Image Texture Research Articles

Techniques for using backlighting to enhance the expressiveness of photographs

This article examines the historical and contemporary aspects of backlighting in photography, focusing on its visual and psychological impact and the technical and color features that enhance its effectiveness. Drawing on principles established during the Baroque era by artists like Caravaggio and Rembrandt, the study explores how modern photographers integrate these techniques with advanced lighting technologies and color theories, particularly those of Johannes Itten. Through a comprehensive analysis of backlighting applications in portrait and food photography, the research highlights how this technique enhances depth, texture, and emotional resonance in images. The findings demonstrate that mastery of backlighting, combined with technical proficiency and an understanding of color interactions, allows photographers to create compelling and emotionally impactful works. The novelty of this work lies in its synthesis of historical practices with modern technological advancements, offering new insights into the creative possibilities of backlighting in professional photography.

Cardiac amyloidosis: a texture issue. a proof of concept study on quantification of granular sparkling in patients with transthyretin-related cardiac amyloidosis using radiomics and granulometry

Abstract Background Granular sparkling is a well-known echocardiographic feature found in patients with transthyretin-related cardiac amyloidosis (ATTR-CA). However, there is no objective technique for quantifying this feature, which therefore remains a qualitative, elusive and ultimately unreliable imaging characteristic. Recent evidence suggests that radiomics and, in particular, ultrasonomics could play an important role in the non-invasive tissue characterization of cardiomyopathies through the study of specific myocardial texture features. Purpose The aim of this study is to statistically and geometrically characterize granular sparkling as a volume-independent texture property of the myocardium in patients with ATTR-CA. Methods We retrospectively collected echocardiogram video-clips in parasternal long axis (PLAX) and 4-chamber (4CH) views of 229 patients with ATTR-CA, and 224 age- and gender-matched hypertensive patients without any known cardiac disease. From each video-clip, one end-diastole frame was extracted and annotated by an expert to identify a region-of-interest (ROI) within the interventricular septum. Left ventricle chamber masks were also extracted, and used as a brightness reference to enforce invariance w.r.t. the settings of the specific ultrasound system (Fig. 1). Because many established radiomic textural features are heavily volume-confounded, we analyzed the ROI texture by extracting a subset of volume-invariant radiomic features (i.e. ROI-independent), as well as morphological granulometry features. We then fitted a logistic regression classifier to discriminate between ATTR-CA and controls based on the computed textural features. Results 94 radiomic features were identified, of which 73 were volume-invariant. The texture-based classifier predicted the diagnosis with a cross-validated accuracy of 91%, a specificity of 90% and a sensitivity of 92% by utilizing 73 volume-invariant radiomic features on both PLAX and 4CH views (Fig. 2). The addition of granulometry and the further addition of 11 volume-confounded features and 9 shape features to the model did not significantly enhance its discriminative performance (Fig. 2). The top 10 volume-invariant discriminative features were largely consistent in PLAX and 4CH views (Fig. 2), with variables descriptive of greater heterogeneity (such as glszm_ZoneEntropy and glszm_RunEnNtropy) and a higher spatial rate of change (such as ngtdm_Complexity) in the texture of ATTR-CA frames as compared to control frames. Conclusions Our results confirm the ability of radiomics in detecting subtle differences in the myocardial texture of echocardiographic images between patients with ATTR-CA and hypertensive controls. High levels of accuracy are obtained with a purely texture-based, volume-independent set of features. These findings suggest that the diagnosis of ATTR-CA could be simplified and made earlier using a statistical model based on the extraction of radiomic features.Figure 1Figure 2

Comparison of image quality and lesion conspicuity between conventional and deep learning reconstruction in gadoxetic acid-enhanced liver MRI

