Traditional Supervised Classification Methods Research Articles

Discrimination of Maturity Stages of Cabernet Sauvignon Wine Grapes Using Visible-Near-Infrared Spectroscopy.

Grape quality and ripeness play a crucial role in producing exceptional wines with high-value characteristics, which requires an effective assessment of grape ripeness. The primary purpose of this research is to explore the possible application of visible-near-infrared spectral (Vis-NIR) technology for classifying the maturity stages of wine grapes based on quality indicators. The reflection spectra of Cabernet Sauvignon grapes were recorded using a spectrometer in the spectral range of 400 nm to 1029 nm. After measuring the soluble solids content (SSC), total acids (TA), total phenols (TP), and tannins (TN), the grape samples were categorized into five maturity stages using a spectral clustering method. A traditional supervised classification method, a support vector machine (SVM), and two deep learning techniques, namely stacked autoencoders (SAE) and one-dimensional convolutional neural networks (1D-CNN), were employed to construct a discriminant model and investigate the association linking grape maturity stages and the spectral responses. The spectral data went through three commonly used preprocessing methods, and feature wavelengths were extracted using a competitive adaptive reweighting algorithm (CARS). The spectral data model preprocessed via multiplicative scattering correction (MSC) outperformed the other two preprocessing methods. After preprocessing, a comparison was made between the discriminant models established with full and effective spectral data. It was observed that the SAE model, utilizing the feature spectrum, demonstrated superior overall performance. The classification accuracies of the calibration and prediction sets were 100% and 94%, respectively. This study showcased the dependability of combining Vis-NIR spectroscopy with deep learning methods for rapidly and accurately distinguishing the ripeness stage of grapes. It has significant implications for future applications in wine production and the development of optoelectronic instruments tailored to the specific needs of the winemaking industry.

Class-specific dictionary-based cross-domain scene classification method with reliable target pseudo labels for remote sensing images

It is usually costly or even impossible to obtain required training labels of remote sensing images for traditional supervised classification method. Domain adaptation approaches can reduce the effort on obtaining training labels by transferring the knowledge in previously labeled data. However, it may deliver an unsatisfactory performance resulted from diverse surrounding environments, image sensors, geolocations and so on. Therefore, a class-specific dictionary-based cross-domain scene classification (CDCSC) method with reliable target pseudo labels is proposed for scene classification without labeled target data. First of all, a progressive target pseudo-labeled data selection strategy is proposed so as to augment the training dataset and increase the adaptive ability of the training dataset to the target domain. Then a class-specific constraint is proposed to learn class-specific transitional dictionaries that can draw data from the same class close and make data belonging to diverse types far away. Finally, the domain-invariant representations that concatenates the source, transitional, and target representations can make features discriminative while decreasing the feature distribution difference. The CDCSC approach is experimented on three target images located in different cities, with two publicly available scene classification datasets serving as the training and validation datasets. The experiments confirm that the CDCSC can achieve an average accuracy over 85% without target labeled data.

Deep Learning-Based Algal Bloom Identification Method from Remote Sensing Images—Take China’s Chaohu Lake as an Example

Rapid and accurate monitoring of algal blooms using remote sensing techniques is an effective means for the prevention and control of algal blooms. Traditional methods often have difficulty achieving the balance between interpretative accuracy and efficiency. The advantages of a deep learning method bring new possibilities to the rapid and precise identification of algal blooms using images. In this paper, taking Chaohu Lake as the study area, a dual U-Net model (including a U-Net network for spring and winter and a U-Net network for summer and autumn) is proposed for the identification of algal blooms using remote sensing images according to the different traits of the algae in different seasons. First, the spectral reflection characteristics of the algae in Chaohu Lake in different seasons are analyzed, and sufficient samples are selected for the training of the proposed model. Then, by adding an attention gate architecture to the classical U-Net framework, which can enhance the capability of the network on feature extraction, the dual U-Net model is constructed and trained for the identification of algal blooms in different seasons. Finally, the identification results are obtained by inputting remote sensing data into the model. The experimental results show that the interpretation accuracy of the proposed deep learning model is higher than 90% in most cases with the fastest processing time being less than 10 s, which achieves much better performance than the traditional supervised classification method and also outperforms the single U-Net model using data of whole year as the training samples. Furthermore, the profiles of algal blooms are well-captured.

