Image Processing Classification Research Articles

Neural Architecture Search for Portrait Parsing.

This work proposes a neural architecture search (NAS) method for portrait parsing, which is a novel up-level task based on portrait segmentation and face labeling. Recently, NAS has become an effective method in terms of automatic machine learning. However, remarkable achievements have been made only in image classification and natural language processing (NLP) areas. Meanwhile, state-of-the-art portrait segmentation and face labeling approaches are all manually designed, but few models reach a tradeoff between efficiency and performance. Thus, we are extremely interested in improving existing NAS methods for dense-per-pixel prediction tasks on portrait datasets. To achieve that, we resort to a cell-based encoder-decoder architecture with an elaborate design of connectivity structure and searching space. As a result, we achieve state-of-the-art performance on three portrait tasks, including 96.8% MIOU on EG1800 (portrait segmentation), 91.2% overall F1-score on HELEN (face labeling), and 95.1% overall F1-score on CelebAMask-HQ (portrait parsing) with only 2.29M model parameters. That is, our approach compares favorably with all previous works on portrait datasets. More crucially, we empirically prove that even a fundamental encoder-decoder architecture may reach an outstanding result on the aforementioned tasks with the help of the innovative approach of NAS. To the best of our knowledge, our work is also the first to report the success of applying NAS on these portrait tasks.

Design Challenges in Effective Algorithm Development of Sign Language Recognition System

Sign language is the most putative language among hearing impaired people. They use non-verbal form of communication that involves hand gestures, head or body movement or facial expressions. Of these hand gestures is more widely used. Automatic Sign Language Recognition (ASLR) System can be used to convert these non-verbal signs into text or sound so that normal people can identify them without learning the sign language. ASLR employs Image Processing and Artificial Intelligence (AI) algorithms for effective conversion from sign to sound or text. This review unveils various image processing and AI steps involved in the conversion process, bringing out important topologies in the Image acquisition, segmentation, feature extraction, classification and detection process and a comparative analysis among various topologies. Attempts have been made to shed light into adoption of alternate design strategies in segmentation and feature extraction that enhance the performance in a complex environment.

Block-Scrambling-Based Encryption with Deep-Learning-Driven Remote Sensing Image Classification

Remote sensing is a long-distance measuring technology that obtains data about a phenomenon or an object. Remote sensing technology plays a crucial role in several domains, such as weather forecasts, resource surveys, disaster evaluation and environment protection. The application of remote-sensing images (RSIs) is extensive in some specific domains, such as national security and business secrets. Simple multimedia distribution techniques and the development of the Internet make the content security of RSIs a significant problem for both engineers and scientists. In this background, RSI classification using deep learning (DL) models becomes essential. Therefore, the current research article develops a block-scrambling-based encryption with privacy preserving optimal deep-learning-driven classification (BSBE-PPODLC) technique for the classification of RSIs. The presented BSBE-PPODLC technique follows a two-stage process, i.e., image encryption and classification. Initially, the RSI encryption process takes place based on a BSBE approach. In the second stage, the image classification process is performed, and it encompasses multiple phases, such as densely connected network (DenseNet) feature extraction, extreme gradient boosting (XGBoost) classifier and artificial gorilla troops optimizer (AGTO)-based hyperparameter tuning. The proposed BSBE-PPODLC technique was simulated using the RSI dataset, and the outcomes were assessed under different aspects. The outcomes confirmed that the presented BSBE-PPODLC approach accomplished improved performance compared to the existing models.

Optimal segmentation and fusion of multi-modal brain images using clustering based deep learning algorithm