ObjectiveTo compare the image quality and lesion conspicuity of conventional vs deep learning (DL)-based reconstructed three-dimensional T1-weighted images in gadoxetic acid-enhanced liver magnetic resonance imaging (MRI).MethodsThis prospective study (NCT05182099) enrolled participants scheduled for gadoxetic acid-enhanced liver MRI due to suspected focal liver lesions (FLLs) who provided signed informed consent. A liver MRI was conducted using a 3-T scanner. T1-weighted images were reconstructed using both conventional and DL-based (AIRTM Recon DL 3D) reconstruction algorithms. Three radiologists independently reviewed the image quality and lesion conspicuity on a 5-point scale.ResultsFifty participants (male = 36, mean age 62 ± 11 years) were included for image analysis. The DL-based reconstruction showed significantly higher image quality than conventional images in all phases (3.71–4.40 vs 3.37–3.99, p < 0.001 for all), as well as significantly less noise and ringing artifacts than conventional images (p < 0.05 for all), while also showing significantly altered image texture (p < 0.001 for all). Lesion conspicuity was significantly higher in DL-reconstructed images than in conventional images in the arterial phase (2.15 [95% confidence interval: 1.78, 2.52] vs 2.03 [1.65, 2.40], p = 0.036), but no significant difference was observed in the portal venous phase and hepatobiliary phase (p > 0.05 for all). There was no significant difference in the figure-of-merit (0.728 in DL vs 0.709 in conventional image, p = 0.474).ConclusionDL reconstruction provided higher-quality three-dimensional T1-weighted imaging than conventional reconstruction in gadoxetic acid-enhanced liver MRI.Critical relevance statementDL reconstruction of 3D T1-weighted images improves image quality and arterial phase lesion conspicuity in gadoxetic acid-enhanced liver MRI compared to conventional reconstruction.Key PointsDL reconstruction is feasible for 3D T1-weighted images across different spatial resolutions and phases.DL reconstruction showed superior image quality with reduced noise and ringing artifacts.Hepatic anatomic structures were more conspicuous on DL-reconstructed images.Graphical

Non-Destructive Monitoring of Sweet Pepper Samples After Selected Periods of Lacto-Fermentation

Fermented food is characterized by positive health-promoting properties. The objective of this study was to distinguish and assess the changes in the flesh structure of sweet bell pepper samples after specific periods of fermentation in a non-destructive manner. Two cultivars of pepper, red and yellow, were subjected to lacto-fermentation. The experiments lasted 56 days and the samples were taken for analysis at the beginning of the study (0 days) and after 3, 7, 10, 14, 21, 28, and 56 days. The fermentation process was monitored based on image features, which were used to develop machine learning models distinguishing samples before and after various periods of lacto-fermentation (0, 3, 7, 10, 14, 21, 28, and 56 days). The average accuracy of the classification of red bell pepper samples was up to 93% for the model built using IBk (Lazy group). The yellow bell pepper samples were distinguished up to 90% accuracy by the LMT algorithm (Trees group). The performed study allowed us to determine the changes in pepper flesh in terms of image textures during lacto-fermentation.

Research on Target Image Classification in Low-Light Night Vision.

In extremely dark conditions, low-light imaging may offer spectators a rich visual experience, which is important for both military and civic applications. However, the images taken in ultra-micro light environments usually have inherent defects such as extremely low brightness and contrast, a high noise level, and serious loss of scene details and colors, which leads to great challenges in the research of low-light image and object detection and classification. The low-light night vision image used as the study object in this work has an excessively dim overall picture and very little information about the screen's features. Three algorithms, HE, AHE, and CLAHE, were used to enhance and highlight the image. The effectiveness of these image enhancement methods is evaluated using metrics such as the peak signal-to-noise ratio and mean square error, and CLAHE was selected after comparison. The target image includes vehicles, people, license plates, and objects. The gray-level co-occurrence matrix (GLCM) was used to extract the texture features of the enhanced images, and the extracted image texture features were used as input to construct a backpropagation (BP) neural network classification model. Then, low-light image classification models were developed based on VGG16 and ResNet50 convolutional neural networks combined with low-light image enhancement algorithms. The experimental results show that the overall classification accuracy of the VGG16 convolutional neural network model is 92.1%. Compared with the BP and ResNet50 neural network models, the classification accuracy was increased by 4.5% and 2.3%, respectively, demonstrating its effectiveness in classifying low-light night vision targets.

