Low-cost system for multispectral image acquisition and its applicability to analysis of the physiological potential of soybean seeds
Júlia Martins SoaresAndré Dantas de MedeirosDaniel Teixeira PinheiroJorge Tadeu Fim RosasLaércio Junio da SilvaDaniel Lucas Magalhães MachadoDenise Cunha Fernandes dos Santos Dias
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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.Keywords:
Multispectral pattern recognition
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In this paper, a new multispectral image wavelet representation is introduced, based on multiscale fundamental forms. This representation describes gradient information of multispectral images in a multiresolution framework. The representation is in particular extremely suited for the fusion and merging of multispectral images. For fusion as well as for merging, a strategy is described. Experiments are performed on multispectral images. In these experiments, Landsat Thematic Mapper images are fused and merged with panchromatic images. The proposed techniques are compared to wavelet-based techniques described in the literature.
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Multispectral remote sensing is one of the most popular techniques in the earth observation, because this technique can provide information of ground objects on Earth’s surface using hundreds of narrow bands. However, multispectral images produces a very large volume of data. Processing the huge volume of information is one of most important and actual problems of remote sensing. The rapid development of the remote sensing demand to develop the data processing algorithms. But at present data processing techniques cannot give accurate results. If we use traditional methods to process multispectral images, the volume of the data increases. The main goal of the band selection is to choose the optimal combination of spectral bands for the solution of the particular remote sensing task. This process is important because different bands are sensitive to different objects. Selecting the right bands can help to optimize the detection of different ground objects. Some spectral bands are more sensitive to minerals, while others are more sensitive to vegetation or water bodies. Under a small number of training samples, the classification accuracy of multispectral images decreases when the volume of multispectral data increases. Usually adjacent bands are highly correlated, and some spectral bands may not carry unique information. That’s why it is necessarily to reduce the dimensionality of multispectral data. It helps to store, process, transmit information more efficiently and to reduce the computational costs while processing images. The different modern methods of multispectral band selection are also considered and analyzed in this work. It also is proposed a new method to select spectral bands, which is based on the concept of criterion function of information capability of spectral bands. In this article some examples using criterion function of information capability are considered too.
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An innovative approach to spectral pattern recognition for multispectral images based on genetic programming is introduced. The problem is faced in terms of unsupervised pixel classification. The system is tested on a multispectral image with 31 spectral bands and 256-256 pixels. A good quality clustered output image is obtained.
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Multispectral data is obtained using special sensors that measure reflected or emitted energy in certain spectral ranges. This data represents information about various properties of objects, such as color, texture, structure, and more. Various methods of multispectral data processing can be used to detect buried objects. The article deals with the issues of image segmentation using deep learning, in order to obtain estimates of the thermophysical parameters of hidden subsurface objects, the coefficient inverse problem of thermal conductivity was solved using a genetic algorithm.
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A new multispectral image wavelet representation is introduced, based on multiscale fundamental forms. This representation describes gradient information of multispectral images in a multiresolution framework. The representation is, in particular, extremely suited for fusion and merging of multispectral images. For fusion as well as for merging, a strategy is described. Experiments are performed on multispectral images, where Landsat Thematic Mapper images are fused and merged with SPOT Panchromatic images. The proposed techniques are compared with wavelet-based techniques described in the literature.
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Compressive sensing of noisy multispectral images is considered in this letter. Multispectral images in remote sensing applications are multichannel and inherently noisy. An approach using Bregman split method for optimization in both spatial and transform domains is proposed. The performance of the proposed algorithm is evaluated by comparing with other approaches. It is shown that the proposed algorithm performs favorably compared with other approaches with noisy multispectral images in experiments.
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Many remote-sensing satellites can obtain images in multispectral and panchromatic bands. By fusing low-resolution multispectral and high-resolution panchromatic images, one can obtain high-resolution multispectral images. In this paper, an image fusion algorithm based on image restoration is proposed to combine multispectral and panchromatic images. For remote-sensing satellites, the wavelength of the panchromatic band usually covers the wavelengths of the multispectral bands. This relationship between the two kinds of images is useful for fusion. In our approach, the low-resolution multispectral images are first resampled to the scale of the high-resolution panchromatic image. The relationship between these two kinds of images is then used to restore the resampled multispectral images. That is, the resampled multispectral images are modeled as the noisy blurred versions of the ideal multispectral images, and the high-resolution panchromatic image is modeled as a linear combination of the ideal multispectral images plus the observation noise. The ideal high-resolution multispectral images are then estimated based on the panchromatic and the resampled multispectral images. A closed-form solution of the fused images is derived here. Experiments show that the proposed fusion algorithm works effectively in integrating multispectral and panchromatic images.
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This research proposes the multispectral image retrieval method by using spectral feature and semantic computing which is not many studies have focused. The main contributions are to enhance the effectiveness and advantageous of global environmental analysis system and realize semantic associative search and analysis. In this work, we study multispectral image retrieval using spectral feature computed in multispectral semantic-image space. The multispectral semantic-image space is supposing to realize the interpretation of substance (materials) on earth surface which can be provided the analyzed results as human-level interpretation. Our essential approach is utilizing the semantic computing to measure the similarity between multispectral image and the meaningful keywords which according to the user's contexts. Our research results found that this method possible to acquire the spectral feature from the multispectral image and could be used in multispectral image retrieval. In this study, a multispectral image is used as the image query according to user's query contexts. Moreover, the method performance of UAV-based multispectral aerial image retrieval using spectral feature and semantic computing is measured based on the queries with three contexts of multispectral image which is indicated by previous study on agricultural monitoring system and semantic interpretation model.
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