Subband Image Research Articles

Mango leaf disease diagnosis using Total Variation Filter Based Variational Mode Decomposition

Mango leaf diseases significantly threaten mango cultivation, impacting both yield and quality. Accurate and early diagnosis is essential for effectively managing and controlling these diseases. This study introduces a novel approach for diagnosing mango leaf diseases, leveraging Total Variation Filter-based Variational Mode Decomposition. The proposed method enhances the extraction of disease-specific features from leaf images by decomposing them into intrinsic mode functions while simultaneously reducing noise and preserving important edge information. Experimental results demonstrate that the proposed method effectively isolates relevant patterns associated with various mango leaf diseases, improving diagnostic accuracy compared to traditional methods. Deep learning models, DenseNet121 and VGG-19, are used for feature extraction from sub-band images, and extracted features are concatenated and fed to Random Forest for classification. Utilizing tenfold cross-validation, our model demonstrated enhanced classification accuracy (98.85 %), specificity (99.37 %), and sensitivity (98.0 %) in detecting diseases from Mango leaf images. Feature maps and Gradient-weighted Class Activation Mapping analysis was conducted to visualize and scrutinize the essential regions crucial for accurate predictions. Statistical analysis indicates that our proposed architecture outperforms pre-trained models and existing mango leaf disease detection methods. This diagnostic approach can be a rapid disease detection tool for imaging specialists utilizing leaf images. The robustness and efficiency of the presented work in handling complex and noisy image data make it a promising tool for automated agricultural disease diagnosis systems, facilitating timely and precise interventions in mango orchards.

Glaucoma diagnosis from fundus images using modified Gauss-Kuzmin-distribution-based Gabor features in 2D-FAWT

PurposeGlaucoma is an eye disease that can result in perpetual eye vision loss if not perceived and treated in time. Therefore, an accurate and efficient automated method has been proposed for diagnosing Glaucoma. This method enables mass screening for such conditions and reduces the task of healthcare professionals. MethodologyThis proposed method involves splitting the fundus images into red, green, and blue channel components, where the green channel component is selected for further processing. The processed green channel of the images has been decomposed using Flexible analytical wavelet transform (FAWT) in sub-bands. Furthermore, the features are mined from decomposed green channel sub-band images using the modified Gauss-Kuzmin-distribution-based Gabor (GKDG) filter (eight scales and five orientations) and NCA by employing them to lessen the dimensionality of the silent features. Then, the selected features are graded using the student t-value, and the graded features are fed to the kernels like linear, gradient descent, Morlet, and Mexican-hat-based LS-SVM classifier to classify the Glaucoma. Results and ConclusionThe results of this research suggest that the newly proposed glaucoma classification model demonstrates superior performance compared to contemporary techniques. The model's evaluation was conducted using tenfold cross-validation, revealing an enhanced accuracy of 95.84 %, a specificity of 97.17 %, and a sensitivity of 94.55 %.

A new approach based on 2D wavelet transform in the analysis of wettability behavior and evaporation rate for the surface of PTFE dielectric material used in high voltage applications

In this study, a new approach based on 2D wavelet transform is proposed for the analysis of wettability behavior and evaporation rate. Droplet images were taken from the PTFE dielectric sample surface at 30th, 270th, 510th, 750th, 990th, 1230th, 1470th, and 1710th seconds. The droplet images were decomposed into sub-bands using functions from six different wavelet families. The energy, entropy, mean, standard deviation, skewness, and kurtosis measures were calculated using wavelet coefficients for each sub-band image. Linear, exponential, and polynomial fitting methods were used to estimate the contact angle of the droplet images. The energy and standard deviation values, which are the selected optimal measures, were used as input in the fitting methods, and the regression coefficient (R2) and Root Mean Square Error (RMSE) values of the fitting methods were calculated. Thus, a high-performing contact angle prediction model with 2nd level sub-band image wavelet coefficients has been obtained.