In the last several years, the world of medical technology has seen a boom in multimodal picture fusion. Information is constrained since a single medical instrument can only acquire single modal pictures. Doctors often need a large number of multimodal pictures to get the complete information necessary for disease diagnosis. The burden associated with illness diagnosis will significantly rise when multimodal pictures are employed directly, and errors and interference are likely to occur. Fusion algorithms, which have been extensively employed in the medical industry, may very effectively combine a lot of information in multimodal pictures. However, the existing method has an issue with the earlier stages of brain tumor prediction in white images and inaccuracy image results. To overcome the above-mentioned problems, in this work, Adaptive Firefly Optimization based Convolutional Neural Network (AFFOCNN) and Modified Fully Connected Layer (MFCL) scheme is proposed. This work contains main steps such as noise removal, segmentation, feature extraction, image fusion, and image classification process. Initially, noise removal is done for improving the image quality by removing the noise. Then the modality MRI images are segmented and it is used for subdividing an image into its constituent regions or object. It segments the image into black and white images. After that, feature extraction is applied through the AFFOCNN algorithm which extracts the more informative image features. Image fusion of multi-modal images derived the lower-level, middle-level, and higher-level image contents. It can be viewed in multiple directions and fused in all directions. Finally, image classification is performed by using a Modified Fully Connected Layer (MFCL) which improves the training and testing features efficiently. It was determined from the results that the suggested combination of AFFOCN and MFCL algorithm performs improved than the current algorithms with the increased accuracy, precision, recall, and mean square error (MSE), as well as execution time with the values of 99.00%, 98.00%, 96.00%, 12.00% and 2.40 seconds respectively.

Deep Attention Network for Pneumonia Detection Using Chest X-Ray Images

In computer vision, object recognition and image categorization have proven to be difficult challenges. They have, nevertheless, generated responses to a wide range of difficult issues from a variety of fields. Convolution Neural Networks (CNNs) have recently been identified as the most widely proposed deep learning (DL) algorithms in the literature. CNNs have unquestionably delivered cutting-edge achievements, particularly in the areas of image classification, speech recognition, and video processing. However, it has been noticed that the CNN-training assignment demands a large amount of data, which is in low supply, especially in the medical industry, and as a result, the training process takes longer. In this paper, we describe an attention-aware CNN architecture for classifying chest X-ray images to diagnose Pneumonia in order to address the aforementioned difficulties. Attention Modules provide attention-aware properties to the Attention Network. The attention-aware features of various modules alter as the layers become deeper. Using a bottom-up top-down feedforward structure, the feedforward and feedback attention processes are integrated into a single feedforward process inside each attention module. In the present work, a deep neural network (DNN) is combined with an attention mechanism to test the prediction of Pneumonia disease using chest X-ray pictures. To produce attention-aware features, the suggested network was built by merging channel and spatial attention modules in DNN architecture. With this network, we worked on a publicly available Kaggle chest X-ray dataset. Extensive testing was carried out to validate the suggested model. In the experimental results, we attained an accuracy of 95.47% and an F- score of 0.92, indicating that the suggested model outperformed against the baseline models.

Optimal Deep Learning Model Enabled Secure UAV Classification for營ndustry 4.0

Emerging technologies such as edge computing, Internet of Things (IoT), 5G networks, big data, Artificial Intelligence (AI), and Unmanned Aerial Vehicles (UAVs) empower, Industry 4.0, with a progressive production methodology that shows attention to the interaction between machine and human beings. In the literature, various authors have focused on resolving security problems in UAV communication to provide safety for vital applications. The current research article presents a Circle Search Optimization with Deep Learning Enabled Secure UAV Classification (CSODL-SUAVC) model for Industry 4.0 environment. The suggested CSODL-SUAVC methodology is aimed at accomplishing two core objectives such as secure communication via image steganography and image classification. Primarily, the proposed CSODL-SUAVC method involves the following methods such as Multi-Level Discrete Wavelet Transformation (ML-DWT), CSO-related Optimal Pixel Selection (CSO-OPS), and signcryption-based encryption. The proposed model deploys the CSO-OPS technique to select the optimal pixel points in cover images. The secret images, encrypted by signcryption technique, are embedded into cover images. Besides, the image classification process includes three components namely, Super-Resolution using Convolution Neural Network (SRCNN), Adam optimizer, and softmax classifier. The integration of the CSO-OPS algorithm and Adam optimizer helps in achieving the maximum performance upon UAV communication. The proposed CSODL-SUAVC model was experimentally validated using benchmark datasets and the outcomes were evaluated under distinct aspects. The simulation outcomes established the supreme better performance of the CSODL-SUAVC model over recent approaches.

