Use Of Multispectral Images Research Articles

О ПРИМЕНЕНИИ СПЕКТРОЗОНАЛЬНОЙ ВИЗУАЛИЗАЦИИ В ДИСТАНЦИОННОМ ЗОНДИРОВАНИИ ЗЕМЛИ

The issues of using multispectral imaging in remote sensing of the Earth, both using satellite images and using unmanned aerial vehicles (UAVs), are discussed. The use of multispectral images for assessing reforestation, the effectiveness of herbicide use, reconstruction of archaeological sites, environmental monitoring of the surface of reservoirs by monitoring the extent of the spread of blue-green algae, and recognition of the road network in forestry is described. The feasibility of using UAVs to survey individual areas of the terrain instead of obtaining images from satellites is assessed. The results of increasing the information content of multispectral images obtained using various processing options are presented. Based on the results of the analytical review, trends in the development of methods for improving the visual quality of multispectral images obtained during monitoring of the earth's surface are formulated. The disadvantages of the considered methods for increasing the information content of multispectral images are indicated, and improvements are proposed that can improve the methods used.

SNDVI: a new scalable serverless framework to compute NDVI

Farmers and agronomists require crop health metrics to monitor plantations and detect problems like diseases or droughts at an early stage. This enables them to implement measures to address crop problems. The use of multispectral images and cloud computing is conducive to obtaining such metrics. Drones and satellites capture extensive multispectral image datasets, while the cloud facilitates the storage of these images and provides execution services for extracting crop health metrics, such as the Normalized Difference Vegetation Index (NDVI). The use of the Cloud to compute NDVI poses new research challenges, such as determining which cloud technology offers the optimal balance of execution time and monetary cost. In this article, we present Serverless NDVI (SNDVI), a new framework based on serverless computing for NDVI computation. The objective of SNDVI is to minimize the monetary costs and computing times associated with using a Public Cloud while processing NDVI from large datasets. One of SNDVI's key contributions is to crop the dataset into subsegments to leverage Lambda's ability to run up to 1,000 NDVI computing functions in parallel on each subsegment. We deployed SNDVI using Amazon Lambda and conducted two experiments to analyze and validate its performance. Both experiments focused on two key metrics: (i) execution time and (ii) monetary costs. The first experiment involved executing SNDVI to extract NDVI from a multispectral dataset. The objective was to evaluate the overall SNDVI functionality, assess its performance, and verify the quality of SNDVI output. In the second experiment, we conducted a benchmarking analysis comparing SNDVI with an EC2-based NDVI computing architecture. Results from the first experiment demonstrated that the processing times for the entire SNDVI execution ranged from 9 to 15 seconds, with a total cost (including storage) of 4.19 USD. Results from the second experiment revealed that the monetary costs of EC2 and Lambda were similar, but the computing time for SNDVI was 411 times faster than the EC2 architecture. In conclusion, the investigation reported in this paper demonstrates that SNDVI successfully achieves its goals and that Serverless Computing presents a promising native serverless alternative to traditional cloud services for NDVI computation.

Multispectral Food Classification and Caloric Estimation Using Convolutional Neural Networks.

Continuous monitoring and recording of the type and caloric content of ingested foods with a minimum of user intervention is very useful in preventing metabolic diseases and obesity. In this paper, automatic recognition of food type and caloric content was achieved via the use of multi-spectral images. A method of fusing the RGB image and the images captured at ultra violet, visible, and near-infrared regions at center wavelengths of 385, 405, 430, 470, 490, 510, 560, 590, 625, 645, 660, 810, 850, 870, 890, 910, 950, 970, and 1020 nm was adopted to improve the accuracy. A convolutional neural network (CNN) was adopted to classify food items and estimate the caloric amounts. The CNN was trained using 10,909 images acquired from 101 types. The objective functions including classification accuracy and mean absolute percentage error (MAPE) were investigated according to wavelength numbers. The optimal combinations of wavelengths (including/excluding the RGB image) were determined by using a piecewise selection method. Validation tests were carried out on 3636 images of the food types that were used in training the CNN. As a result of the experiments, the accuracy of food classification was increased from 88.9 to 97.1% and MAPEs were decreased from 41.97 to 18.97 even when one kind of NIR image was added to the RGB image. The highest accuracy for food type classification was 99.81% when using 19 images and the lowest MAPE for caloric content was 10.56 when using 14 images. These results demonstrated that the use of the images captured at various wavelengths in the UV and NIR bands was very helpful for improving the accuracy of food classification and caloric estimation.