Detecting Elevated Air Pollution Levels by Monitoring Web Search Queries: Algorithm Development and Validation

Real-time air pollution monitoring is a valuable tool for public health and environmental surveillance. In recent years, there has been a dramatic increase in air pollution forecasting and monitoring research using artificial neural networks. Most prior work relied on modeling pollutant concentrations collected from ground-based monitors and meteorological data for long-term forecasting of outdoor ozone (O3), oxides of nitrogen, and fine particulate matter (PM2.5). Given that traditional, highly sophisticated air quality monitors are expensive and not universally available, these models cannot adequately serve those not living near pollutant monitoring sites. Furthermore, because prior models were built based on physical measurement data collected from sensors, they may not be suitable for predicting the public health effects of pollution exposure. This study aimed to develop and validate models to nowcast the observed pollution levels using web search data, which are publicly available in near real time from major search engines. We developed novel machine learning-based models using both traditional supervised classification methods and state-of-the-art deep learning methods to detect elevated air pollution levels at the US city level by using generally available meteorological data and aggregate web-based search volume data derived from Google Trends. We validated the performance of these methods by predicting 3 critical air pollutants (O3, nitrogen dioxide, and PM2.5) across 10 major US metropolitan statistical areas in 2017 and 2018. We also explore different variations of the long short-term memory model and propose a novel search term dictionary learner-long short-term memory model to learn sequential patterns across multiple search terms for prediction. The top-performing model was a deep neural sequence model long short-term memory, using meteorological and web search data, and reached an accuracy of 0.82 (F1-score 0.51) for O3, 0.74 (F1-score 0.41) for nitrogen dioxide, and 0.85 (F1-score 0.27) for PM2.5, when used for detecting elevated pollution levels. Compared with using only meteorological data, the proposed method achieved superior accuracy by incorporating web search data. The results show that incorporating web search data with meteorological data improves the nowcasting performance for all 3 pollutants and suggest promising novel applications for tracking global physical phenomena using web search data.

Lightweight Heterogeneous Kernel Convolution for Hyperspectral Image Classification With Noisy Labels

Convolutional neural networks (CNNs) have exhibited commendable performance in the hyperspectral images (HSIs) classification task with manually annotated limited available training data for supervision. The accurate classification of pixel-wise land covers using traditional CNNs is often hampered by the presence of wrong (noisy) labels in the training data and can easily be overfitted to the label noises. However, training on noisy labeled data inevitably suffers from performance degradation since CNNs tend to overfit the label noises. To overcome this problem, we propose a lightweight heterogeneous kernel convolution ( <monospace xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">HetConv3D</monospace> ) for HSI classification with noisy labels, where <monospace xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">HetConv3D</monospace> uses two different types of convolutional kernels, i.e., spectral and spatial domains, and fuses them to produce the final feature maps that are less prompted to the noises and also reduces the computation time. The experiments are conducted using three well-known HSI datasets, i.e., Kennedy Space Center (KSC), Salinas Scene (SA), and University of Pavia (UP), and results are compared with traditional supervised classification methods, including support vector machine (SVM), random forest (RF), CNN3D, ContextNet, MS3DNet, lightweight dual-channel residual network (DCRN), and HetConv3DNet. The superior performance exhibited by the proposed model <monospace xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">HetConv3D-HSI</monospace> confirms the importance of learning a fusion of spatial and spectral kernel features. The source code will be made available publicly at <uri xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">https://github.com/purbayankar/HetConv3DNet</uri> .

Hyperspectral Open Set Classification towards Deep Networks Based on Boxplot

Recently, hyperspectral imaging (HSI) supervised classification has achieved an astonishing performance by using deep learning. However, most of them take the ideal assumption of ‘closed set’, where all testing classes have been known during training. In fact, in the real world, new classes unseen in training may appear during testing. Obviously, traditional supervised classification methods cannot operate correctly in the real world, which requires classifiers not only to classify known classes, but reject the unknown in order to avoid false positives. This challenge is called ‘open set classification’(OSC). Considering the increased applications of deep learning in the real world, rejecting unknown classes during classification is of vital importance. To tackle it, we present a simple but effective HSI OSC method toward deep networks. In this method, we tighten the decision boundaries of SoftMax function of the last layer of the deep networks by using boxplots to analysis the statistical characteristics of the probability distribution of known classes and generate a proper rejection threshold for each known class. To test the performance of the proposed HSI OSC method, experiments are conducted on three HSI datasets. The results show that the proposed method outperforms existing state-of-the-art HSI OSC methods.