Image Robust Watermarking Method Based on DWT-SVD Transform and Chaotic Map

The existing watermarking algorithms make it difficult to balance the invisibility and robustness of the watermark. This paper proposes a robust image watermarking method based on discrete wavelet transform (DWT), singular value decomposition (SVD), and chaotic maps. This method is a semiblind watermarking method. First, a chaotic logistic-tent map is introduced, employing an extensive chaotic parameter domain. This map is amalgamated with Arnold’s transformation to encrypt the watermark image, thereby bolstering the security of the watermark information. Subsequently, the frequency domain is obtained by applying DWT to the carrier image. Embedding watermarks in the frequency domain ensures the invisibility of the watermark, with a preference for a high-frequency subband after the DWT of the carrier image for enhanced watermark robustness. SVD is then applied to both the high-frequency subband of the carrier image after DWT and the encrypted watermark image. The final step involves embedding the singular values of the encrypted watermark image into the carrier image’s singular values, thereby completing the watermark information embedding process. In simulation experiments, an invisibility test was conducted on various carrier images, yielding peak signal-to-noise ratio (PSNR) values consistently exceeding 43, and structural similarity (SSIM) close to 1. Robustness testing against various types of attacks resulted in normalized correlation (NC) values consistently surpassing 0.9, with bit error rate (BER) values approaching 0. In conclusion, the proposed algorithm satisfies imperceptibility requirements while demonstrating formidable robustness.

A Multi-Scale Fusion Strategy for Side Scan Sonar Image Correction to Improve Low Contrast and Noise Interference

Side scan sonar images have great application prospects in underwater surveys, target detection, and engineering activities. However, the acquired sonar images exhibit low illumination, scattered noise, distorted outlines, and unclear edge textures due to the complicated undersea environment and intrinsic device flaws. Hence, this paper proposes a multi-scale fusion strategy for side scan sonar (SSS) image correction to improve the low contrast and noise interference. Initially, an SSS image was decomposed into low and high frequency sub-bands via the non-subsampled shearlet transform (NSST). Then, modified multi-scale retinex (MMSR) was employed to enhance the contrast of the low frequency sub-band. Next, sparse dictionary learning (SDL) was utilized to eliminate high frequency noise. Finally, the process of NSST reconstruction was completed by fusing the emerging low and high frequency sub-band images to generate a new sonar image. The experimental results demonstrate that the target features, underwater terrain, and edge contours could be clearly displayed in the image corrected by the multi-scale fusion strategy when compared to eight correction techniques: BPDHE, MSRCR, NPE, ALTM, LIME, FE, WT, and TVRLRA. Effective control was achieved over the speckle noise of the sonar image. Furthermore, the AG, STD, and E values illustrated the delicacy and contrast of the corrected images processed by the proposed strategy. The PSNR value revealed that the proposed strategy outperformed the advanced TVRLRA technology in terms of filtering performance by at least 8.8%. It can provide sonar imagery that is appropriate for various circumstances.

Sub-Band Backdoor Attack in Remote Sensing Imagery

Remote sensing datasets usually have a wide range of spatial and spectral resolutions. They provide unique advantages in surveillance systems, and many government organizations use remote sensing multispectral imagery to monitor security-critical infrastructures or targets. Artificial Intelligence (AI) has advanced rapidly in recent years and has been widely applied to remote image analysis, achieving state-of-the-art (SOTA) performance. However, AI models are vulnerable and can be easily deceived or poisoned. A malicious user may poison an AI model by creating a stealthy backdoor. A backdoored AI model performs well on clean data but behaves abnormally when a planted trigger appears in the data. Backdoor attacks have been extensively studied in machine learning-based computer vision applications with natural images. However, much less research has been conducted on remote sensing imagery, which typically consists of many more bands in addition to the red, green, and blue bands found in natural images. In this paper, we first extensively studied a popular backdoor attack, BadNets, applied to a remote sensing dataset, where the trigger was planted in all of the bands in the data. Our results showed that SOTA defense mechanisms, including Neural Cleanse, TABOR, Activation Clustering, Fine-Pruning, GangSweep, Strip, DeepInspect, and Pixel Backdoor, had difficulties detecting and mitigating the backdoor attack. We then proposed an explainable AI-guided backdoor attack specifically for remote sensing imagery by placing triggers in the image sub-bands. Our proposed attack model even poses stronger challenges to these SOTA defense mechanisms, and no method was able to defend it. These results send an alarming message about the catastrophic effects the backdoor attacks may have on satellite imagery.

Multibranch Wavelet-Based Network for Image Demoiréing.

