A Systematic Literature Review of Machine Learning and Deep Learning Approaches for Spectral Image Classification in Agricultural Applications Using Aerial Photography

Recently,there has been a notable surge of interest in scientific research regarding spectral images.The potential of these images to revolutionize the digital photography industry,like aerial photography through Unmanned Aerial Vehicles(UAVs),has captured considerable attention.One encouraging aspect is their combination with machine learning and deep learning algorithms,which have demonstrated remarkable outcomes in image classification.As a result of this powerful amalgamation,the adoption of spectral images has experienced exponential growth across various domains,with agriculture being one of the prominent beneficiaries.This paper presents an extensive survey encompassing multispectral and hyperspectral images,focusing on their applications for classification challenges in diverse agricultural areas,including plants,grains,fruits,and vegetables.By meticulously examining primary studies,we delve into the specific agricultural domains where multispectral and hyperspectral images have found practical use.Additionally,our attention is directed towards utilizing machine learning techniques for effectively classifying hyperspectral images within the agricultural context.The findings of our investigation reveal that deep learning and support vector machines have emerged as widely employed methods for hyperspectral image classification in agriculture.Nevertheless,we also shed light on the various issues and limitations of working with spectral images.This comprehensive analysis aims to provide valuable insights into the current state of spectral imaging in agriculture and its potential for future advancements.

Multispectral image compression and encryption method based on tensor decomposition in wavelet domain

Multispectral image compression and encryption algorithms commonly suffer from issues such as low compression efficiency,lack of synchronization between the compression and encryption proces-ses,and degradation of intrinsic image structure.A novel approach is proposed to address these is-sues.Firstly,a chaotic sequence is generated using the Lorenz three-dimensional chaotic mapping to initiate the encryption process,which is XORed with each spectral band of the multispectral image to complete the initial encryption of the image.Then,a two-dimensional lifting 9/7 wavelet transform is applied to the processed image.Next,a key-sensitive Arnold scrambling technique is employed on the resulting low-frequency image.It effectively eliminates spatial redundancy in the multispectral image while enhancing the encryption process.To optimize the compression and encryption processes further,fast Tucker decomposition is applied to the wavelet sub-band tensor.It effectively removes both spectral redundancy and residual spatial redundancy in the multispectral image.Finally,the core tensor and pattern matrix obtained from the decomposition are subjected to entropy encoding,and real-time chaotic encryption is implemented during the encoding process,effectively integrating compression and encryption.The results show that the proposed algorithm is suitable for occasions with high requirements for compression and encryption,and it provides valuable insights for the de-velopment of compression and encryption in multispectral field.

MULTI-SPECTRAL AND HYPERSPECTRAL IMAGE FUSION USING 3-D WAVELET TRANSFORM

Image fusion is performed between one band of multi-spectral image and two bands of hyperspectral image to produce fused image with the same spatial resolution as source multi-spectral image and the same spectral resolution as source hyperspeetral image. According to the characteristics and 3-Dimensional （3-D） feature analysis of multi-spectral and hyperspectral image data volume, the new fusion approach using 3-D wavelet based method is proposed. This approach is composed of four major procedures： Spatial and spectral resampling, 3-D wavelet transform, wavelet coefficient integration and 3-D inverse wavelet transform. Especially, a novel method, Ratio Image Based Spectral Resampling （RIBSR）method, is proposed to accomplish data resampling in spectral domain by utilizing the property of ratio image. And a new fusion rule, Average and Substitution （A＆S） rule, is employed as the fusion rule to accomplish wavelet coefficient integration. Experimental results illustrate that the fusion approach using 3-D wavelet transform can utilize both spatial and spectral characteristics of source images more adequately and produce fused image with higher quality and fewer artifacts than fusion approach using 2-D wavelet transform. It is also revealed that RIBSR method is capable of interpolating the missing data more effectively and correctly, and A＆S rule can integrate coefficients of source images in 3-D wavelet domain to preserve both spatial and spectral features of source images more properly.

Robust key point descriptor for multi-spectral image matching

Histogram of collinear gradient-enhanced coding （HCGEC）, a robust key point descriptor for multi-spectral image matching, is proposed. The HCGEC mainly encodes rough structures within an image and suppresses detailed textural information, which is desirable in multi-spectral image matching. Experiments on two multi-spectral data sets demonstrate that the proposed descriptor can yield significantly better results than some state-of- the-art descriptors.

Automatic abstraction of interference fringes with image technique

ion technique of interference fringes used in phase modulation and phase scanning modulation interferometer is presented. For the measurement of amplitudes of interference fringes, fringes are fitted and their central points are determined automatically according to their distribution rules. However, for the measurement of their phases, fringes should be recognized and processed with different calculating algorithms and least square optimization methods depending on the scanning modulation mode. When this technique is used for measurement of surface roughness, the measurement uncertainty is better than 5nm and the repeatability is less than 5%.

