Hyperspectral Image Segmentation Research Articles

A Hybrid Firefly with Particle Swarm Optimization Based Hyperspectral Image Classification and Segmentation Using Structured Support Vector Machine

A Hybrid Firefly with Particle Swarm Optimization Based Hyperspectral Image Classification and Segmentation Using Structured Support Vector Machine

A semi-supervised segmentation method for microscopic hyperspectral pathological images based on multi-consistency learning.

Pathological images are considered the gold standard for clinical diagnosis and cancer grading. Automatic segmentation of pathological images is a fundamental and crucial step in constructing powerful computer-aided diagnostic systems. Medical microscopic hyperspectral pathological images can provide additional spectral information, further distinguishing different chemical components of biological tissues, offering new insights for accurate segmentation of pathological images. However, hyperspectral pathological images have higher resolution and larger area, and their annotation requires more time and clinical experience. The lack of precise annotations limits the progress of research in pathological image segmentation. In this paper, we propose a novel semi-supervised segmentation method for microscopic hyperspectral pathological images based on multi-consistency learning (MCL-Net), which combines consistency regularization methods with pseudo-labeling techniques. The MCL-Net architecture employs a shared encoder and multiple independent decoders. We introduce a Soft-Hard pseudo-label generation strategy in MCL-Net to generate pseudo-labels that are closer to real labels for pathological images. Furthermore, we propose a multi-consistency learning strategy, treating pseudo-labels generated by the Soft-Hard process as real labels, by promoting consistency between predictions of different decoders, enabling the model to learn more sample features. Extensive experiments in this paper demonstrate the effectiveness of the proposed method, providing new insights for the segmentation of microscopic hyperspectral tissue pathology images.

Automatic labeling framework for paint loss disease of ancient murals based on hyperspectral image classification and segmentation

Ancient murals have suffered from continuous damage over time, and especially paint loss disease. Therefore, disease labeling, as the basis for ancient mural restoration, plays an important role in the protection of cultural relics. The predominant method of disease labeling is currently manual labeling, which is highly dependent on expert experience, time consuming, inefficient and results in inconsistent accuracy of the marking effect. In this paper, we propose a labeling framework for paint loss disease of ancient murals based on hyperspectral image classification and segmentation. The proposed framework involves first the extraction of features from the hyperspectral image, and then image segmentation is performed based on the spatial features to obtain more accurate region boundaries. Then, the hyperspectral image’s regions are classified based on their spatial-spectral characteristics, and the candidate areas of paint loss disease are obtained. Finally, by leveraging the true color image segmentation results, the proposed disease labeling strategy combines the results of classification and segmentation to propose the final paint loss disease labeling areas. The experimental results show that the proposed method can not only combine the hyperspectral space and spectral information effectively to obtain accurate labeling of paint loss disease, but can also mark the paint loss disease not easily observed using ordinary digital cameras. Compared with the state-of-the-art methods, the proposed framework could be promising for accurate and effective paint loss disease labeling for ancient murals.

Full-scale semantic segmentation of hyperspectral imaging based on spatial spatial-spectral joint network

Abstract. Hyperspectral images contain dozens or even hundreds of spectral bands, which contain rich spectral information and help distinguish different ground objects. Hyperspectral images have a wide range of applications in urban planning, environmental monitoring, and other fields. The semantic segmentation of hyperspectral images is one of the current research hotspots. The difficulty lies in the rich spectral information and strong correlation of hyperspectral images. Traditional semantic segmentation methods cannot fully extract information, which affects the accuracy of classification. This article utilizes an encoding decoding structure to simultaneously extract deep and shallow features of images. A REGCS convolution module was constructed using the idea of group convolution to extract spectral and spatial features of images. We compared the Salinas Valley dataset and MUUFL dataset with various classification algorithms. The experimental results show that compared with other classification models, the RESSU model has achieved stable and excellent results in hyperspectral image classification experiments. Among them, in the classification experiment of the Salinas Valley dataset, the accuracy of single class classification reached over 92%. In the effectiveness analysis experiment, we calculated different model parameter quantities to verify the performance of our method, and ultimately achieved good results.