Yield Prediction of Four Bean (Phaseolus vulgaris) Cultivars Using Vegetation Indices Based on Multispectral Images from UAV in an Arid Zone of Peru

In Peru, common bean varieties adapt very well to arid zones, and it is essential to strengthen their evaluations accurately during their phenological stage by using remote sensors and UAV. However, this technology has not been widely adopted in the Peruvian agricultural system, causing a lack of information and precision data on this crop. Here, we predicted the yield of four beans cultivars by using multispectral images, vegetation indices (VIs) and multiple linear correlations (with 11 VIs) in 13 different periods of their phenological development. The multispectral images were analyzed with two methods: (1) a mask of only the crop canopy with supervised classification constructed with QGIS software; and (2) the grids corresponding to each plot (n = 48) without classification. The prediction models can be estimated with higher accuracy when bean plants reached maximum canopy cover (vegetative and reproductive stages), obtaining higher R2 for the c2000 cultivar (0.942) with the CIG, PCB, DVI, EVI and TVI indices with method 2. Similarly, with five VIs, the camanejo cultivar showed the highest R2 for both methods 1 and 2 (0.89 and 0.837) in the reproductive stage. The models better predicted the yield in the phenological stages V3–V4 and R6–R8 for all bean cultivars. This work demonstrated the utility of UAV tools and the use of multispectral images to predict yield before harvest under the Peruvian arid ecosystem.

Data acquisition for testing potential detection of Flavescence dorée with a designed, affordable multispectral camera

There is a constant push on agriculture to produce more food and other inputs for different industries. Precision agriculture is essential to meet these demands. The intake of this modern technology is rapidly increasing among large and medium-sized farms. However, small farms still struggle with their adaptation due to the expensive initial costs. A contribution in handling this challenge, this paper presents data gathering for testing an in-house made, cost-effective, multispectral camera to detect Flavescence dorée (FD). FD is a grapevine disease that, in the last few years, has become a major concern for grapevine producers across Europe. As a quarantine disease, mandatory control procedures, such as uprooting infected plants and removing all vineyard if the infection is higher than 20%, lead to an immense economic loss. Therefore, it is critical to detect each diseased plant promptly, thus reducing the expansion of Flavescence dorée. Data from two vineyards near Riva del Garda, Trentino, Italy, was acquired in 2022 using multispectral and hyperspectral cameras. The initial finding showed that there is a possibility to detect Flavescence dorée using Linear discriminant analysis (LDA) with hyperspectral data, obtaining an accuracy of 96.6 %. This result justifies future investigation on the use of multispectral images for Flavescence dorée detection.

Low-cost system for multispectral image acquisition and its applicability to analysis of the physiological potential of soybean seeds

The use of multispectral images has great potential to assess seed quality and represents a significant technological advance in the search for fast and non-destructive analysis techniques. However, the devices currently available are expensive. Thus, this study aimed to propose a low-cost method for acquisition and processing of multispectral images of soybean seeds and to evaluate their potential for rapid determination of seed physiological potential. The study was conducted in three steps: implementation of the multispectral image acquisition system, development of an algorithm for automatic image processing, and evaluation of the relationship between the data obtained through image analysis and the results of standard tests used to evaluate seed physiological potential. A total of 43 variables were assessed, eight related to seed physiological potential (germination and vigor) and 35 obtained from the analysis of the multispectral images. Of the variables obtained from multispectral images, 21 were related to pixel values in the images in the different bands evaluated (green, red, and infrared) and 14 associated with seed morphometric characteristics. The proposed system is efficient in obtaining multispectral images and the algorithm developed was efficient to extract morphometric characteristics and pixel information from the images. The parameters obtained from the NIR spectrum region showed a good relationship with the physiological potential of soybean seeds.

APPLICATION OF MULTISPECTRAL IMAGES TO SEARCH FOR CONSTRUCTION OBJECTS ON THE SPECTRAL SIGNATURES BASE

The work is devoted to the study of Landsat-8 multispectral images of not high resolution using the spectral angle method on the base of spectral signatures libraries to detect objects under construction in an urban area. The physical basis of the research method is that all objects have different reflection coefficients depending on the wavelength. This property makes it possible to identify various substances by their spectral signatures. In the work, an automatic comparison of the curves of the spectral reflectivity of objects on a low resolution space multispectral image was made to identify the identity of the characteristic energy absorption and reflection zones for detecting objects in the construction process. The article also describes the stages of image preprocessing, cross-track illumination correction of the image, atmospheric correction, and mathematical operations of bands transformation, which provide more opportunities for analysis and recognition of objects using a spectral study of a space image. The study accurately determines the presence or absence of the desired materials, since the search is based on the molecular structure of the substance. Also, the use of multispectral images allows you to analyze the entire city at the same time. The initial data was taken from a 2021 Landsat-8 satellite image with 11 bands, with a resolution of 30 meters, which was enhanced to 15 meters during pre-processing. The results of the search and detection of objects under construction in the city are given. The detection results can be used as input data for further in-depth analysis.