Need of Feature Extraction in Coconut Tree Disease Detection: A Review

Agriculture has served as the primary means of sustenance for mankind for thousands of years. Even in the present day, it continues to support approximately 50% of the global population. Plant diseases pose a significant threat to crop production, resulting in substantial losses each year on a global scale. To mitigate the financial impact of these diseases, it is crucial to maintain the health of plants throughout their growth and development. The symptoms of infections are predominantly visible on plant leaves, making them a common indicator for disease detection and identification. However, visually observing and identifying diseases is a challenging task that requires extensive human expertise. To provide farmers with improved assistance in disease detection, image processing techniques combined with computational intelligence or soft computing techniques can be employed. These methods offer a more effective approach to disease detection. By extracting features from the symptoms of a disease, it becomes possible to identify and detect the presence of a disease in plants. Therefore, feature extraction techniques play a vital role in such systems. This paper focuses on reviewing the feature extraction methods, highlighting their advantages and disadvantages. It provides a comprehensive discussion on various image features, including color, texture, and shape, for different types of disorders found in diverse agricultural practices.

Optimal Illumination Distance Metrics for Person Re-identification in Complex Lighting Conditions

Person Re-identification is extensively applied in public security and surveillance. However, environmental factors like time and location often lead to varying lighting conditions in captured pedestrian images, significantly impacting identification accuracy. Current approaches mitigate this issue through lighting transformation techniques, aiming to normalize images to a standard lighting condition for consistent person re-identification results. Yet, these methods overlook the fact that different content may hold distinct identification values under diverse lighting conditions. To address this, we conducted an analysis on the identification distance between images of the same or different pedestrians under predefined lighting conditions. From this analysis, we introduce the concept of optimal lighting: a condition where the distance between image pairs is minimized compared to other lighting scenarios. We propose utilizing this optimal lighting distance in the image retrieval process for final ranking. Our study, validated on synthetic datasets Market-IA and Duke-IA, demonstrates that optimal lighting is independent of image texture information. Each image pair exhibits a unique optimal lighting, yet consistently shows a minimum distance value.

Remote Sensing Image Denoising Based on Feature Interaction Complementary Learning

Optical remote sensing images are of considerable significance in a plethora of applications, including feature recognition and scene semantic segmentation. However, the quality of remote sensing images is compromised by the influence of various types of noise, which has a detrimental impact on their practical applications in the aforementioned fields. Furthermore, the intricate texture characteristics inherent to remote sensing images present a significant hurdle in the removal of noise and the restoration of image texture details. In order to address these challenges, we propose a feature interaction complementary learning (FICL) strategy for remote sensing image denoising. In practical terms, the network is comprised of four main components: noise predictor (NP), reconstructed image predictor (RIP), feature interaction module (FIM), and fusion module. The combination of these modules serves to not only complete the fusion of the prediction results of NP and RIP, but also to achieve a deep coupling of the characteristics of the two predictors. Consequently, the advantages of noise prediction and reconstructed image prediction can be combined, thereby enhancing the denoising capability of the model. Furthermore, comprehensive experimentation on both synthetic Gaussian noise datasets and real-world denoising datasets has demonstrated that FICL has achieved favorable outcomes, emphasizing the efficacy and robustness of the proposed framework.

Advanced Deep Learning Fusion Model for Early Multi-Classification of Lung and Colon Cancer Using Histopathological Images.