Integrating Coordinate Features in CNN-Based Remote Sensing Imagery Classification

The land cover classification has played an important role in remote sensing applications. However, most classification methods were designed based on the pixel features or local spatial features of the remote sensing image, which limits the classification accuracy and generalization. In order to further utilize the spatial information, this letter proposes a dual-branch neural network (NN) inspired by the conditional random field (CRF) model, namely CRF-Net, which takes into account the global spatial features of the image, i.e., geographic latitude-longitude information. First, a dual-branch NN is designed to extract the pixel features and coordinate features. Then, the two kinds of features are fused to realize the remote sensing imagery classification. In the experiments, randomly selected samples and spatial-disjoint samples are employed to verify the effectiveness of the proposed method for hyperspectral image (HSI) and polarimetric synthetic aperture radar (PolSAR) image classification. The experimental results show that the proposed method is superior to the traditional supervised classification methods under the spatial-disjoint sampling strategy, and can achieve the same level of accuracy under the random sampling condition.

Mapping an Invasive Plant Spartina alterniflora by Combining an Ensemble One-Class Classification Algorithm with a Phenological NDVI Time-Series Analysis Approach in Middle Coast of Jiangsu, China

Spartina alterniflora (S. alterniflora) is one of the worst plant invaders in the coastal wetlands of China. Accurate and repeatable mapping of S. alterniflora invasion is essential to develop cost-effective management strategies for conserving native biodiversity. Traditional remote-sensing-based mapping methods require a lot of fieldwork for sample collection. Moreover, our ability to detect this invasive species is still limited because of poor spectral separability between S. alterniflora and its co-dominant native plants. Therefore, we proposed a novel scheme that uses an ensemble one-class classifier (EOCC) in combination with phenological Normalized Difference Vegetation Index (NDVI) time-series analysis (TSA) to detect S. alterniflora. We evaluated the performance of the EOCC algorithm in two scenarios, i.e., single-scene analysis (SSA) and NDVI-TSA in the core zones of Yancheng National Natural Reserve (YNNR). Meanwhile, a fully supervised classifier support vector machine (SVM) was tested in the two scenarios for comparison. With these scenarios, the crucial phenological stages and the advantage of phenological NDVI-TSA in S. alterniflora recognition were also investigated. Results indicated the EOCC using only positive training data performed similarly well with the SVM trained on complete training data in the YNNR. Moreover, the EOCC algorithm presented a more robust transferability with notably higher classification accuracy than the SVM when being transferred to a second site, without a second training. Furthermore, when combined with the phenological NDVI-TSA, the EOCC algorithm presented more balanced sensitivity–specificity result, showing slightly better transferability than it performed in the best phenological stage (i.e., senescence stage of November). The achieved results (overall accuracy (OA), Kappa, and true skill statistic (TSS) were 92.92%, 0.843, and 0.834 for the YNNR, and OA, Kappa, and TSS were 90.94%, 0.815, and 0.825 for transferability to the non-training site) suggest that our detection scheme has a high potential for the mapping of S. alterniflora across different areas, and the EOCC algorithm can be a viable alternative to traditional supervised classification method for invasive plant detection.