Extracting Feature Bands for Damaged Rice Leaves by Planthoppers Using Multi-spectral Imaging Technology

[Objective] The aim of this study was to extract effective feature bands of damaged rice leaves by planthoppers to make identification and classification rapidly from great amounts of imaging spectral data. [Method] The experiment, using multi-spectral imaging system, acquired the multi-spectral images of damaged rice leaves from band 400 to 720 nm by interval of 5 nm. [Result] According to the principle of band index, it was calculated that the bands at 515, 510, 710, 555, 630, 535, 505, 530 and 595 nm were having high band index value with rich information and little correlation. Furthermore, the experiment used two classification methods and calcu-lated the classification accuracy higher than 90.00% for feature bands and ful bands of damaged rice leaves by planthoppers respectively. [Conclusion] It can be con-cluded that these bands can be considered as effective feature bands to identify damaged rice leaves by planthoppers quickly from a large scale of crops.

Multi-Spectral and Fluorescence Imaging in Prevention of Overdose of Herbicides: The Case of Maize

Evaluation of the impact of herbicides on maize was done through multi- spectral and multi-modal imaging and multi-spectral fluorescence imaging combined with statistical methods. Spectra containing 13 wavelengths ranging from 375 nm to 940 nm were derived from multi-spectral images in transmission, reflection and scattering mode and fluorescence images obtained using high-pass filters (F450 nm, F500 nm, F550 nm, F600 nm, F650 nm) on control maize samples and maize samples treated with Herbextra herbicide were used. The appearance of the spectra allowed us to characterize the effect of the herbicide on the maize pigment concentration. The fluorescence images allowed us to track the fate of absorbed energy and through PLS-DA and SVM-DA to discriminate the two leaf categories with very low error rates for the test, i.e. 4.9% and 2% respectively. The results of this technique can be used in the context of precision agriculture.

Application of PCA Numalgorithm in Remote Sensing Image Processing

A numerical algorithm of principal component analysis (PCA) is proposed and its application in remote sensing image processing is introduced: (1) Multispectral image compression;(2) Multi-spectral image noise cancellation;(3) Information fusion of multi-spectral images and spot panchromatic images. The software experiments verify and evaluate the effectiveness and accuracy of the proposed algorithm.

Opto-mechanical-thermal integration analysis of Doppler asymmetric spatial heterodyne interferometer

In order to improve the detection accuracy of Doppler asymmetric spatial heterodyne(DASH)interferometer in harsh temperatures,an opto-mechanical-thermal integration analysis is carried out.Firstly,the correlation between the interference phase and temperature is established according to the working principle and the phase algorithm of the interferometer.Secondly,the optical mechanical thermal analysis model and thermal deformation data acquisition model are designed.The deformation data of the interference module and the imaging optical system at different temperatures are given by temperature load simulation analysis,and the phase error caused by thermal deformation is obtained by fitting.Finally,based on the wind speed error caused by thermal deformation of each component,a reasonable temperature control scheme is proposed.The results show that the interference module occupies the main cause,the temperature must be controlled within(20±0.05)℃,and the temperature control should be carried out for the temperature sensitive parts,and the wind speed error caused by the part is 3.8 m/s.The thermal drift between the magnification of the imaging optical system and the thermal drift of the relative position between the imaging optical system and the detector should occupy the secondary cause,which should be controlled within(20±2)℃,and the wind speed error caused by the part is 3.05 m/s.In summary,the wind measurement error caused by interference module,imaging optical system,and the relative position between the imaging optical system and the detector can be controlled within 6.85 m/s.The analysis and temperature control schemes presented in this paper can provide theoretical basis for DASH interferometer engineering applications.

An improved non-uniform fast Fourier transform method for radio imaging of coronal mass ejections

Radioheliographs can obtain solar images at high temporal and spatial resolution,with a high dynamic range.These are among the most important instruments for studying solar radio bursts,understanding solar eruption events,and conducting space weather forecasting.This study aims to explore the effective use of radioheliographs for solar observations,specifically for imaging coronal mass ejections(CME),to track their evolution and provide space weather warnings.We have developed an imaging simulation program based on the principle of aperture synthesis imaging,covering the entire data processing flow from antenna configuration to dirty map generation.For grid processing,we propose an improved non-uniform fast Fourier transform(NUFFT)method to provide superior image quality.Using simulated imaging of radio coronal mass ejections,we provide practical recommendations for the performance of radioheliographs.This study provides important support for the validation and calibration of radioheliograph data processing,and is expected to profoundly enhance our understanding of solar activities.