A Novel Framework for Semi-supervised Multiple-label Image Classification using Multi-stage CNN and Visual Attention Mechanism

To train deep neural networks effectively, a lot of labeled data is typically needed. However, real-time applications make it difficult and expensive to acquire high-quality labels for the data because it takes skill and knowledge to accurately annotate multiple label images. In order to enhance classification performance, it is also crucial to extract image features from all potential objects of various sizes as well as the relationships between labels of numerous label images. The current approaches fall short in their ability to map the label dependencies and effectively classify the labels. They also perform poor to label the unlabeled images when small amount of labeled images available for classification. In order to solve these issues, we suggest a new framework for semi-supervised multiple object label classification using multi-stage Convolutional neural networks with visual attention (MSCNN)and GCN for label co-occurrence embedding(LCE) (MSCNN-LCE-MIC), which combines GCN and attention mechanism to concurrently capture local and global label dependencies throughout the entire image classification process. Four main modules make up MSCNN-LCE-MIC: (1) improved multi-label propagation method for labeling largely available unlabeled image; (2) a feature extraction module using multi-stage CNN with visual attention mechanism that focuses on the connections between labels and target regions to extract accurate features from each input image; (3) a label co-existence learning that applies GCN to discover the associations between different items to create embeddings of label co-occurrence; and (4) an integrated multi-modal fusion module. Numerous tests on MS-COCO and PASCAL VOC2007 show that MSCNN-LCE-MIC significantly improves classification efficiency on mAP 84.3% and 95.8% respectively when compared to the most recent existing methods.

Political Optimizer with Deep Learning-Enabled Tongue Color Image Analysis Model

Biomedical image processing is widely utilized for disease detection and classification of biomedical images. Tongue color image analysis is an effective and non-invasive tool for carrying out secondary detection at anytime and anywhere. For removing the qualitative aspect, tongue images are quantitatively inspected, proposing a novel disease classification model in an automated way is preferable. This article introduces a novel political optimizer with deep learning enabled tongue color image analysis (PODL-TCIA) technique. The presented PODL-TCIA model purposes to detect the occurrence of the disease by examining the color of the tongue. To attain this, the PODL-TCIA model initially performs image pre-processing to enhance medical image quality. Followed by, Inception with ResNet-v2 model is employed for feature extraction. Besides, political optimizer (PO) with twin support vector machine (TSVM) model is exploited for image classification process, shows the novelty of the work. The design of PO algorithm assists in the optimal parameter selection of the TSVM model. For ensuring the enhanced outcomes of the PODL-TCIA model, a wide-ranging experimental analysis was applied and the outcomes reported the betterment of the PODL-TCIA model over the recent approaches.

A Technique for Approximate Communication in Network-on-Chips for Image Classification

Approximation is an emerging design methodology for reducing power consumption and latency of on-chip communication in many computing applications. However, existing approximation techniques either achieve modest improvements in these metrics or require retraining after approximation. Since classifying many images introduces intensive on-chip communication, reductions in both network latency and power consumption are highly desired. In this paper, we propose an approximate communication technique (ACT) to improve the efficiency of on-chip communications for image classification applications. The proposed technique exploits the error-tolerance of the image classification process to reduce power consumption and latency of on-chip communications, resulting in better overall performance for image classification. This is achieved by incorporating novel quality control and data approximation mechanisms that reduce the packet size. In particular, the proposed quality control mechanisms identify the error-resilient variables and automatically adjust the error thresholds of the variables based on the image classification accuracy. The proposed data approximation mechanisms significantly reduce packet size when the variables are transmitted. The proposed technique reduces the number of flits in each data packet as well as the on-chip communication while maintaining an excellent image classification accuracy. Cycle-accurate simulation results show that ACT achieves 23% in network latency reduction and 24% in dynamic power reduction as compared to existing approximate communication techniques with less than 0.99% classification accuracy loss.

Review on Deep Learning Approaches for Anomaly Event Detection in Video Surveillance

In the last few years, due to the continuous advancement of technology, human behavior detection and recognition have become important scientific research in the field of computer vision (CV). However, one of the most challenging problems in CV is anomaly detection (AD) because of the complex environment and the difficulty in extracting a particular feature that correlates with a particular event. As the number of cameras monitoring a given area increases, it will become vital to have systems capable of learning from the vast amounts of available data to identify any potential suspicious behavior. Then, the introduction of deep learning (DL) has brought new development directions for AD. In particular, DL models such as convolution neural networks (CNNs) and recurrent neural networks (RNNs) have achieved excellent performance dealing with AD tasks, as well as other challenging domains like image classification, object detection, and speech processing. In this review, we aim to present a comprehensive overview of those research methods using DL to address the AD problem. Firstly, different classifications of anomalies are introduced, and then the DL methods and architectures used for video AD are discussed and analyzed, respectively. The revised contributions have been categorized by the network type, architecture model, datasets, and performance metrics that are used to evaluate these methodologies. Moreover, several applications of video AD have been discussed. Finally, we outlined the challenges and future directions for further research in the field.