Multispectral Facial Recognition in the Wild.

This work proposes a multi-spectral face recognition system in an uncontrolled environment, aiming to identify or authenticate identities (people) through their facial images. Face recognition systems in uncontrolled environments have shown impressive performance improvements over recent decades. However, most are limited to the use of a single spectral band in the visible spectrum. The use of multi-spectral images makes it possible to collect information that is not obtainable in the visible spectrum when certain occlusions exist (e.g., fog or plastic materials) and in low- or no-light environments. The proposed work uses the scores obtained by face recognition systems in different spectral bands to make a joint final decision in identification. The evaluation of different methods for each of the components of a face recognition system allowed the most suitable ones for a multi-spectral face recognition system in an uncontrolled environment to be selected. The experimental results, expressed in Rank-1 scores, were 99.5% and 99.6% in the TUFTS multi-spectral database with pose variation and expression variation, respectively, and 100.0% in the CASIA NIR-VIS 2.0 database, indicating that the use of multi-spectral images in an uncontrolled environment is advantageous when compared with the use of single spectral band images.

Operational use of multispectral images for macro-litter mapping and categorization by Unmanned Aerial Vehicle

The use of Unmanned Aerial Systems (UAS, aka drones) has shown to be feasible to perform marine litter surveys. We operationally tested the use of multispectral images (5 bands) to classify litter type and material on a beach-dune system. For litter categorization by their multispectral characteristics, the Spectral Angle Mapping (SAM) technique was adopted.The SAM-based categorization of litter agreed with the visual classification, thus multispectral images can be used to fasten and/or making more robust the manual RGB image screening. Fully automated detection returned an F-score of 0.64, and a reasonable categorization of litter. Overall, the image-based litter density maps were in line with the manual detection. Assessments were promising given the complexity of the study area, where different dunes plants and partially-buried items challenged the UAS-based litter detection. The method can be easily implemented for both floating and beached litter, to advance litter survey in the environment.

Integrated NIR-HE based SPOT-5 image enhancement method for features preservation and edge detection

Recently, many researchers have directed their attention to methods of predicting shorelines by the use of multispectral images. Thus, a simple and optimised method using image enhancements is proposed to improve the low contrast of the Satellite pour l'Observation de la Terre-5 (SPOT-5) images in the detection of shorelines. The near-infrared (NIR) channel is important in this study to ensure the contrast of the vegetated area and sea classification, due to the high reflectance of leaves in the near infrared wavelength region. This study used five scenes of interest to show the different results in shoreline detection. The results demonstrated that the proposed method performed in an enhanced manner as compared to current methods when dealing with the low contrast ratio of SPOT-5 images. As a result, by utilising the near-infrared histogram equalization (NIR-HE), the contrast of all datasets was efficiently restored, producing a higher efficiency in edge detection, and achieving higher overall accuracy. The improved filtering method showed significantly better shoreline detection results than the other filter methods. It was concluded that this method would be useful for detecting and monitoring the shoreline edge in Tanjung Piai.

Dinámica de la vegetación y variabilidad pluviométrica en la cuenca del río Portoviejo.

Climate change is currently a global problem, as it significantly affects the dynamics of vegetation. The objective of this research is to analyze the influence of climate variability on the dynamics of vegetation in the Portoviejo river basin. The methodology consisted in the calculation of the NDVI with the use of multispectral images from the Landsat satellite and the analysis of the fluviometric records. The images were downloaded from the Earth Explorer geospatial platform with a spatial resolution of 30 m, images from the period 1998-2019 were selected to analyze their temporal trend. The historical fluviometric records of the Portoviejo station, of the National Institute of Hydrology and Meteorology were used. The results showed that the vegetation experienced a discontinuous greening trend, influenced by the variability of rainfall. A homogeneous trend was found in the spatial distribution of the NDVI, with dense and very dense vegetation cover in the upper part of the basin, as well as little or no cover in the lower part. The area with the greatest coverage corresponded to dense vegetation with a percentage higher than 30%.

