rapid and comprehensive breast cancer diagnosis. The suggested scheme extracts features from microscope images with the help of well-known conventional and deep learning models such as HOG, LBP, SURP, DenseNet201, and VGG19. Comparatively, transfer learning models provide good accuracy than conventional models. The collected properties of transfer learning models are subsequently dispatched into the summing layer, resulting in a fused vector. The proposed framework achieves 99% and 95% classification accuracy on both BreakHis and ICIAR dataset respectively, outperforming all the other state of the art techniques. In terms of accuracy, the “BreastMultiNet” framework may be employed as a modeling approach in hospitals and medical care contexts.

CBAM + ASFF-YOLOXs: An improved YOLOXs for guiding agronomic operation based on the identification of key growth stages of lettuce

To the optimal time to conduct farming operations in the traditional agricultural production process mainly depends on human observation and planting experience, which is time-consuming and laborious, and makes it easy to miss the best agricultural operation opportunities. In this study, our main objective is to accurately detect the key growth stages of lettuce to guide the timely implementation of corresponding agricultural operations. Firstly, the dataset was collected for the growth stage with important agricultural operations in the growth process of multi-variety lettuce, to lay the data foundation for the construction of the model. Secondly, considering the difference in plant growth, we compared many methods and selected the optimal modeling method YOLOXs to identify the key growth stages of multi-variety lettuce (mAP = 98.75 %). Finally, to ensure the applicability of the detection model in complex agricultural scenes, we tried to improve the effect of YOLOXs by three attention mechanisms and one multi-scale feature fusion method, and proposed a new method CBAM + ASFF-YOLOXs (mAP = 99.04 %). The results showed that this method is expected to replace human eye observation and experience in planting, to provide accurate technical feedback on relevant agricultural operation time, and to provide technical support for the unmanned operation of agriculture. At the same time, the limitations, challenges, and prospects of this method are discussed.

Cross attention guided multi-scale feature fusion for false-positive reduction in pulmonary nodule detection

False-positive reduction is a crucial step of computer-aided diagnosis (CAD) system for pulmonary nodules detection and it plays an important role in lung cancer diagnosis. In this paper, we propose a novel cross attention guided multi-scale feature fusion method for false-positive reduction in pulmonary nodule detection. Specifically, a 3D SENet50 fed with a candidate nodule cube is applied as the backbone to acquire multi-scale coarse features. Then, the coarse features are refined and fused by the multi-scale fusion part to achieve a better feature extraction result. Finally, a 3D spatial pyramid pooling module is used to enhance receptive field and a distributed aligned linear classifier is applied to get the confidence score. In addition, each of the five nodule cubes with different sizes centering on every testing nodule position is fed into the proposed framework to obtain a confidence score separately and a weighted fusion method is used to improve the generalization performance of the model. Extensive experiments are conducted to demonstrate the effectiveness of the classification performance of the proposed model. The data used in our work is from the LUNA16 pulmonary nodule detection challenge. In this data set, the number of true-positive pulmonary nodules is 1,557, while the number of false-positive ones is 753,418. The new method is evaluated on the LUNA16 dataset and achieves the score of the competitive performance metric (CPM) 84.8%.

Traffic Sign Detection Based on Lightweight Multiscale Feature Fusion Network

Traffic sign detection is a research hotspot in advanced assisted driving systems, given the complex background, light transformation, and scale changes of traffic sign targets, as well as the problems of slow result acquisition and low accuracy of existing detection methods. To solve the above problems, this paper proposes a traffic sign detection method based on a lightweight multiscale feature fusion network. Since a lightweight network model is simple and has fewer parameters, it can greatly improve the detection speed of a target. To learn more target features and improve the generalization ability of the model, a multiscale feature fusion method can be used to improve recognition accuracy during training. Firstly, MobileNetV3 was selected as the backbone network, a new spatial attention mechanism was introduced, and a spatial attention branch and a channel attention branch were constructed to obtain a mixed attention weight map. Secondly, a feature-interleaving module was constructed to convert the single-scale feature map of the specified layer into a multiscale feature fusion map to realize the combined encoding of high-level semantic information and low-level semantic information. Then, a feature extraction base network for lightweight multiscale feature fusion with an attention mechanism based on the above steps was constructed. Finally, a key-point detection network was constructed to output the location information, bias information, and category probability of the center points of traffic signs to achieve the detection and recognition of traffic signs. The model was trained, validated, and tested using TT100K datasets, and the detection accuracy of 36 common categories of traffic signs reached more than 85%, among which the detection accuracy of five categories exceeded 95%. The results showed that, compared with the traditional methods of Faster R-CNN, CornerNet, and CenterNet, traffic sign detection based on a lightweight multiscale feature fusion network had obvious advantages in the speed and accuracy of recognition, significantly improved the detection performance for small targets, and achieved a better real-time performance.

