Hyperspectral image classification based on adaptive optimisation of morphological profile and spatial correlation information

Aiming at the problem of “curse of dimensionality” in hyperspectral image (HSI) classification, this paper proposes an HSI feature extraction algorithm combining spatial-spectral information. The proposed algorithm makes full use of the spatial information and spectral information of HSI. First, the image is reconstructed by introducing the principle of spatial consistency, reduces the redundancy of local information, and incorporates spatial structure information into the spectral feature set automatically. Then, the traditional PCA-LPP manifold learning algorithm is improved to extract spatial-spectral information based on the reconstructed image. The Parzen window density estimation method is used to adapt the neighborhood size, the local spatial features of the image are extracted. At the same time, the neighbor graph is constructed based on the spatial-spectral distance metric, so that the similarity measure between data points is better realized. Finally, the extracted spatial-spectral features are input into a Support Vector Machine (SVM) for HSI classification. Experimental results on Salinas and Pavia U datasets show that the algorithm has better classification accuracy than traditional algorithms.

Accurate estimation of river flow can have a significant importance in water resources management. In this study, Genetic programming (GP) and Support Vector Machine (SVM) methods were used to forecast daily discharge of Barandoozchay River. The daily discharge data of Barandoozchay River measured at the Dizaj hydrometric station during 2007 to 2011 was used for modeling, which 80% of the data used for training and remaining 20% used for testing of models. The results showed that in the both of considered methods, the models including discharges of one, two and three days ago had higher accuracy in verification step and the accuracy of models decreased with increasing discharge values. Comparing the performance of GP and SVM methods indicated that, however the accuracy of the GP method with the R=0.978 and RMSE=1.66 (m3/s) was slightly more than SVM method with R=0.976 and RMSE=1.80 (m3/s), but the SVM is easier than GP method. Thus, the SVM method can be used as an alternative method in forecasting daily river discharge.

Accuracy of data and information on various types of land use and land cover (LULC) is crucial in watershed management planning. The development of remote sensing technology in predicting LULC has been widely used, such as the Support Vector Machine (SVM) and Random Trees (RT) methods. Both methods are part of the model of machine learning and belong to the category of supervised learning. This method can predict, study, and recognize data patterns from the results of training data. The aim of this research is the use of SVM and RT methods for the identification of LULC. Based on the SVM method analysis results, the forest area is 211.61 km2, coffee plantations are 32.92 km2, shrubs are 16.85 km2, built-up/bare land is 3.40 km2, the water body is 3.17 km2, and cloud/shadow is 6.79 km2. While the RT method obtained a forest area of 215.51 km2, coffee plantations covering an area of 29.32 km2, shrubs are covering an area of 16.32 km2, built-up/bare land surrounding an area of 7.22 km2, water body covering an area of 2.62 km2, and cloud/shadow covering an area of 3.75 km2. Overall, the percentage accuracy of the SVM method of 87%, and the RT of 86%. The success rate of modeling SVM (AUC = 0.73) and RT (AUC = 0.75) based on the results of data validation using the area under the receiver operating characteristic curve (AUC) was considered fair good.

Flood susceptibility mapping using a novel ensemble weights-of-evidence and support vector machine models in GIS

The specifications of the forests in Kalimantan are part of the vegetation type which is characteristic of dry lowland forests that are rich in biodiversity. The Kalimantan forest area is known as an area that has the highest vascular biodiversity in the world. Until 2014, the rate of forest loss as a source of biodiversity was always high every year. The National Polar-Orbiting Suomi Partnership (Suomi NPP) is a new generation of weather satellite imagery from NASA which currently continues to develop application algorithms for environmental monitoring. The support vector machine (SVM) method is a one-class learning machine method with artificial neural network methods that can recognize patterns from input or examples given and also include supervised learning. This study aims to analyze the effect of each parameter on the SVM method and the combination that can produce the highest accuracy, and analyze the ability of the SVM method for making hotspot maps based on the number of forest fire areas in East Kalimantan. The results of the study showed that the Support Vector Machine (SVM) method on the spectral data produced a total of 85%.

Sirekap is a mobile application that was built to help the public monitor and oversee the development of the 2024 elections held in Indonesia. The research aims to apply the Support Vector Machine algorithm in analyzing sentiment about the use of the Sirekap application in 2024. The Support Vector Machine method is used to classify user sentiment into classes, namely positive, negative, and very negative. The amount of data used is 15,000 data sourced from Sirekap application reviews on Google PlayStore, with more detailed research stages including data collection, data preprocessing, data labeling, visualization, word weighting, and testing and analysis. The results show that the Support Vector Machine algorithm provides an accuracy of 88% for the Sirekap 2024 application. These results are expected to help developers to develop further the Sirekap 2024 application in improving the quality of the application and providing better user comfort Based on the results of the sentiment analysis of Sirekap 2024 application users on the Google Play store using the Support Vector Machine (SVM) method, an accuracy rate of 88% was obtained in classifying the sentiment of reviews into positive, negative, and very negative. This shows that the Support Vector Machine method is quite accurate for sentiment analysis of Indonesian text data. Overall, most reviews are very negative with a percentage reaching 76.9%, followed by negative reviews at 12.6%, and the least are positive reviews at 11%.