Hybrid DLDT algorithm based tumor classification of mammogram images

Despite the fact that current classical image classification techniques have been widely used to address real-world problems, these applications are frequently hampered by problems including disappointing results, poor classification accuracy, and constrained adaptability. This method separates the mammography image tumour feature extraction and classification processes. The effective way to improve the accuracy of mammography image tumour classification is to integrate the feature extraction and classification processes using the potent deep learning model. The deep structural advantages of multilayer nonlinear mapping and the tumour representation of well-multidimensional data linear decomposition, however, are fully utilised in this paper, which also introduces the idea of tumour representation based on shape-based feature extraction into the architecture of the deep learning network.

Deep Learning-Based BoVW–CRNN Model for Lung Tumor Detection in Nano-Segmented CT Images

One of the most common oncologies analyzed among people worldwide is lung malignancy. Early detection of lung malignancy helps find a suitable treatment for saving human lives. Due to its high resolution, greater transparency, and low noise and distortions, Computed Tomography (CT) images are most commonly used for processing. In this context, this research work mainly focused on the multifaceted nature of lung cancer diagnosis, a quintessential, fascinating, and risky subject of oncology. The input used here has been nano-image, enhanced with a Gabor filter and modified color-based histogram equalization. Then, the image of lung cancer was segmented by using the Guaranteed Convergence Particle Swarm Optimization (GCPSO) algorithm. A graphical user interface nano-measuring tool was designed to classify the tumor region. The Bag of Visual Words (BoVW) and a Convolutional Recurrent Neural Network (CRNN) were employed for image classification and feature extraction processes. In terms of findings, we achieved the average precision of 96.5%, accuracy of 99.35%, sensitivity of 97%, specificity of 99% and F1 score of 95.5%. With the proposed solution, the overall time required for the segmentation of images was much smaller than the existing solutions. It is also remarkable that biocompatible-based nanotechnology was developed to distinguish the malignancy region on a nanometer scale and has to be evaluated automatically. That novel method succeeds in producing a proficient, robust, and precise segmentation of lesions in nano-CT images.

Accuracy Assessment of Unsupervised Land Use and Land Cover Classification Using Remote Sensing and Geographical Information Systems

A significant tool for determining how accurate a categorization product is the accuracy assessment. Remote Sensing is one of the essential tools for compiling land use and land cover maps through image classification. The availability of high-quality Landsat imagery and secondary data, an accurate classification technique, and the user's knowledge and competence with the procedures essential to the image classification process. Assess the satellite image classification suitability for further mapping and analysis through accuracy assessments. This paper examined land use and land cover classification using unsupervised classification and extracted NDBI and NDVI further to support main land use and land cover types in the area. The accuracy of classifications was assessed using an error matrix and Kappa statistics. Land use and land cover, NDBI, and NDVI classification accuracies are almost perfect, further verified by the Kappa statistics tool. An excellent unsupervised classification of land use and land cover classes was generated. Accuracy assessment evaluation is one of the most significant tools for determining a classification product's accuracy. The confusion error matrix and Kappa coefficient were particularly useful in calculating accuracy assessment. Typically, accuracy measures of the unsupervised classification show a moderate accuracy level. This study observed almost perfect agreement in all types of accuracy measures. This study is an important source of information that planners and decision-makers may utilize to plan the environment sustainably.

Comparison of Convolutional Neural Network for Classifying Lung Diseases from Chest CT Images