Moiré patterns caused by aliasing between the camera's sensor and the monitor can severely degrade image quality. Image demoiréing is a multi-task image restoration method that includes texture and color restoration. This paper proposes a new multibranch wavelet-based image demoiréing network (MBWDN) for moiré pattern removal. Moiré images are separated into sub-band images using wavelet decomposition, and demoiréing can be achieved using the different learning strategies of two networks: moiré removal network (MRN) and detail-enhanced moiré removal network (DMRN). MRN removes moiré patterns from low-frequency images while preserving the structure of smooth areas. DMRN simultaneously removes high-frequency moiré patterns and enhances fine details in images. Wavelet decomposition is used to replace traditional upsampling, and max pooling effectively increases the receptive field of the network without losing the spatial information. Through decomposing the moiré image into different levels using wavelet transform, the feature learning results of each branch can be fully preserved and fed into the next branch; therefore, possible distortions in the recovered image are avoided. Thanks to the separation of high- and low-frequency images during feature training, the proposed two networks achieve impressive moiré removal effects. Based on extensive experiments conducted using public datasets, the proposed method shows good demoiréing validity both quantitatively and qualitatively when compared with the state-of-the-art approaches.

SuperPCA and 2-D compact variational mode decomposition for feature extraction from hyperspectral images

ABSTRACT The 2-D compact variational mode decomposition (2-D-C-VMD) has potential research significance in data mining of hyperspectral images (HSIs). The paper proposes a method of concatenation of features. First, the 2-D-C-VMD technique is used to decompose the HSIs to obtain smooth and clear boundary sub-band images when the 2-D-C-VMD takes the most preferred a priori parameter K × . Second, the optimal low-frequency component features of the image and the optimal high-frequency component constructed features are concatenated first and last, and the structure of the concatenation is constructed into a new fused data. Finally, in order to avoid the redundancy of the fused data band information from affecting the classification result, combine a recently developed simple and practical superpixelwise principal component analysis (SuperPCA) dimensionality reduction method to effectively reduce the dimensionality of the fused data. By comparing the analysis with relying only on the optimal low-frequency image feature information and with the direct linear superposition fusion that has been employed, it is verified that the concatenation of features approach proposed in this paper can obtain better recognition results.

DUIANet: A double layer U-Net image hiding method based on improved Inception module and attention mechanism

Image hiding is the process of hiding secret image into cover image, and revealing secret image from steganographic image. However, the quality of generated images and the ability to resist steganalysis detection of steganographic image can be further improved. We propose a novel double-layer U-Net image hiding method based on improved Inception and attention mechanism. The secret image undergoes Haar wavelet transform, hiding network hides the wavelet sub-band of secret image into cover image. We use improved Inception while cascading convolution and attention mechanisms to better extract and fuse information, resulting in high-quality steganographic image. In the revealing stage, Attention mechanism can help networks extract more features about secret image, then we using inverse Haar wavelet transform to revealing high-quality secret image. The experimental results show that the method can obtain high quality images and have good security. When hiding multiple images, the method still has good performance.

Analytic Design Technique for 2D FIR Circular Filter Banks and Their Efficient Implementation Using Polyphase Approach.

This paper proposes an analytical design procedure for 2D FIR circular filter banks and also a novel, computationally efficient implementation of the designed filter bank based on a polyphase structure and a block filtering approach. The component filters of the bank are designed in the frequency domain using a specific frequency transformation applied to a low-pass, band-pass and high-pass 1D prototype with a specified Gaussian shape and imposed specifications (peak frequency, bandwidth). The 1D prototype filter frequency response is derived in a closed form as a trigonometric polynomial with a specified order using Fourier series, and then it is factored. Since the design starts from a 1D prototype with a factored transfer function, the frequency response of the designed 2D filter bank components also results directly in a factored form. The designed filters have an accurate shape, with negligible distortions at a relatively low order. We present the design of two types of circular filter banks: uniform and non-uniform (dyadic). An example of image analysis with the uniform filter bank is also provided, showing that the original image can be accurately reconstructed from the sub-band images. The proposed implementation is presented for a simpler case, namely for a smaller size of the filter kernel and of the input image. Using the polyphase and block filtering approach, a convenient implementation at the system level is obtained for the designed 2D FIR filter, with a relatively low computational complexity.