In this paper, we develop a low-cost portable spectrometer based on Support Vector Machines (SVM) method. The purpose of the spectrometer development is for halal food research project. The portable spectrometer uses various light-emitting diodes (LEDs), light sensor and Arduino microcontroller. The initial research of the spectrometer is testing various oil, such as palm oil, olive oil, sesame oil, soybean oil, and lard. The waveform analysis of each oil can be distinguished by spectroscopy method. But, by using Support Vector Machines (SVM) method the various oil can be distinguished and classified better. The data measurement, classification, and graphical interface result show on the interface of Raspberry Pi based on Python 2.7. Thus, the spectrometer can be used for lard detection.

Most of the existing hyperspectral image change detection methods based on deep learning utilize convolutional neural networks (CNN), recurrent neural networks (RNN), and long-short-term memory (LSTM). However, CNNs primarily focus on extracting spatial information from hyperspectral images, thereby failing to fully utilize channel and temporal information. In this paper, we propose a network called STSTNet that can jointly extract spatial, spectral, and temporal feature information. To achieve a richer feature representation space, we employ a spatial attention module to extract spatial feature information from feature maps, and a spectral transformer encoder structure to extract abundant spectral sequence information. Specifically, we design two temporal information extraction structures after each spatial information extraction structure or spectral information extraction structure to capture correlations and interactions within the hyperspectral images. Extensive experiments have been conducted on three hyperspectral datasets, and the experimental results demonstrate the network’s excellent performance.

In recent times, Coronavirus Disease 2019 (COVID-19) has become a growing concern which has taken the world by surprise. Early detection of this virus can be used to save millions of lives. In this study, a Support Vector Machine (SVM) method is proposed for the identification and classification of COVID-19 as an early diagnostic method to help clinicians and doctors to accurate distinguish COVID-19 from SARS-CoV-2. A discrete wavelet transform (DWT) algorithm was used to extract features, while SVM was used to classify the extracted features. For the performance evaluation, metrics such as sensitivity (Sens), specificity (Spec), accuracy (Acc), and F -score metrics were used. A detection rate of 98.2 % was achieved using the proposed SVM method. Finally, the performance of the SVM method is compared to that of current methods, and it is discovered that the SVM method outperforms them.

In order to quickly distinguish infertile eggs from fertile eggs, the hyperspectral imaging technology consisting of imaging and spectral information was used for detecting the fertile information of eggs. Before hatching eggs were incubated, a hyperspectral imaging system (wavelength between 400 to 1 000 nm) was used to acquire the images one-by-one manually. The characteristic information of ratios of length to short axis, elongation, roundness and the ratios of the yolk area to the whole area was extracted based on the images. The normalization method was used as the spectral data preprocessing, and then 155 spectral characteristic variables were extracted from 520 nm waveband through the correlation coefficient method. Principal component analysis (PCA) method was adopted to reduce the dimensions of image-spectrum fusion information; the top six principal components were extracted. Support vector machine (SVM) method was used to establish classification of fertile and infertile eggs models, which are based on image, spectrum and image-spectrum fusion information respectively. The accuracy rates of the SVM models were 84.00%, 90.00% and 93.00% respectively. The experimental results show that the model based on image-spectrum fusion information technology is superior to the single information model. Hyperspectral transmission imaging technology is effective and feasible to detect the fertile hatching eggs before incubation. Keywords: hyperspectral image, hatching eggs, information fusion, nondestructive detection DOI: 10.3965/j.ijabe.20150804.1672 Citation: Zhu Z H, Liu T, Xie D J, Wang Q H, Ma M H. Nondestructive detection of infertile hatching eggs based on spectral and imaging information. Int J Agric & Biol Eng, 2015; 8(4): 69－76.