SAHIS-Net: a spectral attention and feature enhancement network for microscopic hyperspectral cholangiocarcinoma image segmentation

Cholangiocarcinoma (CCA) poses a significant clinical challenge due to its aggressive nature and poor prognosis. While traditional diagnosis relies on color-based histopathology, hyperspectral imaging (HSI) offers rich, high-dimensional data holding potential for more accurate diagnosis. However, extracting meaningful insights from this data remains challenging. This work investigates the application of deep learning for CCA segmentation in microscopic HSI images, and introduces two novel neural networks: (1) Histogram Matching U-Net (HM-UNet) for efficient image pre-processing, and (2) Spectral Attention based Hyperspectral Image Segmentation Net (SAHIS-Net) for CCA segmentation. SAHIS-Net integrates a novel Spectral Attention (SA) module for adaptively weighing spectral information, an improved attention-aware feature enhancement (AFE) mechanism for better providing the model with more discriminative features, and a multi-loss training strategy for effective early stage feature extraction. We compare SAHIS-Net against several general and CCA-specific models, demonstrating its superior performance in segmenting CCA regions. These results highlight the potential of our approach for segmenting medical HSI images.

Modified HDBSCAN based segmentation hyperspectral image segmentation for cotton crop classification

ABSTRACT The most crucial element in accurately monitoring and assessing cotton development is having effective cotton maps. In order to make decisions about governance, precision agriculture, and field management, the county-scale cotton remote sensing categorisation models must be evaluated. The main objective of this research is to propose novel hyperspectral image segmentation approach for cotton crops to monitor the crops and identify early signs of disease. The proposal for a hyperspectral image-based classification of cotton crops is made in this research. Using ‘Modified Hierarchical density-based spatial clustering of applications with noise (HDBSCAN),’ the procedure begins with the input image being segmented. Following this, features based on vegetation indices, hybrid vegetation indices, and statistical characteristics will be retrieved and trained with the classification model to ensure proper classification. Specifically, EVI, NDVI, and RVI are features that are based on vegetation indices. Using techniques like SVM, CNN, DBN, DT, and Improved Bidirectional Long Short-Term Memory (IBi-LSTM), this study replicates a stacked ensemble framework for classification. While the MHDBSCAN achieved the maximum accuracy value of 97.97%, the conventional techniques achieved limited accuracy. Thus, the MHDBSCAN far more effective at classifying the crop utilising hyperspectral image segmentation and the classification become more precise and accurate.

FD-Net: Feature Distillation Network for Oral Squamous Cell Carcinoma Lymph Node Segmentation in Hyperspectral Imagery.

Oral squamous cell carcinoma (OSCC) has the characteristics of early regional lymph node metastasis. OSCC patients often have poor prognoses and low survival rates due to cervical lymph metastases. Therefore, it is necessary to rely on a reasonable screening method to quickly judge the cervical lymph metastastic condition of OSCC patients and develop appropriate treatment plans. In this study, the widely used pathological sections with hematoxylin-eosin (H&E) staining are taken as the target, and combined with the advantages of hyperspectral imaging technology, a novel diagnostic method for identifying OSCC lymph node metastases is proposed. The method consists of a learning stage and a decision-making stage, focusing on cancer and non-cancer nuclei, gradually completing the lesions' segmentation from coarse to fine, and achieving high accuracy. In the learning stage, the proposed feature distillation-Net (FD-Net) network is developed to segment the cancerous and non-cancerous nuclei. In the decision-making stage, the segmentation results are post-processed, and the lesions are effectively distinguished based on the prior. Experimental results demonstrate that the proposed FD-Net is very competitive in the OSCC hyperspectral medical image segmentation task. The proposed FD-Net method performs best on the seven segmentation evaluation indicators: MIoU, OA, AA, SE, CSI, GDR, and DICE. Among these seven evaluation indicators, the proposed FD-Net method is 1.75%, 1.27%, 0.35%, 1.9%, 0.88%, 4.45%, and 1.98% higher than the DeepLab V3 method, which ranks second in performance, respectively. In addition, the proposed diagnosis method of OSCC lymph node metastasis can effectively assist pathologists in disease screening and reduce the workload of pathologists.

Namib Beetle Firefly Optimization enabled Densenet architecture for hyperspectral image segmentation and classification

ABSTRACT Many organisations have concentrated on how hyperspectral images allow for the automatic pixel-level classification and segmentation since each pixel’s underlying spectrum is abundantly documented. Due to the unpredictable nature of the spectrum and the noise in the hyperspectral data, this task is very challenging and calls for specific solutions. The hyperspectral picture segmentation procedure in this instance makes use of the newly developed Namib Beetle Firefly Optimization (NBFO) method, which was created by combining the Namib Beetle Optimization method (NBOA) and Firefly Algorithm (FAO) for tackling optimisation issues. Therefore, in order to segment the images, the U-Net++ model is used. The DenseNet model, which is likewise trained using the NBFO approach, is then used to classify the segmented images. Utilising hyperspectral image segmentation techniques, the NBFO-driven DenseNet model surpassed the competition, resulting in a True Positive Rate (TPR) of 0.906786, a Positive Rate (FPR) of 0.889466, and a False Pixel Accuracy (FPA) of 0.931562.