This paper proposes a convolutional neural network for diagnosing various lung illnesses from chest CT images based on a customized Medical Image Analysis and Detection network (MIDNet18). With simplified model building, minimal complexity, easy technique, and high-performance accuracy, the MIDNet-18 CNN architecture classifies binary and multiclass medical images. Fourteen convolutional layers, 7 pooling layers, 4 dense layers, and 1 classification layer comprise the MIDNet-18 architecture. The medical image classification process involves training, validating, and testing the MIDNet-18 model. In the Lung CT image binary class dataset, 2214 images as training set, 1800 images as validation set, and 831 as test set are considered for classifying COVID images and normal lung images. In the multiclass dataset, 6720 images as training sets belonging to 3 classes, 3360 images as validation sets and 601 images as test sets are considered for classifying COVID, cancer images and normal images. Independent sample size calculated for binary classification is 26 samples for each group. Similarly, 10 sample sizes are calculated for multiclass dataset classification keeping GPower at 80%. To validate the performance of the MIDNet18 CNN architecture, the medical images of two different datasets are compared with existing models like LeNet-5, VGG-16, VGG-19, ResNet-50. In multiclass classification, the MIDNet-18 architecture gives better training accuracy and test accuracy, while the LeNet5 model obtained 92.6% and 95.9%, respectively. Similarly, VGG-16 is 89.3% and 77.2% respectively; VGG-19 is 85.8% and 85.4%, respectively; ResNet50 is 90.6% and 99%, respectively. For binary classification, the MIDNet18 architecture gives better training accuracy and test accuracy, while the LeNet-5 model has obtained 52.3% and 54.3%, respectively. Similarly, VGG 16 is 50.5% and 45.6%, respectively; VGG-19 is 50.6% and 45.6%, respectively; ResNet-50 is 96.1% and 98.4%, respectively. The classified images are further predicted using detectron-2 model and the results identify abnormalities (cancer, COVID-19) with 99% accuracy. The MIDNET18 is significantly more accurate than LeNet5, VGG19, VGG16 algorithms and is marginally better than the RESNET50 algorithm for the given lung binary dataset (Bonferroni — one-way Anova and pairwise comparison of MIDNET, LeNet5, VGG19, VGG16, and RESNET 50 ([Formula: see text])). The proposed MIDNet18 model is significantly more accurate than LeNet5, VGG19, VGG16, ResNet50 algorithms in classifying the diseases for the given multiclass lung dataset (Bonferroni — one-way Anova and pairwise comparison of MIDNET18, LeNet5, VGG19, VGG16, ResNet50 ([Formula: see text])).

Valuing cultural ecosystem services combining deep learning and benefit transfer approach

The importance placed by policy-makers on cultural ecosystem services (CES) is on the rise worldwide, inducing a growing interest in the development of innovative approaches for their mapping, characterization, and assessment. Previous studies have shown how the analysis of the content of geotagged images shared on social media can be used to shed a light on CES provision and consumption without having to resort to time-consuming surveys and interviews. However, to make this approach viable over large study areas, the image classification process must be automatized and new developments of deep learning field have the potential to do so. To evaluate this potential, we have gathered over 91,000 images acquired within British Columbia (Canada) provincial parks system and then trained and deployed convolutional neural networks (CNN) to classify them. Images were classified based on: (i) their relevance in the context of CES research; (ii) the type of CES depicted (aesthetic or recreation); and (iii) the type of aesthetic experience or recreational activity depicted; with an estimated accuracy of 85 %. Then we combined the outcomes of the image classification process with images metadata to assess the value of the recreational ecosystem service provided by the most popular parks with an innovative crowdsourced benefit transfer approach, obtaining values that range from the 105.2 M CAN$ of Cypress park to the 0.3 M CAN$ of Lac du Bois park. Our findings show how crowdsourced social media images, coupled with the new developments of deep learning, offer to ecosystem managers across the world the opportunity of gathering cheap, fine scale, and comprehensive information about CES. Finally, we address the limitations and future perspectives of automated image classification for CES research.

Estimation of Breast Cancer with a Combined Feature Selection Algorithm

Image features are considered as a parametric factor that contains some of the specific information about the given image. In simple terms, a feature can be either a size or resolution or color information of an image. From the observed feature, a computer system can predict the nature of the image same as that of a human’s perception. In the beginning, the image processing algorithms utilized the features of the image only for the preprocessing and segmentation kinds of applications. An information regarding the noise ratio is considered for the preprocessing work to estimate the amount of smoothness needed to be given to the image. Similarly, the contrast difference or color difference features are widely employed by the segmentation algorithms. The proposed work aims to prove the efficacy of features on breast cancer image classification process using a multilayer perceptron algorithm. An experimental study is conducted on CBIS-DDSM dataset to estimate the importance of local and global features on breast cancer images.