Online Investigation of Optical Properties and Craze Propagation of Drawn Polypropylene Fiber by Using Interferometric Image Analysis

Pluta polarizing interference microscope was used to follow the crazing that occur on the surface of stretched polypropylene fibres at different drawing conditions. The samples were stretched until crazing initiated, and then craze propagation was monitored as a function of drawing speed and test temperature. The effect of craze dimension on their propagation velocity was taken into account. Three-dimensional birefringence profile for crazed polypropylene fibre has been demonstrated to investigate the birefringence of crazed fibre at different test times for fixed drawing speed value. Also the mean birefringence values of crazed polypropylene fibres were calculated and the results showed that, these values increased with the areal craze density. Video images were used to calculate the craze velocity. Optical micrographs and microinterferograms were presented for demonstrations.

Image Processing of Corona Virus Using Interferometry

A new method of image processing of corona virus based on two and multiple beam interference is suggested. The method is based on measuring the fringe shift with respect to the background interference pattern. The interested application of the corona virus image in confocal microscopy is getting depth information since it has the property of optical sectioning. An accurate measurement of the fringe shift is obtained using multiple beam interference since contrast is higher than that for two beam interference. The refractive index of the corona virus image is deduced from the fringe shift. A MATLAB code is used for the processing of all images.

Design of interference-imaging spectrum data transmission Card Based on PCI Bus

Take the interference- imaging spectrometer for exam-ple, the paper analyses the characters of the spectrum- formingsignals and their timing sequence. Then inspects the key tech-nology in the interference - imaging spectrum data transmissionsystem, and indicates a design for the system- - which is basedon computer PCI bus with CPLD technique imbedded. The sys-tem’ s circuitry and function are also presented.

Low-dimensional multi-spectral space for color reproduction based on nonnegative constrained principal component analysis

In order to overcome the shortcomings that the reconstructed spectral reflectance may be negative when using the classic principal component analysis （PCA）to reduce the dimensions of the multi-spectral data, a nonnegative constrained principal component analysis method is proposed to construct a low-dimensional multi-spectral space and accomplish the conversion between the new constructed space and the multispectral space. First, the reason behind the negative data is analyzed and a nonnegative constraint is imposed on the classic PCA. Then a set of nonnegative linear independence weight vectors of principal components is obtained, by which a lowdimensional space is constructed. Finally, a nonlinear optimization technique is used to determine the projection vectors of the high-dimensional multi-spectral data in the constructed space. Experimental results show that the proposed method can keep the reconstructed spectral data in [ 0, 1 ]. The precision of the space created by the proposed method is equivalent to or even higher than that by the PCA.

Gene interference regulates aquaporin-4 expression in swollen tissue of rats with cerebral ischemic edema Correlation with variation in apparent diffusion coefficient

To investigate the effects of mRNA interference on aquaporin-4 expression in swollen tissue of rats with ischemic cerebral edema, and diagnose the significance of diffusion-weighted MRI, we injected 5 pL shRNA- aquaporin-4 （control group） or siRNA- aquaporin-4 solution （1：800） （RNA interference group） into the rat right basal ganglia immediately before occlusion of the middle cerebral artery. At 0.25 hours after occlusion of the middle cerebral artery, diffusion-weighted MRI displayed a high signal; within 2 hours, the relative apparent diffusion coefficient decreased markedly, aquaporin-4 expression increased rapidly, and intracellular edema was obviously aggravated; at 4 and 6 hours, the relative apparent diffusion coefficient slowly returned to control levels, aquaporin-4 expression slightly increased, and angioedema was observed. In the RNA interference group, during 0.25- 6 hours after injection of siRNA- aquaporin-4 solution, the relative apparent diffusion coefficient slightly fluctuated and aquaporin-4 expression was upregulated; during 0.5 4 hours, the relative apparent diffusion coefficient was significantly higher, while aquaporin-4 expression was significantly lower when compared with the control group, and intracellular edema was markedly reduced; at 0.25 and 6 hours, the relative apparent diffusion coefficient and aquaporin-4 expression were similar when compared with the control group; obvious angioedema remained at 6 hours. Pearson＇s correlation test results showed that aquaporin-4 expression was negatively correlated with the apparent diffusion coefficient （r = -0.806, P 〈 0.01）. These findings suggest that upregulated aquaporin-4 expression is likely to be the main molecular mechanism of intracellular edema and may be the molecular basis for decreased relative apparent diffusion coefficient. Aquaporin-4 gene interference can effectively inhibit the upregulation of aquaporin-4 expression during the stage of intracelfular edema with time-effectiveness. Moreover, diffusion-weighted MRI can accurately detect intracellular edema.