Proposed Framework for Semantic Segmentation of Aerial Hyperspectral Images Using Deep Learning and SVM Approach

The combination of deep neural networks and assistance vector machines for hyperspectral image recognition is presented in this work. A key issue in the real-world hyperspectral imaging system is hyperspectral picture recognition. Although deep learning can replicate highly dimensional feature vectors from source data, it comes at a high cost in terms of time and the Hugh phenomenon. The selection of the kernel feature and limit has a significant impact on the presentation of a kernel-based learning system. We introduce Support Vector Machine (SVM), a kernel learning method that is used to feature vectors obtained from deep learning on hyperspectral images. By modifying the data structure's parameters and kernel functions, the learning system's ability to solve challenging problems is enhanced. The suggested approaches' viability is confirmed by the outcomes of the experiments. At a particular rate, accuracy of testing for classification is around 90%. Moreover, to significantly make framework robust, validation is done using 5-flod verification.

Enhanced hyperspectral image segmentation and classification using K-means clustering with connectedness theorem and swarm intelligent-BiLSTM

The deep learning method provides better hyperspectral image classification due to proficiency in extracting the deep features from the original images. However they require a vast training samples, the labeled samples are sufficient in hyperspectral image classification that can create several underfitting and overfitting issues. This study proposed a deep learning-based hyperspectral image classification. Optimized K-means clustering with fuzzy-based Connectedness Theorem (OKCCT) is developed for segmenting the input hyperspectral images. The parameter optimization is performed to improve the effectiveness of the proposed model using the Hybrid Whale Dingo Optimization Algorithm (HWDOA). The optimized weighted fused deep features are acquired from the extracted features of three deep learning approaches. This fused feature is utilized for the final classification using the Modified Bidirectional Longs Short-Term Memory (M-BiLSTM) to achieve accurate classification results. The results of the proposed model is compared with existing models such as Particle Swarm Optimization (PSO), Jaya Algorithm (JA), Whale Optimization Algorithm (WOA) and Dingo Optimization Algorithm (DOA) for precision, specificity, F1 score, FDR, FNR, and FPR. The proposed method gives greater accuracy than the PSO, JA, WOA and DOA in most of the cases.

Multiscale Feature Fusion for Hyperspectral Marine Oil Spill Image Segmentation

Oil spills have always been a threat to the marine ecological environment; thus, it is important to identify and divide oil spill areas on the ocean surface into segments after an oil spill accident occurs to protect the marine ecological environment. However, oil spill area segmentation using ordinary optical images is greatly interfered with by the absorption of light by the deep sea and the distribution of algal organisms on the ocean surface, and it is difficult to improve segmentation accuracy. To address the above problems, a hyperspectral ocean oil spill image segmentation model with multiscale feature fusion (MFFHOSS-Net) is proposed. Specifically, the oil spill segmentation dataset was created using hyperspectral image data from NASA for the Gulf of Mexico oil spill, small-size images after the waveband filtering of the hyperspectral images were generated and the oil spill images were annotated. The model makes full use of having different layers with different characteristics by fusing feature maps of different scales. In addition, an attention mechanism was used to effectively fuse these features to improve the oil spill region segmentation accuracy. A case study, ablation experiments and model evaluation were also carried out in this work. Compared with other models, our proposed method achieved good results according to various evaluation metrics.

On-chip hyperspectral image segmentation with fully convolutional networks for scene understanding in autonomous driving