Multiscale feature fusion network for automatic port segmentation from remote sensing images

In recent years, remote sensing image observation technology has developed rapidly. Extracting coastlines from remote sensing images has become an indispensable means of port area measurement. Port segmentation from remote sensing images is an important method of coastline extraction and measurement. The remote sensing images used are panchromatic remote sensing images. Due to complex remote sensing image scenes and the large difference in feature information at different scales, traditional segmentation methods cannot perform effective extraction, and it is difficult to accurately segment the coastline in remote sensing images. We propose a multiscale feature fusion network for automatic port segmentation from remote sensing images. First, to reduce the redundant parameters and complex operation problems in traditional convolutional neural networks, we propose using MobileNetv2 as the base network for feature extraction to achieve a lightweight model. Then aiming at the feature differences of remote sensing images at different scales, we present atrous convolution as a convolution method for the entire network and combine a multiscale feature fusion method to extract the features of remote sensing images and improve the feature extraction ability. To reduce the problem of ships calling at the port being easily mistaken for port area and causing false segmentation, we propose a method of eliminating the ship area to reduce the interference. Finally, the comprehensive evaluation of a large number of Google port remote sensing data shows that compared with existing methods, the proposed method has the characteristics of being lightweight and having high precision.

Research on Waste Plastics Classification Method Based on Multi-Scale Feature Fusion.

Microplastic particles produced by non-degradable waste plastic bottles have a critical impact on the environment. Reasonable recycling is a premise that protects the environment and improves economic benefits. In this paper, a multi-scale feature fusion method for RGB and hyperspectral images based on Segmenting Objects by Locations (RHFF-SOLOv1) is proposed, which uses multi-sensor fusion technology to improve the accuracy of identifying transparent polyethylene terephthalate (PET) bottles, blue PET bottles, and transparent polypropylene (PP) bottles on a black conveyor belt. A line-scan camera and near-infrared (NIR) hyperspectral camera covering the spectral range from 935.9 nm to 1722.5 nm are used to obtain RGB and hyperspectral images synchronously. Moreover, we propose a hyperspectral feature band selection method that effectively reduces the dimensionality and selects the bands from 1087.6 nm to 1285.1 nm as the features of the hyperspectral image. The results show that the proposed fusion method improves the accuracy of plastic bottle classification compared with the SOLOv1 method, and the overall accuracy is 95.55%. Finally, compared with other space-spectral fusion methods, RHFF-SOLOv1 is superior to most of them and achieves the best (97.5%) accuracy in blue bottle classification.

Muti-Frame Point Cloud Feature Fusion Based on Attention Mechanisms for 3D Object Detection.

Continuous frames of point-cloud-based object detection is a new research direction. Currently, most research studies fuse multi-frame point clouds using concatenation-based methods. The method aligns different frames by using information on GPS, IMU, etc. However, this fusion method can only align static objects and not moving objects. In this paper, we proposed a non-local-based multi-scale feature fusion method, which can handle both moving and static objects without GPS- and IMU-based registrations. Considering that non-local methods are resource-consuming, we proposed a novel simplified non-local block based on the sparsity of the point cloud. By filtering out empty units, memory consumption decreased by 99.93%. In addition, triple attention is adopted to enhance the key information on the object and suppresses background noise, further benefiting non-local-based feature fusion methods. Finally, we verify the method based on PointPillars and CenterPoint. Experimental results show that the mAP of the proposed method improved by 3.9% and 4.1% in mAP compared with concatenation-based fusion modules, PointPillars-2 and CenterPoint-2, respectively. In addition, the proposed network outperforms powerful 3D-VID by 1.2% in mAP.