In recent years, the healthcare field has experienced significant advancements. New diagnostic techniques, treatments, and insights into the causes of various diseases have emerged. Despite these progressions, cancer remains a major concern. It is a widespread illness affecting individuals of all ages and leads to one out of every six deaths. Lung and colon cancer alone account for nearly two million fatalities. Though it is rare for lung and colon cancers to co-occur, the spread of cancer cells between these two areas-known as metastasis-is notably high. Early detection of cancer greatly increases survival rates. Currently, histopathological image (HI) diagnosis and appropriate treatment are key methods for reducing cancer mortality and enhancing survival rates. Digital image processing (DIP) and deep learning (DL) algorithms can be employed to analyze the HIs of five different types of lung and colon tissues. Therefore, this paper proposes a refined DL model that integrates feature fusion for the multi-classification of lung and colon cancers. The proposed model incorporates three DL architectures: ResNet-101V2, NASNetMobile, and EfficientNet-B0. Each model has limitations concerning variations in the shape and texture of input images. To address this, the proposed model utilizes a concatenate layer to merge the pre-trained individual feature vectors from ResNet-101V2, NASNetMobile, and EfficientNet-B0 into a single feature vector, which is then fine-tuned. As a result, the proposed DL model achieves high success in multi-classification by leveraging the strengths of all three models to enhance overall accuracy. This model aims to assist pathologists in the early detection of lung and colon cancer with reduced effort, time, and cost. The proposed DL model was evaluated using the LC25000 dataset, which contains colon and lung HIs. The dataset was pre-processed using resizing and normalization techniques. The model was tested and compared with recent DL models, achieving impressive results: 99.8% for precision, 99.8% for recall, 99.8% for F1-score, 99.96% for specificity, and 99.94% for accuracy. Thus, the proposed DL model demonstrates exceptional performance across all classification categories.

DBSF-Net: Infrared Image Colorization Based on the Generative Adversarial Model with Dual-Branch Feature Extraction and Spatial-Frequency-Domain Discrimination

Thermal infrared cameras can image stably in complex scenes such as night, rain, snow, and dense fog. Still, humans are more sensitive to visual colors, so there is an urgent need to convert infrared images into color images in areas such as assisted driving. This paper studies a colorization method for infrared images based on a generative adversarial model. The proposed dual-branch feature extraction network ensures the stability of the content and structure of the generated visible light image; the proposed discrimination strategy combining spatial and frequency domain hybrid constraints effectively improves the problem of undersaturated coloring and the loss of texture details in the edge area of the generated visible light image. The comparative experiment of the public infrared visible light paired data set shows that the algorithm proposed in this paper has achieved the best performance in maintaining the consistency of the content structure of the generated image, restoring the image color distribution, and restoring the image texture details.

Low-light image enhancement guided by multi-domain features for detail and texture enhancement

Low-light image enhancement holds significant value in the fields of computer vision and image processing, such as in applications like surveillance photography and medical imaging. Images captured in low-light environments typically suffer from significant noise levels, low contrast, and color distortion. Although existing low-light image enhancement techniques can improve image brightness and contrast to some extent, they often introduce noise or result in over-enhancement, leading to the loss of detail and texture. This paper introduces an innovative approach to low-light image enhancement by fusing spatial and frequency domain features while optimizing them with multiple loss functions. The core of the algorithm lies in its multi-branch feature extraction, multi-loss function constraints, and a carefully designed model structure. In particular, the model employs an encoder-decoder architecture, where the encoder extracts spatial features from the image, the Fourier feature extraction module captures frequency domain information, and the histogram feature encoder-decoder module processes global brightness distribution. These extracted features are then fused and reconstructed in the decoder to produce the enhanced image. In terms of loss functions, the algorithm combines perceptual loss, structural similarity loss, Fourier loss, and histogram loss to ensure comprehensive and natural enhancement effects. The novelty of this algorithm lies not only in its multi-branch feature extraction design but also in its unique model structure, which synergistically improves image quality across different domains, effectively preventing over-enhancement, and ultimately achieving a balanced enhancement of brightness, details, and texture. Experimental results on multiple datasets, including SIDD, LOL, MIT-Adobe-FiveK, and LOL-v2-synthetic, demonstrate that the proposed method outperforms existing techniques in terms of image detail, texture, and brightness. Specifically, it achieves a PSNR of 27.52 dB on the LOL dataset, surpassing Wavelet Diffusion by 1.19 dB. Additionally, on the LOL-v2-synthetic dataset, it achieves a PSNR of 29.56 dB, exceeding Wavelet Diffusion by 3.06 dB. These results demonstrate a significant enhancement in the visual quality of low-light images.