A novel unsupervised adversarial domain adaptation network for remotely sensed scene classification

ABSTRACT High-resolution remote sensing scene classification is a widely applicable task. Due to the diversity of natural scenes and acquisition methods, in different satellite images, scenes of the same class are often variable in texture, background, illumination and spatial resolution. Thus, it is hard for a remote sensing sense database to contain enough representative images. In this case, the generalization error of traditional supervised classification methods might be too large to generate ideal results. Domain adaptation (DA) has been applied to image classification by reducing feature distribution discrepancy between the source domain (where labels are available) and the target domain (where images need to be classified). In this paper, we propose an unsupervised adversarial domain adaptation method boosted by a domain confusion network (ADA-BDC) which aims at adapting the images from different domains to appear as if drawn from the same domain and improve the transferability of our classifier. For this purpose, the feature extractor in ADA-BDC makes the source and target distributions closer by training a Generative Adversarial Nets (GAN) model. After that, a transferred classifier trained by transferred source domain features is able to acquire a better classification accuracy on the target domain than a non-transferred classifier. In this paper, the experiments are conducted on four remote sensing scene benchmark datasets which are different in spatial scale, resolution, land-cover pattern, etc. Experimental results show that our proposed method is able to improve the transferability across different datasets and improve the classification overall accuracy by 17.18% on average. The comparative experiments demonstrate that the proposed DA network outperforms the compared state-of-the-art domain adaptation methods on remote sensing image scene classification.

Sea Ice Automatic Extraction in the Liaodong Bay from Sentinel-2 Imagery Using Convolutional Neural Networks

Sea ice classification is one of the important tasks of sea ice monitoring. Accurate extraction of sea ice types is of great significance on sea ice conditions assessment, smooth navigation and safty marine operations. Sentinel-2 is an optical satellite launched by the European Space Agency. High spatial resolution and wide range imaging provide powerful support for sea ice monitoring. However, traditional supervised classification method is difficult to achieve fine results for small sample features. In order to solve the problem, this paper proposed a sea ice extraction method based on deep learning and it was applied to Liaodong Bay in Bohai Sea, China. The convolutional neural network was used to extract and classify the feature of the image from Sentinel-2. The results showed that the overall accuracy of the algorithm was 85.79% which presented a significant improvement compared with the tranditional algorithms, such as minimum distance method, maximum likelihood method, Mahalanobis distance method, and support vector machine method. The method proposed in this paper, which combines convolutional neural networks and high-resolution multispectral data, provides a new idea for remote sensing monitoring of sea ice.

Construction of knowledge rule sets for the classification of land cover information for the coastal zone of Peninsular Malaysia

ABSTRACT Landsat images have large advantages for studying land cover changes over long time periods and have become the main data source for the extraction of large-scale land cover. However, due to the image resolution limitations, it is difficult to classify second-class land cover types with high accuracy through only traditional automatic classification methods. This study selected the coastal zone of Peninsular Malaysia in Southeast Asia as the experimental area for studying the knowledge rule sets classification method. First, a system for the classification of coastal land cover suitable for Southeast Asia was established based on regional characteristics, which included 8 first-class and 18 second-class types. Then, through the combination of multiple geoscience knowledge rules, a set of knowledge rules for extracting the second-class types was established by integrating multiple sources of information, such as the temporal features, topographic features, texture features, shape features, topological features and spectral features. The experimental results showed that the knowledge rule sets were effective in the extraction of the secondary land types in the coastal zones. The overall classification accuracy was over 85%, and the Kappa coefficient was higher than 0.8. Compared with the traditional supervised classification method, the classification accuracy was obviously improved.

Information extraction of Baiyangdian wetland based on GF-2 remote sensing data

The study on monitoring the water body range of Baiyangdian and the change of wetland information by means of remote sensing is of great significance to ensure the ecological security of Xiong’an New Area. This paper was conducted using the GF-2 remote sensing data on March and October 2018. The study was to provide an extraction model basing on NDVI-NDWI method, which compare with traditional supervised classification method. The selected sample points by using visual interpretation verified the extracted wetland information. The results show that the classification and extraction of Baiyangdian wetland information are carried out by using the model and supervised classification method respectively, and the wetland area (in which) is obtained in March. The precision of the sample points and the extracted wetland information are all above 90%, and the NDVI-NDWI method is constructed based on the NDVI-NDWI method. The extraction model is more accurate. Since the bare waters in March were 102.31 km2, 28.27 km2 more than in October; the area of aquatic plants extracted in October increased significantly from 122.57 km2 in March to 154.5 km2. It can be seen that the information of Baiyangdian wetland changes with the growth of aquatic plants. The model established by the institute can accurately extract the information of Baiyangdian wetland and provide a scientific reference for the planning and management of Xiong’an New District.