DeepFusion: A deep learning based multi-scale feature fusion method for predicting drug-target interactions.

Predicting drug-target interactions (DTIs) is essential for both drug discovery and drug repositioning. Recently, deep learning methods have achieved relatively significant performance in predicting DTIs. Generally, it needs a large amount of approved data of DTIs to train the model, which is actually tedious to obtain. In this work, we propose DeepFusion, a deep learning based multi-scale feature fusion method for predicting DTIs. To be specific, we generate global structural similarity feature based on similarity theory, convolutional neural network and generate local chemical sub-structure semantic feature using transformer network respectively for both drug and protein. Data experiments are conducted on four sub-datasets of BIOSNAP, which are 100%, 70%, 50% and 30% of BIOSNAP dataset. Particularly, using 70% sub-dataset, DeepFusion achieves ROC-AUC and PR-AUC by 0.877 and 0.888, which is close to the performance of some baseline methods trained by the whole dataset. In case study, DeepFusion achieves promising prediction results on predicting potential DTIs in case study.

Dynamic Multiscale Feature Fusion Method for Underwater Target Recognition

The feature information of small-scale targets is seriously missing under the interference of complex underwater terrain and light refraction. Moreover, the unbalanced distribution of underwater target samples can also affect the accuracy of spatial semantic feature extraction. Aiming at the above problems, this paper proposes a dynamic multiscale feature fusion method for underwater target recognition. Firstly, this paper uses multiscale info noise contrastive estimation (MS-InfoNCE) loss to extract the significant features of the target at 4 scales. Secondly, the method learns the spatial semantic features of the target through a dynamic conditional probability matrix. Finally, this paper designs different feature fusion mechanisms for different scale targets, dynamically fusing multiscale significant features and spatial semantic features to recognize underwater weak targets. The experimental results show that the recognition accuracy of the proposed algorithm is 1.38% higher than that of the existing algorithm when recognizing underwater distorted targets.

Mapping the Corn Residue-Covered Types Using Multi-Scale Feature Fusion and Supervised Learning Method by Chinese GF-2 PMS Image.

The management of crop residue covering is a vital part of conservation tillage, which protects black soil by reducing soil erosion and increasing soil organic carbon. Accurate and rapid classification of corn residue-covered types is significant for monitoring crop residue management. The remote sensing technology using high spatial resolution images is an effective means to classify the crop residue-covered areas quickly and objectively in the regional area. Unfortunately, the classification of crop residue-covered area is tricky because there is intra-object heterogeneity, as a two-edged sword of high resolution, and spectral confusion resulting from different straw mulching ways. Therefore, this study focuses on exploring the multi-scale feature fusion method and classification method to classify the corn residue-covered areas effectively and accurately using Chinese high-resolution GF-2 PMS images in the regional area. First, the multi-scale image features are built by compressing pixel domain details with the wavelet and principal component analysis (PCA), which has been verified to effectively alleviate intra-object heterogeneity of corn residue-covered areas on GF-2 PMS images. Second, the optimal image dataset (OID) is identified by comparing model accuracy based on the fusion of different features. Third, the 1D-CNN_CA method is proposed by combining one-dimensional convolutional neural networks (1D-CNN) and attention mechanisms, which are used to classify corn residue-covered areas based on the OID. Comparison of the naive Bayesian (NB), random forest (RF), support vector machine (SVM), and 1D-CNN methods indicate that the residue-covered areas can be classified effectively using the 1D-CNN-CA method with the highest accuracy (Kappa: 96.92% and overall accuracy (OA): 97.26%). Finally, the most appropriate machine learning model and the connected domain calibration method are combined to improve the visualization, which are further used to classify the corn residue-covered areas into three covering types. In addition, the study showed the superiority of multi-scale image features by comparing the contribution of the different image features in the classification of corn residue-covered areas.