A computer vision system and machine learning algorithms for prediction of physicochemical changes and classification of coated sweet cherry

The current research utilized visual characteristics obtained from RGB images and qualitative characteristics to investigate changes in surface defects, predict physical and chemical characteristics, and classify sweet cherries during storage. It was achieved with the help of ANN (Artificial Neural Network) and ANFIS (Adaptive Neuro-Fuzzy Inference System) models. The ANN used in this study was a Multilayer Perceptron (MLP) with SigmoidAxon and TanhAxon threshold functions, trained with the Momentum training function. Additionally, ANFIS with a Mamdani system and Triangle, Gauss, and Trapezoidal membership functions, was employed to predict sweet cherries' physical and chemical properties and their quality classification. Both models incorporate four algorithms. Additionally, the algorithms use color statistical features and color texture features combined with physical and chemical properties, including weight loss, firmness, titratable acidity, and total anthocyanin content. The image color and texture characteristics were used by ANN and ANFIS models to predict physical and chemical properties with high accuracy. ANN and ANFIS models accurately estimate sweet cherry quality grades in all four algorithms with over 90% accuracy. According to the findings, the ANN and ANFIS models have demonstrated satisfactory performance in the qualitative classification and prediction of sweet cherries' physical and chemical properties.

Contour and texture preservation underwater image restoration via low-rank regularizations

Due to the wavelength-dependent absorption and scattering, underwater images are often degraded by color distortion, haze, and low contrast. Despite the great advancements achieved in improving the quality of underwater image, the loss of contour and texture details in the restored results remains a major obstacle for subsequent high-level visual tasks. To this end, we develop a novel variational model, including one data term and two fidelity terms, for underwater image restoration. Technically, according to Retinex theory, we first extend the traditional underwater image formation (UIF) model by decomposing the total radiance into the reflectance component and illumination component. Based on the extended UIF model, two low-rank penalty regularization terms are designed to constrain the gradients of reflectance and illumination. Furthermore, we innovatively incorporate the weighted fusion bright-dark channel algorithm and the fusion upper-middle channel prior algorithm into the proposed variational model for estimating the local background light (ambient illumination) and transmission map, respectively. Simultaneously, based on the alternating direction multiplier method (ADMM), we develop a fast optimization algorithm to speed up the iterative process. Extensive experiments demonstrate that the proposed method is effective in removing haze, enhancing contrast and sharpness, as well as preserving the image contour and texture details. Comprehensive comparisons on two large-scale underwater image datasets further validate the superiority of our method against several state-of-the-art methods in both qualitative and quantitative evaluations. The code of the proposed method will be available soon.

Single-image SVBRDF estimation with auto-adaptive high-frequency feature extraction

In this paper, we address the task of estimating spatially-varying bi-directional reflectance distribution functions (SVBRDF) of a near-planar surface from a single flash-lit image. Disentangling SVBRDF from the material appearance by deep learning has proven a formidable challenge. This difficulty is particularly pronounced when dealing with images lit by a point light source because the uneven distribution of irradiance in the scene interacts with the surface, leading to significant global luminance variations across the image. These variations may be overemphasized by the network and wrongly baked into the material property space. To tackle this issue, we propose a high-frequency path that contains an auto-adaptive subband “knob”. This path aims to extract crucial image textures and details while eliminating global luminance variations present in the original image. Furthermore, recognizing that color information is ignored in this path, we design a two-path strategy to jointly estimate material reflectance from both the high-frequency path and the original image. Extensive experiments on a substantial dataset have confirmed the effectiveness of our method. Our method outperforms state-of-the-art methods across a wide range of materials.