Wavelet Transform Feature Enhancement for Semantic Segmentation of Remote Sensing Images

With developments in deep learning, semantic segmentation of remote sensing images has made great progress. Currently, mainstream methods are based on convolutional neural networks (CNNs) or vision transformers. However, these methods are not very effective in extracting features from remote sensing images, which are usually of high resolution with plenty of detail. Operations including downsampling will cause the loss of such features. To address this problem, we propose a novel module called Hierarchical Wavelet Feature Enhancement (WFE). The WFE module involves three sequential steps: (1) performing multi-scale decomposition of an input image based on the discrete wavelet transform; (2) enhancing the high-frequency sub-bands of the input image; and (3) feeding them back to the corresponding layers of the network. Our module can be easily integrated into various existing CNNs and transformers, and does not require additional pre-training. We conducted experiments on the ISPRS Potsdam and ISPRS Vaihingen datasets, with results showing that our method improves the benchmarks of CNNs and transformers while performing little additional computation.

Fine Perceptive GANs for Brain MR Image Super-Resolution in Wavelet Domain.

Magnetic resonance (MR) imaging plays an important role in clinical and brain exploration. However, limited by factors such as imaging hardware, scanning time, and cost, it is challenging to acquire high-resolution MR images clinically. In this article, fine perceptive generative adversarial networks (FP-GANs) are proposed to produce super-resolution (SR) MR images from the low-resolution counterparts. By adopting the divide-and-conquer scheme, FP-GANs are designed to deal with the low-frequency (LF) and high-frequency (HF) components of MR images separately and parallelly. Specifically, FP-GANs first decompose an MR image into LF global approximation and HF anatomical texture subbands in the wavelet domain. Then, each subband generative adversarial network (GAN) simultaneously concentrates on super-resolving the corresponding subband image. In generator, multiple residual-in-residual dense blocks are introduced for better feature extraction. In addition, the texture-enhancing module is designed to trade off the weight between global topology and detailed textures. Finally, the reconstruction of the whole image is considered by integrating inverse discrete wavelet transformation in FP-GANs. Comprehensive experiments on the MultiRes_7T and ADNI datasets demonstrate that the proposed model achieves finer structure recovery and outperforms the competing methods quantitatively and qualitatively. Moreover, FP-GANs further show the value by applying the SR results in classification tasks.

Multimodality medical image fusion analysis with multi-plane features of PET and MRI images using ONSCT

ABSTRACT The Multimodal Medical Image Fusion (MMIF) is affected by poor image quality, which leads to the extraction of inefficient features. The main intent of this work is to fuse various planes in the PET and MRI medical images efficiently using the MMIF approach. Initially, the sample images containing the axial plane of PET and MRI images are aggregated from standard datasets. Then, the collected images are employed for the decomposition process, which is accomplished via Optimal Non-Subsampled Contourlet Transform (ONSCT). The parameters in the NSCT are optimized using the Modified Water Strider Algorithm (MWSA. Once the images are decomposed, it is segmented into two sub-bands as high frequency and low-frequency sub-bands. Consequently, the high-frequency sub-bands of both PET and MRI images are fused by using the optimal weighted average fusion, in which the weight factor is obtained optimally by the MWSA algorithm. Similarly, the low-frequency sub-bands of both medical images are combined by sparse fusion technique. Finally, both the resultant fused images are subjected to Inverse Non-Subsampled Contourlet Transform (INSCT) to get desired fused images. The experimental findings suggest that the proposed model has effectively fused the images, and it also enhances the similarity score with axial planes.

Wavelet and Earth Mover’s Distance Coupling Denoising Techniques

The widely used wavelet-thresholding techniques (DWT-H and DWT-S) have a near-optimal behavior that cannot be enhanced by any local denoising filter, but they cannot utilize the similarity of small-size image patches to enhance the denoising performance. Two of the latest improvements (WNLM and NLMW) introduced the Euclidean distance to measure the similarity of image patches, and then used the non-local meaning of similar patches for further denoising. Since the Euclidean distance is not a good similarity measurement, these two improvements are limited. In this study, we introduced the earth mover’s distance (EMD) as the similarity measure of small-scale patches within the wavelet sub-bands of noisy images. Moreover, at higher noise levels, we further incorporated joint bilateral filtering, which can filter both the spatial domain and the intensity domain of images. Denoising simulation experiments on BSDS500 demonstrated that our algorithm outperformed the DWT-H, DWT-S, WNLM, and NLMW algorithms by 4.197 dB, 3.326 dB, 2.097 dB, and 1.162 dB in terms of the average PSNR, and by 0.230, 0.213, 0.132, and 0.085 in terms of the average SSIM.