Histogram of Oriented Gradients (HOG)-Based Artificial Neural Network (ANN) Classifier for Glaucoma Detection

Glaucoma is a severe condition of the optic nerve resulting in the loss of eyesight. The proposed methodology has introduced the extraction of HOG (histogram of oriented gradients) features from the retinal fundus image. After the removal of HOG features, the authors compare the performance of five different machine learning techniques like k-nearest neighbour (KNN), support vector machine (SVM), linear discriminant analysis (LDA), naïve bayes, and artificial neural network. The process of image classification is based on analyzing the numerical properties of the obtained image features and classifying the data into different categories. In the paper, the authors intend to classify whether the image belongs to the glaucomatous category or the healthy category. After the application of the different classification algorithms to the test data and further analysis of the results, they could conclude that the SVM classifier provided an accuracy of 90%, KNN 86%, Naïve Bayes 96%, LDA 86%, and ANN 96.90% on the dataset in hand.

Adaptasi Model CNN Terlatih pada Aplikasi Bergerak untuk Klasifikasi Citra Termal Payudara

The model development for breast thermal image classification can be done using deep learning methods, especially the convolutional neural network (CNN) architecture. This article focuses on adapting a trained CNN (trained model) on a mobile application for binary classification of breast thermal images into normal and abnormal classes. The CNN model applied in this study was based on ShuffleNet, called BreaCNet, with a learning weight of 1028 filters generated from training on images downloaded from the Database for Mastology Research (DMR) and a model size of 22 MB. The model must be converted into a mobile application to enable a trained model to be adapted into a mobile platform. The BreaCNet model was built using MatLab; thus, the stages in the adaptation process consisted of converting the model into ONNX file format, converting ONNX files into Tensorflow files, and Tensorflow files into Tensorflow Lite format. However, not all nodes are fully supported by MATLAB. The shuffle node on ShuffleNet cannot be fully exported using ExportToOnnx, so it needs to be re-defined with a placeholder named “MATLAB PLACEHOLDER”. In addition to the model conversion process, this article describes the user interaction process with the application using UML diagrams and application feature menu designs. The application was also tested on 20 thermal images of the breast. The testing results show that the application can perform the image classification process on mobile devices in less than 1 second with an accuracy rate of 85%. Finally, the breast thermal image screening application has been successfully built by directly interpreting the thermal image of the breast on a mobile device to keep the user data private.

WORKSHOP MACHINE LEARNING KLASIFIKASI TUMOR OTAK PADA CITRA MRI MENGGUNAKAN CONVOLUTIONAL NEURAL NETWORK DAN SUPPORT VECTOR MACHINE

The brain is one of the organs that very important role for humans. Brain tumors can be a threat for humans. So that The researchers developed a CNN method that was tested effective for detecting brain tumors. CNN is a method that is quite popular, in its application it is used for image classification and several other image processing cases. CNN can be used to detect and recognize objects in an image better on an Artificial Neural Network. In addition, many researchers also use the SVM method, SVM can be applied to perform pattern recognition in the case of image processing. Brain tumors can be caused by the spread of cancer in parts of the other body. According to a report by the World Health Organization (WHO) brain cancer accounts for less than 2% of other cancers, but the severe morbidity and resulting complications are enormous. Brain cancer requires multidisciplinary treatment, so a professional standard policy is needed for optimal treatment. This activity proposes a Machine Learning Workshop on Brain Tumor Classification in MRI Imagery using CNN and SVM, in CNN activities can be divided into several parts such as CNN modeling, data preprocessing, building, and implementing CNN models in The SVM teaches how to build a hyperplane. This activity was delivered by expert speakers in their fields from alumni of the University of Muhammadiyah Malang.

Spectral classification of space agricultural landscape images using ranking of channels according to the highest informativity

This paper explores the question of classification of a series of space images for analysis of the processes of growing crops, using the spectral channels of minimum dimension. This study attempts to give a partial answer to the question: how to detect the problems in growing processes much faster and with a high level of accuracy? In this paper, we report on the results of the survey conducted to determine the existing methods and tools for spectral analysis of multispectral space images for solving forecasting and search problems of mapping the agricultural objects. The present work extends the use of the general image classification process by the computational procedure of ranking spectral channels according to the value of spectral information. The structure of the classification rule based on the informativity indicator of the spectral channel has been developed. The proposed prototype of a geospatial recognition system will provide a digital stratification landscape plan.