An automated CAD-to-XR framework based on generative AI and Shrinkwrap modelling for a User-Centred design approach

CAD-to-XR is the workflow to generate interactive Photorealistic Virtual Prototypes (iPVPs) for Extended Reality (XR) apps from Computer-Aided Design (CAD) models. This process entails modelling, texturing, and XR programming. In the literature, no automatic CAD-to-XR frameworks simultaneously manage CAD simplification and texturing. There are no examples of their adoption for User-Centered Design (UCD). Moreover, such CAD-to-XR workflows do not seize the potentialities of generative algorithms to produce synthetic images (textures). The paper presents a framework for implementing the CAD-to-XR workflow. The solution consists of a module for texture generation based on Generative Adversarial Networks (GANs). The generated texture is then managed by another module (based on Shrinkwrap modelling) to develop the iPVP by simplifying the 3D model and UV mapping the generated texture. The geometric and material data is integrated into a graphic engine, which allows for programming an interactive experience with the iPVP in XR. The CAD-to-XR framework was validated on two components (rifle stock and forend) of a sporting rifle. The solution can automate the texturing process of different product versions in shorter times (compared to a manual procedure). After each product revision, it avoids tedious and manual activities required to generate a new iPVP. The image quality metrics highlight that images are generated in a “realistic” manner (the perceived quality of generated textures is highly comparable to real images). The quality of the iPVPs, generated through the proposed framework and visualised by users through a mixed reality head-mounted display, is equivalent to traditionally designed prototypes.

PTPFusion: A progressive infrared and visible image fusion network based on texture preserving

Infrared and visible image fusion aims to provide a more comprehensive image for downstream tasks by highlighting the main target and maintaining rich texture information. Image fusion methods based on deep learning suffer from insufficient multimodal information extraction and texture loss. In this paper, we propose a texture-preserving progressive fusion network (PTPFusion) to extract complementary information from multimodal images to solve these issues. To reduce image texture loss, we design multiple consecutive texture-preserving blocks (TPB) to enhance fused texture. The TPB can enhance the features by using a parallel architecture consisting of a residual block and derivative operators. In addition, a novel cross-channel attention (CCA) fusion module is developed to obtain complementary information by modeling global feature interactions via cross-queries mechanism, followed by information fusion to highlight the feature of the salient target. To avoid information loss, the extracted features at different stages are merged as the output of TPB. Finally, the fused image will be generated by the decoder. Extensive experiments on three datasets show that our proposed fusion algorithm is better than existing state-of-the-art methods.

Sterilization of image steganography using self-supervised convolutional neural network

Background With the development of steganography technology, lawbreakers can implement covert communication in social networks more easily, exacerbating network security risks. Sterilization of image steganography methods can eliminate secret messages to block the transmission of illegal covert communication. However, existing methods overly rely on cover-stego image pairs and are unable to sanitize unknown image, which reduces stego image blocking rate in social networks. Methods To address the above problems, this paper proposes an effective sterilization of image steganography method using self-supervised convolutional neural network (SS-Net), which does not require any prior knowledge of image steganography schemes. SS-Net includes a purification module and a refinement module. Firstly, the pixel-shuffle down-sampling in purification module is adopted to reduce the spatial correlation of pixels in the stgeo image, and improve the learning mode from supervised learning to self-supervised learning. Secondly, centrally masked convolutions and dilated convolution residual blocks are merged to eliminate secret messages and avoid image quality degradation. Finally, a refinement module is employed to improve image texture details and boundaries. Results A series of experiments show that SS-Net from BOSSbase test sets is able to balance the destruction of secret messages with image quality, achieving 100% blocking rate of stego image. Meanwhile, our method outperforms the state-of-the-art methods in secret messages elimination ability and image quality preserving ability.

A Novel Network for Low-Dose CT Denoising Based on Dual-Branch Structure and Multi-Scale Residual Attention.