A novel framework of multimodal medical image fusion using adaptive NSST and optimized deep learning approach

ABSTRACT Multimodal medical image fusion plays a pivotal role in the medical and imaging industry. Existing works of deep learning method suffers from blurred texture characteristics and computing efficiency. Thus, a novel deep learning model is proposed for multimodal medical image fusion. Initially, an Adaptive Non-Subsampled Shearlet Transform (ANSST) approach is developed for decomposing the images, where the filter coefficient is optimized by Hybrid Water Strider-Dingo Optimization (HWS-DOX). The fusion of the high sub-bands of source image 1 and high sub-bands of source image 2 is done by an Optimized Deep Neural Network (ODNN). Then, the lower sub-bands of source image 1 and lower sub-bands of source image 2 will be fused through a weighted averaging scheme. Finally, the final fused images are attained by applying the inverse ANSST. Thus, the performance of recommended module is compared over classical heuristics and various transform methods.

A Hybrid Domain Medical Image Encryption Scheme Using URUK and WAM Chaotic Maps with Wavelet – Fourier Transforms

Image encryption is one of the most important techniques to maintain data confidentiality against illegal access and fraudulent usage. In this study, a new medical image encryption technique was developed by combining the discrete wavelet transform, the fast Fourier transform, the Arnold transform, and two multidimensional chaotic systems. The medical image is subjected to a discrete wavelet transform before the magic square shuffles the image sub-bands. Confusion operations are performed on each scrambled subdomain using the Uruk 4D chaotic system. To increase randomness and unpredictability, a second stage of confusion is implemented in the domain of the Fast Fourier transform using the Arnold transform. The final encrypted image is obtained utilizing secret keys derived from the WAM 3D chaotic system. In particular, the initial conditions for chaotic systems are derived from grayscale values, thereby increasing the algorithm’s sensitivity to the input image. The results of the tests and the security analysis indicate that the proposed algorithm is exceptionally reliable and secure.

Efficient and secure image encryption using key substitution process with discrete wavelet transform

Over the past few years, there has been a rise in the utilization of chaotic encryption algorithms for securing images. The majority of chaos-based encryption algorithms adhere to the conventional model of confusion and diffusion, which typically involves either implementing multiple encryption rounds or employing a single round of intricate encryption to guarantee robust security. However, such kind of approaches reduces the computational efficiency of the encryption process but compromises security. There is a trade-off between security and computational efficiency. Prioritizing security may require high computational processes. To overcome this issue, a key substitution encryption process with discrete wavelet transform (KSP-DWT) is developed in the proposed image encryption technique (IET). Based on KSP-DWT and IET, the abbreviation of the proposed work is used in this paper as KSP-DWT-IET. The proposed KSP-DWT algorithm employs a key scheming technique to update the initial keys and uses a novel substitution method to encrypt digital images of different sizes. Additionally, the integration of DWT can result in the compression of frequency sub-bands of the source image, leading to lower computational overheads without compromising the security of the encryption. The KSP-DWT-IET performs a single encryption round and is highly secure and efficient. The simulation results and security analysis conducted on KSP-DWT-IET confirm its effectiveness in ensuring high-security image encryption while minimizing computational overhead. The proposed encryption technique undergoes various security analyses, including entropy, contrast, correlation, energy, NPCR (Number of Pixel Changes Rate), UACI (Unified Average Change Intensity) and computational complexity. The statistical values obtained for such parameters are 7.9991, 10.9889, 0.0001, 0.0152, 33.6767, and 33.6899, respectively, which indicate that the encryption technique performs very well in terms of security and computational efficiency. The proposed encryption scheme is also analyzed for its computational time in addition to its security. The analysis shows that the scheme can efficiently encrypt images of varying sizes with a high level of security in a short amount of time (i.e., 2 ms). Therefore, it is feasible to use this encryption scheme in real-time applications without causing any significant delays. Moreover, the key space of the proposed encryption scheme is large enough (i.e. Keyspace >2100) to resist the brute force attack.

Recognize enhanced temporal-spatial-spectral features with a parallel multi-branch CNN and GRU.