Use of multispectral images to evaluate the efficacy of pre-emergent herbicides in peas under greenhouse conditions

In Colombia, peas are the second most important legume after the bean, and weeds are the main biotic factor that limits production, causing losses of up to 100%. Manual control can represent up to 40% of the labor force. The critical period in the crop-weed competition is the first third of the crop cycle; therefore, pre-emergent herbicide applications are a cost-effective way to control weeds. Common variables for assessing weed-control efficacy include, weed density (individuals/area), which is precise but time consuming, and weed coverage (%), which is faster but very subjective. Therefore, pre-emergence herbicides and a weed-control evaluation method that standardizes, facilitates, and provides greater precision are needed for peas cultivation and experimentation. Five pre-emergent herbicides (linuron, S-metolachlor, metribuzine, oxifluorfen and pendimetalin) were evaluated at two doses in a greenhouse pea crop. Also, two methods (quantification process of multispectral images and conventional human visual) for assessing weed coverage and control efficacy were compared. The best herbicide treatment for the dry grain yield was metribuzine (2.36 t ha-1). Furthermore, the effectiveness of the weed control was 88% at 36 days after sowing, which is optimal. Finally, there was agreement between the weed assessment methods (human vs. machine). The intraclass correlation coefficient was over 0.95, which validates the use of machine quantification for weed coverage.

ПЕРСПЕКТИВЫ ПРИМЕНЕНИЯ ДАННЫХ МУЛЬТИСПЕКТРАЛЬНОГО ЗОНДИРОВАНИЯ ЗЕМЛИ ПРИ ПРОГНОЗИРОВАНИИ И ПОИСКАХ ПЛАСТОВО-ИНФИЛЬТРАЦИОННЫХ МЕСТОРОЖДЕНИЙ УРАНА

Reservoir-infiltration uranium deposits («sandstone» type) today are the main active source of uranium mineral raw materials in the world and the only one in Kazakhstan. Their main advantage in the form of better environmental friendliness, productivity and minimum production costs creates prospects for their further detection in various parts of the world. It is important to simplify and improve multi-stage, expensive and difficult geological exploration works for the purpose of forecasting and searching them with affordable innovative solutions. The available multispectral satellite imagery has opened up new opportunities for the study of uranium ore provinces. Mapping of uranium ore provinces based on multispectral satellite imagery allows them to be compared with certain key ore-controlling data from geological and geophysical studies. The near-surface visible nature of geotectonic structures, climatic conditions and zonal anomalies are more easily and efficiently visualized using modern space technologies and computer solutions. The explanation of the derived correlations with the geotectonic and climatic conditions allows the use of multispectral images in order to simplify and improve the quality of forecasting, prospecting and exploration of reservoir-infiltration uranium deposits. More advanced aerial and space remote sensing methods make it possible to detect surface anomalies associated with this type of ore. The scientific explanation of the nature of these anomalies and their role in the geological and genetic model of ore formation creates a solid theoretical basis for improving the exploration methodology. The convergence of the results obtained, their theoretical explanation, simplicity and convincingness of the results make it possible to make new predictions of promising areas of reservoir-infiltration uranium regions for several of the key ore-controlling factors and use this methodology in conjunction with other data from regional and local studies at all stages of exploration.

OBJECT-BASED ANALYSIS OF LIDAR GEOMETRIC FEATURES FOR VEGETATION DETECTION IN SHADED AREAS

Abstract. The extraction of land cover information from remote sensing data is a complex process. Spectral information has been widely utilized in classifying remote sensing images. However, shadows limit the use of multispectral images because they result in loss of spectral radiometric information. In addition, true reflectance may be underestimated in shaded areas. In land cover classification, shaded areas are often left unclassified or simply assigned as a shadow class. Vegetation indices from remote sensing measurement are radiation-based measurements computed through spectral combination. They indicate vegetation properties and play an important role in remote sensing of forests. Airborne light detection and ranging (LiDAR) technology is an active remote sensing technique that produces a true orthophoto at a single wavelength. This study investigated three types of geometric lidar features where NDVI values fail to represent meaningful forest information. The three features include echo width, normalized eigenvalue, and standard deviation of the unit weight observation of the plane adjustment, and they can be derived from waveform data and discrete point clouds. Various feature combinations were carried out to evaluate the compensation of the three lidar features to vegetation detection in shaded areas. Echo width was found to outperform the other two features. Furthermore, surface characteristics estimated by echo width were similar to that by normalized eigenvalues. Compared to the combination of only NDVI and mean height difference, those including one of the three features had a positive effect on the detection of vegetation class.