Deep learning-based denoising of low-dose medical CT images has received great attention both from academic researchers and physicians in recent years, and has shown important application value in clinical practice. In this work, a novel two-branch and multi-scale residual attention-based network for low-dose CT image denoising is proposed. It adopts a two-branch framework structure, to extract and fuse image features at shallow and deep levels respectively, to recover image texture and structure information as much as possible. We propose the adaptive dynamic convolution block (ADCB) in the local information extraction layer. It can effectively extract the detailed information of low-dose CT denoising and enables the network to better capture the local details and texture features of the image, thereby improving the denoising effect and image quality. Multi-scale edge enhancement attention block (MEAB) is proposed in the global information extraction layer, to perform feature fusion through dilated convolution and a multi-dimensional attention mechanism. A multi-scale residual convolution block (MRCB) is proposed to integrate feature information and improve the robustness and generalization of the network. To demonstrate the effectiveness of our method, extensive comparison experiments are conducted and the performances evaluated on two publicly available datasets. Our model achieves 29.3004 PSNR, 0.8659 SSIM, and 14.0284 RMSE on the AAPM-Mayo dataset. It is evaluated by adding four different noise levels σ = 15, 30, 45, and 60 on the Qin_LUNG_CT dataset and achieves the best results. Ablation studies show that the proposed ADCB, MEAB, and MRCB modules improve the denoising performances significantly. The source code is available at https://github.com/Ye111-cmd/LDMANet .

Image texture analysis of pellets made of lignocellulosic materials

This experiment aimed to find the relation between image texture features of pellets made of various lignocellulosic materials (wood, wheat straw, hay, giant miscanthus, prairie spartina, and giant knotweed) and their physico-mechanical properties (density, compressive energy, maximum compressive strength, modulus of elasticity). Using the Kruskal-Wallis's test, the effect of materials on these properties was examined. Texture features were derived from the grey-level co-occurrence matrix, grey-level run-length matrix, absolute gradient matrix, autoregressive model, and wavelet decomposition, resulting in 86 features, later reduced to 8 factors via explanatory factor analysis. These factors were used as predictors in regression models for physico-mechanical properties. The models for modulus of elasticity achieved R2adj values of 0.91–0.99 (except for hay and wood), compressive stress models achieved 0.65–0.99 (excluding hay and wood), compressive energy models ranged from 0.60 to 0.97 (excluding hay), and density models ranged from 0.56 to 0.97 (excluding wood). The study confirmed a significant correlation between material type, texture parameters and compression resistance, suggesting this method could monitor pellet quality in production.

Enhancing Pléiades-based crop mapping with multi-temporal and texture information

Accurate crop mapping using satellite imagery is crucial for improving the monitoring of agricultural landscapes. Very high resolution (VHR) satellite imagery offers unique capabilities in this respect, allowing for even small fields to be discerned and image texture analysis to be performed. Additionally, satellite imagery has greater efficiency than unmanned aerial vehicles due to its extensive coverage. Moreover, the operation flexibility of VHR satellites means that timely image acquisition is possible several times during the growing season. This study investigates the potential of VHR Pléiades images and the random forest classifier for accurate crop mapping. Four images acquired on April 9th, May 12th, May 31st, and June 20th were used to test 16 classification scenarios, including single-date and multi-temporal combinations of spectral bands, texture features, and vegetation Indices (VIs). The classification using the spectral bands from all four images achieved the highest overall accuracy, 93.9% and 96.3% at field and pixel levels, respectively. The bitemporal classifications had lower accuracy. Nevertheless, the combination of the May 12th and June 20th spectral bands had 90% accuracy, which indicated that two images may be sufficient for reliable mapping if the periods with phenological differences between crops are considered. Adding texture features to the spectral bands significantly enhanced the accuracy (up to 8%) of single-date classifications, making it highly recommended when only one image is available. However, the impact of texture was more pronounced on the later dates. It showed the most marked benefit for vineyards and alfalfa, with minimal or no improvement observed for other classes like winter barley. An additional increase in overall accuracy was achieved in three of the four dates by supplementing the spectral and texture bands with VIs. This study highlights the importance of considering image acquisition dates and crop types when designing satellite-based crop mapping strategies for optimal accuracy.