Fully Polarized SAR imagery Classification Based on Deep Reinforcement Learning Method Using Multiple Polarimetric Features

Most traditional supervised classification methods for polarimetric synthetic aperture radar (PolSAR) imagery require abundant manually selected samples, and the classification results are affected by the size and quality of the samples. In this paper, we propose an improved deep Q-network (DQN) method for PolSAR image classification, which can generate amounts of valid data by interacting with the agent using the e -greedy strategy. The PolSAR data are first preprocessed to reduce the influence of speckle noise and extract the multi-dimensional features. The multi-dimensional feature image and corresponding training image are then fed into a deep reinforcement learning model tailored for PolSAR image classification. After many epochs of training, the method was applied to identify different land cover types in two PolSAR images acquired by different sensors. The experimental results demonstrate that the proposed method has a better classification performance compared with traditional supervised classification methods, such as convolutional neural network (CNN), random forest (RF), and L2-loss linear support vector machine (L2-SVM), and also has a good performance compared with the deep learning method CNN-SVM, which integrates the synergy of the SVM and CNN methods, especially in small sample sizes. This study also provides a toolset for the DQN (kiwi.server) on the GitHub development platform for training and visualization.

A Novel Robust Semisupervised Classification Framework for Quality-Related Coupling Faults in Manufacturing Industries

An imbalanced number of faulty and normal samples make the traditional supervised classification methods difficult to ensure their classification performance. Accordingly, semisupervised learning methods have recently become hotspots both in academic research and practical application domains. Different from previous schemes, this paper dedicates on the correlations, common features, and specific features among quality-related coupling faults in manufacturing industries. The main innovations are as follows: first, it is the first time to develop a robust semisupervised classification framework for quality-related coupling faults, which integrates semisupervised multitask feature selection and manifold learning; second, manifold structures and local discriminant information of unlabeled and limited labeled faulty samples are sufficiently explored to improve the classification performance; and third, correlations among quality-related coupling faults are accurately captured, which are crucial for understanding the uniqueness and relationships of them at the feature level. The proposed method is finally validated in a representative manufacturing industry, i.e., hot strip mill process, where detailed simulation processes are presented and better classification performance is shown compared with the existing approaches.

A Novel Object-Based Supervised Classification Method with Active Learning and Random Forest for PolSAR Imagery

Most of the traditional supervised classification methods using full-polarimetric synthetic aperture radar (PolSAR) imagery are dependent on sufficient training samples, whereas the results of pixel-based supervised classification methods show a high false alarm rate due to the influence of speckle noise. In this paper, to solve these problems, an object-based supervised classification method with an active learning (AL) method and random forest (RF) classifier is presented, which can enhance the classification performance for PolSAR imagery. The first step of the proposed method is used to reduce the influence of speckle noise through the generalized statistical region merging (GSRM) algorithm. A reliable training set is then selected from the different polarimetric features of the PolSAR imagery by the AL method. Finally, the RF classifier is applied to identify the different types of land cover in the three PolSAR images acquired by different sensors. The experimental results demonstrate that the proposed method can not only better suppress the influence of speckle noise, but can also significantly improve the overall accuracy and Kappa coefficient of the classification results, when compared with the traditional supervised classification methods.

One-Class Classification of Airborne LiDAR Data in Urban Areas Using a Presence and Background Learning Algorithm

Automatic classification of light detection and ranging (LiDAR) data in urban areas is of great importance for many applications such as generating three-dimensional (3D) building models and monitoring power lines. Traditional supervised classification methods require training samples of all classes to construct a reliable classifier. However, complete training samples are normally hard and costly to collect, and a common circumstance is that only training samples for a class of interest are available, in which traditional supervised classification methods may be inappropriate. In this study, we investigated the possibility of using a novel one-class classification algorithm, i.e., the presence and background learning (PBL) algorithm, to classify LiDAR data in an urban scenario. The results demonstrated that the PBL algorithm implemented by back propagation (BP) neural network (PBL-BP) could effectively classify a single class (e.g., building, tree, terrain, power line, and others) from airborne LiDAR point cloud with very high accuracy. The mean F-score for all of the classes from the PBL-BP classification results was 0.94, which was higher than those from one-class support vector machine (SVM), biased SVM, and maximum entropy methods (0.68, 0.82 and 0.93, respectively). Moreover, the PBL-BP algorithm yielded a comparable overall accuracy to the multi-class SVM method. Therefore, this method is very promising in the classification of the LiDAR point cloud.