MULTI-SOURCE REMOTE SENSING IMAGE FUSION BASED ON SUPPORT VECTOR MACHINE

Remote Sensing image fusion is an effective way to use the large volume ofdata from multi-source images. This paper introduces a new method of remote sensing image fusionbased on support vector machine (SVM), using high spatial resolution data SPIN-2 and multi-spectralremote sensing data SPOT-4. Firstly, the new method is established by building a model of remotesensing image fusion based on SVM. Then by using SPIN-2 data and SPOT-4 data, image classificationfusion is tested. Finally, an evaluation of the fusion result is made in two ways. 1) Fromsubjectivity assessment, the spatial resolution of the fused image is improved compared to theSPOT-4. And it is clearly that the texture of the fused image is distinctive. 2) From quantitativeanalysis, the effect of classification fusion is better. As a whole, the re-suit shows that theaccuracy of image fusion based on SVM is high and the SVM algorithm can be recommended forapplication in remote sensing image fusion processes.

An application of differential interference contrast in metallographic examination

As one of the most exciting inspection and powerful analysis methods in modern materials metallographic examinations, the difference interference contrast (DIC) method has many advantages, including relatively low requirement for specimen preparation, obvious relief senses observed under microscope. Details such as fine structures or defects that are not or barely visible in incident-light bright field, could be easily revealed and thus make materials analysis more reliable. Differential interference contrast produces an image that can be readily manipulated using digital and video imaging techniques to further enhance contrast. But, studies of material metallography based on DIC method have rarely carried out. Based on the fundamental principle of the DIC method combing with the computer image analysis, applications of DIC method in materials metallographic examination were investigated in this study.

Structured illumination microscopy based on asymmetric three-beam interference

Structured illumination microscopy(SIM)is a rapidly developing super-resolution technology.It has been widely used in various application fields of biomedicine due to its excellent two-and three-dimensional imaging capabilities.Furthermore,faster three-dimensional imaging methods are required to help enable more research-oriented living cell imaging.In this paper,a fast and sensitive three-dimensional structured illumination microscopy based on asymmetric three-beam interference is proposed.An innovative time-series acquisition method is employed to halve the time required to obtain each raw image.A segmented half-wave plate as a substantial linear polarization modulation method is applied to the three-dimensional SIM system for the first time.Although it needs to acquire 21 raw images instead of 15 to reconstruct one super-resolution image,the SIM setup proposed in this paper is 30%faster than the traditional spatial light modulator-SIM(SLM-SIM)in imaging each super-resolution image.The related theoretical derivation,hardware system,and verification experiment are elaborated in this paper.The stable and fast 3D super-resolution imaging method proposed in this paper is of great significance to the research of organelle interaction,intercellular communication,and other biomedical fields.

EDGE DETECTION METHOD OF REMOTE SENSING IMAGES BASED ON MATHEMATICAL MORPHOLOGY OF MULTI-STRUCTURE ELEMENTS

This paper puts forward an effective, specific algorithm for edge detection. Based on multi-structure elements of gray mathematics morphology, in the light of difference between noise and edge shape of RS images, the paper establishes multi-structure elements to detect edge by utilizing the grey form transformation principle. Compared with some classical edge detection operators, such as Sobel Edge Detection Operator, LOG Edge Detection Operator, and Canny Edge Detection Operator, the experiment indicates that this new algorithm possesses very good edge detection ability, which can detect edges more effectively, but its noise-resisting ability is relatively low. Because of the bigger noise & remote sensing image, the authors probe into putting forward other edge detection method based on combination of wavelet directivity checkout technology and small-scale Mathematical Morphology finally. So, position at the edge can be accurately located, the noise can be inhibited to a certain extent and the effect of edge detection is obvious.

USING COVARIANCE INTERSECTION FOR CHANGE DETECTION IN REMOTE SENSING IMAGES

In this paper,an unsupervised change detection technique for remote sensing images acquired on the same geographical area but at different time instances is proposed by conducting Covariance Intersection(CI) to perform unsupervised fusion of the final fuzzy partition matrices from the Fuzzy C-Means(FCM) clustering for the feature space by applying compressed sampling to the given remote sensing images.The proposed approach exploits a CI-based data fusion of the membership function matrices,which are obtained by taking the Fuzzy C-Means(FCM) clustering of the frequency-domain feature vectors and spatial-domain feature vectors,aimed at enhancing the unsupervised change detection performance.Compressed sampling is performed to realize the image local feature sampling,which is a signal acquisition framework based on the revelation that a small collection of linear projections of a sparse signal contains enough information for stable recovery.The experimental results demonstrate that the proposed algorithm has a good change detection results and also performs quite well on denoising purpose.