Most of current computer vision-based advanced driver assistance systems (ADAS) perform detection and tracking of objects quite successfully under regular conditions. However, under adverse weather and changing lighting conditions, and in complex situations with many overlapping objects, these systems are not completely reliable. The spectral reflectance of the different objects in a driving scene beyond the visible spectrum can offer additional information to increase the reliability of these systems, especially under challenging driving conditions. Furthermore, this information may be significant enough to develop vision systems that allow for a better understanding and interpretation of the whole driving scene. In this work we explore the use of snapshot, video-rate hyperspectral imaging (HSI) cameras in ADAS on the assumption that the near infrared (NIR) spectral reflectance of different materials can help to better segment the objects in real driving scenarios. To do this, we have used the HSI-Drive 1.1 dataset to perform various experiments on spectral classification algorithms. However, the information retrieval of hyperspectral recordings in natural outdoor scenarios is challenging, mainly because of deficient color constancy and other inherent shortcomings of current snapshot HSI technology, which poses some limitations to the development of pure spectral classifiers. In consequence, in this work we analyze to what extent the spatial features codified by standard, tiny fully convolutional network (FCN) models can improve the performance of HSI segmentation systems for ADAS applications. In order to be realistic from an engineering viewpoint, this research is focused on the development of a feasible HSI segmentation system for ADAS, which implies considering implementation constraints and latency specifications throughout the algorithmic development process. For this reason, it is of particular importance to include the study of the raw image preprocessing stage into the data processing pipeline. Accordingly, this paper describes the development and deployment of a complete machine learning-based HSI segmentation system for ADAS, including the characterization of its performance on different embedded computing platforms, including a single board computer, an embedded GPU SoC and a programmable system on chip (PSoC) with embedded FPGA. We verify the superiority of the FPGA-PSoC over the GPU-SoC in terms of energy consumption and, particularly, processing latency, and demonstrate that it is feasible to achieve segmentation speeds within the range of ADAS industry specifications using standard development tools.

Hyperspectral image segmentation: a preliminary study on the Oral and Dental Spectral Image Database (ODSI-DB)

ABSTRACT Visual discrimination of clinical tissue types remains challenging, with traditional RGB imaging providing limited contrast for such tasks. Hyperspectral imaging (HSI) is a promising technology providing rich spectral information that can extend far beyond three-channel RGB imaging. Moreover, recently developed snapshot HSI cameras enable real-time imaging with significant potential for clinical applications. Despite this, the investigation into the relative performance of HSI over RGB imaging for semantic segmentation purposes has been limited, particularly in the context of medical imaging. Here we compare the performance of state-of-the-art deep learning image segmentation methods when trained on hyperspectral images, RGB images, hyperspectral pixels (minus spatial context), and RGB pixels (disregarding spatial context). To achieve this, we employ the recently released Oral and Dental Spectral Image Database (ODSI-DB), which consists of 215 manually segmented dental reflectance spectral images with 35 different classes across 30 human subjects. The recent development of snapshot HSI cameras has made real-time clinical HSI a distinct possibility, though successful application requires a comprehensive understanding of the additional information HSI offers. Our work highlights the relative importance of spectral resolution, spectral range, and spatial information to both guide the development of HSI cameras and inform future clinical HSI applications.

Unsupervised hyperspectral image segmentation of films: a hierarchical clustering-based approach

Hyperspectral imaging (HSI) has been drastically applied in recent years to cultural heritage (CH) analysis, conservation, and also digital restoration. However, the efficient processing of the large datasets registered remains challenging and still in development. In this paper, we propose to use the hierarchical clustering algorithm (HCA) as an alternative machine learning approach to the most common practices, such as principal component analysis(PCA). HCA has shown its potential in the past decades for spectral data classification and segmentation in many other fields, maximizing the information to be extracted from the high-dimensional spectral dataset via the formation of the agglomerative hierarchical tree. However, to date, there has been very limited implementation of HCA in the field of cultural heritage. Data used in this experiment were acquired on real historic film samples with various degradation degrees, using a custom-made push-broom VNIR hyperspectral camera (380–780nm). With the proposed HCA workflow, multiple samples in the entire dataset were processed simultaneously and the degradation areas with distinctive characteristics were successfully segmented into clusters with various hierarchies. A range of algorithmic parameters was tested, including the grid sizes, metrics, and agglomeration methods, and the best combinations were proposed at the end. This novel application of the semi-automating and unsupervised HCA could provide a basis for future digital unfading, and show the potential to solve other CH problems such as pigment mapping.