Mapping US Urban Extents from MODIS Data Using One-Class Classification Method

Urban areas are one of the most important components of human society. Their extents have been continuously growing during the last few decades. Accurate and timely measurements of the extents of urban areas can help in analyzing population densities and urban sprawls and in studying environmental issues related to urbanization. Urban extents detected from remotely sensed data are usually a by-product of land use classification results, and their interpretation requires a full understanding of land cover types. In this study, for the first time, we mapped urban extents in the continental United States using a novel one-class classification method, i.e., positive and unlabeled learning (PUL), with multi-temporal Moderate Resolution Imaging Spectroradiometer (MODIS) data for the year 2010. The Defense Meteorological Satellite Program Operational Linescan System (DMSP-OLS) night stable light data were used to calibrate the urban extents obtained from the one-class classification scheme. Our results demonstrated the effectiveness of the use of the PUL algorithm in mapping large-scale urban areas from coarse remote-sensing images, for the first time. The total accuracy of mapped urban areas was 92.9% and the kappa coefficient was 0.85. The use of DMSP-OLS night stable light data can significantly reduce false detection rates from bare land and cropland far from cities. Compared with traditional supervised classification methods, the one-class classification scheme can greatly reduce the effort involved in collecting training datasets, without losing predictive accuracy.

Multiple Classifiers and Graph Cut Methods for Spectral Spatial Classification of Hyperspectral Image

Abstract. Hyperspectral image contains fine spectral and spatial resolutions for generating accurate land use and land cover maps. Supervised classification is the one of method used to exploit the information from the hyperspectral image. The traditional supervised classification methods could not be able to overcome the limitations of the hyperspectral image. The multiple classifier system (MCS) has the potential to increase the classification accuracy and reliability of the hyperspectral image. However, the MCS extracts only the spectral information from the hyperspectral image and neglects the spatial contextual information. Incorporating spatial contextual information along with spectral information is necessary to obtain smooth classification maps. Our objective of this paper is to design a methodology to fully exploit the spectral and spatial information from the hyperspectral image for land cover classification using MCS and Graph cut (GC) method. The problem is modelled as the energy minimization problem and solved using α-expansion based graph cut method. Experiments are conducted with two hyperspectral images and the result shows that the proposed MCS based graph cut method produces good quality classification map.

Using Improved ICA Method for Hyperspectral Data Classification

The hyperspectral remote sensing data have the characteristics of high spectral resolution and vast information and high dimensionality. The traditional supervised classification method and unsupervised one have such shortcomings as the classification accuracy is low, the running time will be long, and they cannot obtain a better classification result. To solve these problems, a hyperspectral remote sensing classification algorithm of improved independent component analysis (ICA) is proposed. The core of improved ICA is that the simplification and optimization of ICA model is completed by variational approximation algorithm and then the hyperspectral remote sensing data classification realized with the aid of nonlinear mapping function in support vector machine. The proposed method is evaluated and tested on simulation experiment and a real hyperspectral imager image experiment. The experiment results show that although the running time is longer for small sample data, the number of support vectors involved in calculation process is smaller, the space complexity is relatively simpler, the overall classification accuracy is higher and the classification image quality is much better.

A Text Categorization Method Based on SVM and Improved K-Means

Traditional supervised classification method such as support vector machine (SVM) could achieve high performance in text categorization. However, we should first hand-labeled the samples before classifying. Its a time-consuming task. Unsupervised method such as k-means could also be used for handling the text categorization problem. However, Traditional k-means could easily be affected by several isolated observations. In this paper, we proposed a new text categorization method. First we improved the traditional k-means clustering algorithm. The improved k-means is used for clustering vectors in our vector space model. After that, we use the SVM to categorize vectors which are preprocessed by improved k-means. The experiments show that our algorithm could out-perform the traditional SVM text categorization method.