Deep learning has been applied to the recognition of motor imagery electroencephalograms (MI-EEG) in brain-computer interface, and the performance results depend on data representation as well as neural network structure. Especially, MI-EEG is so complex with the characteristics of non-stationarity, specific rhythms, and uneven distribution; however, its multidimensional feature information is difficult to be fused and enhanced simultaneously in the existing recognition methods. In this paper, a novel channel importance (NCI) based on time-frequency analysis is proposed to develop an image sequence generation method (NCI-ISG) for enhancing the integrity of data representation and highlighting the contribution inequalities of different channels as well. Each electrode of MI-EEG is converted to a time-frequency spectrum by utilizing short-time Fourier transform; the corresponding part to 8-30Hz is combined with random forest algorithm for computing NCI; and it is further divided into three sub-images covered by α (8-13Hz), β1 (13-21Hz), and β2 (21-30Hz) bands; their spectral powers are further weighted by NCI and interpolated to 2-dimensional electrode coordinates, producing three main sub-band image sequences. Then, a parallel multi-branch convolutional neural network and gate recurrent unit (PMBCG) is designed to successively extract and identify the spatial-spectral and temporal features from the image sequences. Two public four-class MI-EEG datasets are adopted; the proposed classification method respectively achieves the average accuracies of 98.26% and 80.62% by 10-fold cross-validation experiment; and its statistical performance is also evaluated by multi-indexes, such as Kappa value, confusion matrix, and ROC curve. Extensive experiment results show that NCI-ISG + PMBCG can yield great performance on MI-EEG classification compared to state-of-the-art methods. The proposed NCI-ISG can enhance the feature representation of time-frequency-space domains and match well with PMBCG, which improves the recognition accuracies of MI tasks and demonstrates the preferable reliability and distinguishable ability. This paper proposes a novel channel importance (NCI) based ontime-frequencyanalysis to develop an image sequences generation method (NCI-ISG) for enhancing the integrity of data representation and highlighting the contribution inequalities of different channels as well. Then, a parallel multi-branch convolutional neural network and gate recurrent unit (PMBCG) is designed to successively extract and identify the spatial-spectral and temporal features from the image sequences.

Speckle Noise Reduction in Ultrasound Images

Abstract: Ultrasound Imaging is used to examine various organs in the Human Body. However, in the process of obtaining a Ultrasound Image, Speckle noise is generated due to backscattered echo signals. So, a noise reduction method is required. To improve the speckle noise reduction, we propose a novel algorithm using characteristics of speckle noise and filtering methods based on Speckle Reducing Anisotropic Diffusion (SRAD) filtering, Discrete Wavelet Transform (DWT), Weighted Guided Image Filtering (WGIF) and Gradient Domain Guided Image Filtering (GDGIF). The SRAD filter is exploited as a preprocessing filter because it can be directly applied to a medical US image containing speckle noise without a log-compression. The wavelet domain has the advantage of suppressing the additive noise. Therefore, a homomorphic transformation is utilized to convert the multiplicative noise into additive noise. After two-level DWT decomposition is applied, to suppress the residual noise of an SRAD filtered image, GDGIF and WGIF are exploited to reduce noise from seven high-frequency subband images and one low-frequency sub-band image, respectively. Finally, a noise-free image is attained through inverse DWT and an exponential transform

Feature Extraction and Classification of Canopy Gaps Using GLCM- and MLBP-Based Rotation-Invariant Feature Descriptors Derived from WorldView-3 Imagery

Accurate mapping of selective logging (SL) serves as the foundation for additional research on forest restoration and regeneration, species diversification and distribution, and ecosystem dynamics, among other applications. This study aimed to model canopy gaps created by illegal logging of Ocotea usambarensis in Mt. Kenya Forest Reserve (MKFR). A texture-spectral analysis approach was applied to exploit the potential of WorldView-3 (WV-3) multispectral imagery. First, texture properties were explored in the sub-band images using fused grey-level co-occurrence matrix (GLCM)- and local binary pattern (LBP)-based texture feature extraction. Second, the texture features were fused with colour using the multivariate local binary pattern (MLBP) model. The G-statistic and Euclidean distance similarity measures were applied to increase accuracy. The random forest (RF) and support vector machine (SVM) were used to identify and classify distinctive features in the texture and spectral domains of the WV-3 dataset. The variable importance measurement in RF ranked the relative influence of sets of variables in the classification models. Overall accuracy (OA) scores for the respective MLBP models were in the range of 80–95.1%. The respective user’s accuracy (UA) and producer’s accuracy (PA) for the univariate LBP and MLBP models were in the range of 67–75% and 77–100%, respectively.