Beras Miskin (Raskin) Program is a program of social protection, as supporters of other programs such as nutrition improvement, healthy increase, education and productivity improvement of Poor Households. According to Badan Pusat Statistika, there were 14 criteria to determine a household is classified as poor households. Based on these criteria it will be classified of recipient households and non-recipient households of Beras Miskin (Raskin) Program by Support Vector Machine (SVM) method using LibSVM. The concept of classification by SVM is search for the best hyperplane which serves as a separator of two classes of data in the input space. Kernel function is used to convert the data into a higher dimensional space to allow a separation. LibSVM is a package program created by Chih-Chung Chang and Chih-Jen Lin from Department of Computer Science at National Taiwan University. The method used by LibSVM to obtain global solution of duality lagrange problem is decomposition method. To determine the best parameters of kernel function, used k-vold cross validation method and grid search algorithm. In this classification by SVM method using LibSVM, obtain the best accuracy value as 83,1933%, which is the kernel function Radial Basis Function (RBF). Keywords : Beras Miskin (Raskin) Program, Classification, Support Vector Machine (SVM), LibSVM, Kernel Function

Fault detection and diagnosis has an effective role for the safe operation and long life of systems. Condition monitoring is an appropriate way of the maintenance techniques which is applicable in the fault diagnosis of rotating machinery faults. We considered the Support Vector Machine (SVM) method for classifying the condition of centrifugal pump into two types of faults through six features: flow, temperature, suction pressure, discharge pressure, velocity, and vibration. The SVM method is based on statistical learning theory (SLT) and powerful for the problem with small sampling, nonlinear and high dimension. (L.V. Ganyun et al 2005). The SVM classifying is implemented with 4 kernel functions and the results of them are compared. We use an Artificial Neural Network (ANN) as the second classifying method to have comparison among the performance of two methods. After applying the two methods to our data set we make the data set noisy and again we try our SVMs and ANN to compare their robustness in noisy conditions and the results obtained from two methods confirmed the superiority of SVM with some specific kernel functions.

Wheat stripe rust has a severe impact on wheat yield and quality. An effective prediction method is necessary for food security. In this study, we extract the optimal vegetation indices (VIs) sensitive to stripe rust at different time-periods, and develop a wheat stripe rust prediction model with satellite images to realize the multi-temporal prediction. First, VIs related to stripe rust stress are extracted as candidate features for disease prediction from time series Sentinel-2 images. Then, the optimal VI combinations are selected using sequential forward selection (SFS). Finally, the occurrence of wheat stripe rust in different time-periods is predicted using the support vector machine (SVM) method. The results of the features selected demonstrate that, before the jointing period, the optimal VIs are related to the biomass, pigment, and moisture of wheat. After the jointing period, the red-edge VIs related to the crop health status play important roles. The overall accuracy and Kappa coefficient of the prediction model, which is based on SVM, is generally higher than those of the k-nearest neighbor (KNN) and back-propagation neural network (BPNN) methods. The SVM method is more suitable for time series predictions of wheat stripe rust. The model obtained accuracy based on the optimal VI combinations and the SVM increased over time; the highest accuracy was 86.2%. These results indicate that the prediction model can provide guidance and suggestions for early disease prevention of the study site, and the method combines time series Sentinel-2 images and the SVM, which can be used to predict wheat stripe rust.

A new kind of surface material is found and defined in the Balmer–Kapteyn (B-K) cryptomare region, Mare-like cryptomare deposits (MCD), representing highland debris mixed by mare deposits with a certain fraction. This postulates the presence of surface materials in the cryptomare regions. In this study, to objectively verify the existence of the MCD in the cryptomare regions, based on the Chang’E-2 microwave radiometer (MRM) data, the support vector machine (SVM) method was adopted, where the K-means algorithm was used to optimize the training samples and the random forest algorithm was used to select the proper band features. Finally, the extracted MCD is identified with the datasets including Lunar Reconnaissance Orbiter Wide Angle Camera, Diviner, and Clementine UV–VIS. The main findings are as follows: (1) Compared to the range outlined via the TB counter, the range of the MCD is objectively extracted using the SVM method in the B-K cryptomare region, which is reasonably indicated by the FeO abundance, TiO2 abundance, and rock abundance distributions. (2) The MCDs were extracted in the Dewar, Lomonosov–Fleming (L-F), and Schiller–Schickard (S-S) regions, indicating that the MCDs are widely distributed in the cryptomaria. (3) The presence of MCDs is concentrated in a limited region, accounting for 64.9%, 52.3%, 76.4%, and 64%, respectively, in the range of Dewar, L-F, S-S, and B-K regions identified using the optical data. The occurrence of the MCD gives a new understanding of the surface evolution in the cryptomare regions.

In this paper, a soft classification algorithm based on composite kernels, which incorporate both spectral and spatial information, is proposed for hyperspectral image. Compared with hard classification, soft classification provides more information about the probabilities one pixel belongs to each class. To calculate these probabilities, the proposed algorithm uses Support Vector Machine (SVM), and it successfully converts SVM output values into probabilities, while at the same time integrates spatial and spectral information by composite kernels. To validate the proposed algorithm, experiments are conducted on hyperspectral images with 126 and 186 bands, and experimental results show that soft classification using SVM can yield better results compared with Maximum Likelihood Classifier (MLC),and the introduction of spectral-spatial kernels can greatly improve classification accuracies.