OBJECT-BASED ANALYSIS OF LIDAR GEOMETRIC FEATURES FOR VEGETATION DETECTION IN SHADED AREAS

The extraction of land cover information from remote sensing data is a complex process. Spectral information has been widely utilized in classifying remote sensing images. However, shadows limit the use of multispectral images because they result in loss of spectral radiometric information. In addition, true reflectance may be underestimated in shaded areas. In land cover classification, shaded areas are often left unclassified or simply assigned as a shadow class. Vegetation indices from remote sensing measurement are radiation-based measurements computed through spectral combination. They indicate vegetation properties and play an important role in remote sensing of forests. Airborne light detection and ranging (LiDAR) technology is an active remote sensing technique that produces a true orthophoto at a single wavelength. This study investigated three types of geometric lidar features where NDVI values fail to represent meaningful forest information. The three features include echo width, normalized eigenvalue, and standard deviation of the unit weight observation of the plane adjustment, and they can be derived from waveform data and discrete point clouds. Various feature combinations were carried out to evaluate the compensation of the three lidar features to vegetation detection in shaded areas. Echo width was found to outperform the other two features. Furthermore, surface characteristics estimated by echo width were similar to that by normalized eigenvalues. Compared to the combination of only NDVI and mean height difference, those including one of the three features had a positive effect on the detection of vegetation class.

Combined use of LiDAR data and multispectral earth observation imagery for wetland habitat mapping

Although wetlands play a key role in controlling flooding and nonpoint source pollution, sequestering carbon and providing an abundance of ecological services, the inventory and characterization of wetland habitats are most often limited to small areas. This explains why the understanding of their ecological functioning is still insufficient for a reliable functional assessment on areas larger than a few hectares. While LiDAR data and multispectral Earth Observation (EO) images are often used separately to map wetland habitats, their combined use is currently being assessed for different habitat types. The aim of this study is to evaluate the combination of multispectral and multiseasonal imagery and LiDAR data to precisely map the distribution of wetland habitats. The image classification was performed combining an object-based approach and decision-tree modeling. Four multispectral images with high (SPOT-5) and very high spatial resolution (Quickbird, KOMPSAT-2, aerial photographs) were classified separately. Another classification was then applied integrating summer and winter multispectral image data and three layers derived from LiDAR data: vegetation height, microtopography and intensity return. The comparison of classification results shows that some habitats are better identified on the winter image and others on the summer image (overall accuracies=58.5 and 57.6%). They also point out that classification accuracy is highly improved (overall accuracy=86.5%) when combining LiDAR data and multispectral images. Moreover, this study highlights the advantage of integrating vegetation height, microtopography and intensity parameters in the classification process. This article demonstrates that information provided by the synergetic use of multispectral images and LiDAR data can help in wetland functional assessment

A Color Coordinate Normalizer Chip

The use of multispectral images of the same object considerably increases the opportunity for unique selection of features. The use of color information has been receiving growing attention. This paper presents the design and VLSI implementation of a new ASIC, which performs real-time conversion of the raw RGB data into the rgb normalized color coordinates. The high speed of operation is achieved by pipelining the data in a vector fashion. Eight-bit color images have been used, since this resolution is adequate for encoding the composite video signal without noticeable degradation. The inputs to the circuit are the RGB data obtained from a color sensor and digitized through three flash ADCs. The design has been implemented using the CADENCE VLSI CAD tool. The die size dimensions for the core of the chip are 1.87 mm × 1. 80 mm e 3. 37 mm2, for a DLM, 0.7 mm, N-well, CMOS technology and its maximum frequency of operation is 30 MHz. The ASIC is intended to be used in real-time pattern recognition applications, such as robotics, military systems, food, printing, pharmaceutical and agricultural industries. Real-time techniques are important not only in terms of improving productivity, but also in reducing operator errors associated with visual feedback delays.

Analysis of Multispectral Microscope Images Using Neural Networks

In this study, neural networks have been used to classify the clusters in a scatter diagram formed from images in a multispectral set. The use of multispectral images in surface and materials science has been applied to a number of techniques in recent years. The use of neural networks to analyse multispectral Landsat images of the Earth has already been reported, but has not yet been applied in other fields employing multispectral images. The work reported here used two different neural networks : a three-layer neural net with back propagation and a self-organizing map. The results suggest that neural nets can provide a useful addition to the current multispectral analysis procedures in materials and surface science.