Abstract P4-02-22: Breast microcalcification chemistry predicts DCIS prognosis

Abstract Introduction: Microcalcifications are a common feature in mammographic detection of ductal carcinoma in situ (DCIS), and occur in >80% of cases. Known to be present as type I (calcium oxalate-CaO) and type II (carbonated calcium hydroxyapatite-CHAP) microcalcifications, their association with DCIS and their role in the progression of DCIS to invasive breast cancer (IBC) remains unexplored. In an effort to understand the factors involved in DCIS prognosis, it is hypothesized that changes in the chemical composition of calcifications, in tandem with molecular changes in the surrounding soft tissue, will define patients with DCIS who will progress to develop IBC from those who remain with a stable DCIS phenotype. To this end, a novel label-free approach of hyperspectral imaging using mid-infrared (mid-IR) and Raman spectroscopy was used to probe calcification chemistry and molecular composition of the surrounding ductal and stromal soft tissue. The main aim of the work is to identify biomarkers for DCIS prognosis, based on chemical and molecular compositional changes of calcifications and the surrounding soft tissue. It is anticipated that the spectral biomarkers will provide patients and clinicians an informed risk assessment whether to undertake treatment for DCIS or to be placed under active surveillance. Methods: Tissue samples from 422 patient have been obtained and include (i) ‘pure DCIS’ (DCIS without recurrence) (n=193), (ii) ‘DCIS with invasive recurrence’ (DCIS from patients who subsequently were known to develop invasive disease) (n=123), (iii) ‘DCIS plus contemporaneous invasive cancer’ (n=44) and ‘benign’ (n=62) samples. Serial tissue sections were measured using mid-IR and Raman hyperspectral imaging approaches targeting the same calcification and soft tissue regions from specific DCIS ducts. Hyperspectral imaging data was initially pre-processed to digitally remove paraffin and unintended spectral interferences. The pre-processed data was subjected to cluster analysis followed by unsupervised and supervised machine learning classification models to identify spectral features associated with DCIS and its progression to IBC. Results: Cluster analysis based segmentation of hyperspectral images revealed histopathological features including calcifications, epithelium, necrotic areas, connective tissue and stroma. Spectra were extracted from each of the histopathological features using image coordinates, and biomodelling analysis was performed. Initial analysis of 314 calcification images from 170 patients with (i) ‘pure DCIS’ (n=118) and (ii) ‘DCIS with invasive recurrence’ (n=52) showed an area under the receiver operating characteristic (AUROC) mean value of 85% in distinguishing pure DCIS from DCIS that later recurred as IBC. The calcification features appeared to indicate pathology specific changes in phosphate and carbonate content as well as changes in magnesium whitlockite content. Similar analysis of the surrounding soft tissue spectral features showed an AUROC mean value of 85% (necrotic regions surrounding calcifications) and 76% (epithelium) respectively. The epithelial features showed changes in protein secondary structure and content, which together with the calcification changes indicate structural remodelling in DCIS that progresses to IBC, from those that do not. Perspectives: In the ongoing analyses of imaging data from 422 patients, it is anticipated that molecular/structural features from calcification and soft tissue imaging data will provide important cues in understanding DCIS prognosis and could be a promising way forward in determining management of DCIS risk and treatment underpinned by the identification of specific discriminatory spectral markers. Acknowledgments: This work was supported by Cancer Research UK and by KWF Kankerbestrijding (ref. C38317/A24043). Citation Format: Jayakrupakar Nallala, Doriana Calabrese, Sarah Gosling, Esther Lips, Rachel Factor, Allison Hall, Sarah E. Pinder, Ihssane Bouybayoune, Lorraine King, Jeffrey Marks, Thomas Lynch, Donna Pinto, Jelle Wesseling, E Shelley Hwang, Keith Rogers, Nick Stone. Breast microcalcification chemistry predicts DCIS prognosis [abstract]. In: Proceedings of the 2022 San Antonio Breast Cancer Symposium; 2022 Dec 6-10; San Antonio, TX. Philadelphia (PA): AACR; Cancer Res 2023;83(5 Suppl):Abstract nr P4-02-22.

Deep Spectral Clustering With Regularized Linear Embedding for Hyperspectral Image Clustering

The past decade has witnessed the rapid development of deep learning techniques, especially for large-scale and complex data sets. However, it is still a noteworthy problem in dealing with unsupervised hyperspectral image segmentation since inefficiency and misleading result from the absence of supervised information. Generally, spectral clustering is one of the most powerful clustering algorithms, as it often outperforms other methods for image segmentation. Unfortunately, the poor scalability and generalization severely limit the use of spectral clustering, especially for large-scale and high-dimensional hyperspectral images processing. The major motivation of this work is to solve this problem, and we designed a novel algorithm, termed Deep Spectral Clustering with Regularized Linear Embedding (DSCRLE), to benefit from both spectral graph theory and deep learning techniques. The brief procedure is first to construct a fully connected neural network to extract latent feature representations, and then normalize the feature representations by the spectral orthonormal constraint. Lastly, by introducing low-dimensional embedding, we refined the final outputs of all given unlabeled hyperspectral pixels. Extensive experiments have demonstrated that the competitiveness of the proposed method, and it outperforms state-of-art clustering approaches in the task of hyperspectral image segmentation.

Novel Spectral Loss Function for Unsupervised Hyperspectral Image Segmentation

Neural networks (NNs) have gained importance in hyperspectral image (HSI) segmentation for earth observation due to its unparalleled data-driven feature extraction capability. However, in many real-life situations, ground truth is not available, and the performance of unsupervised NNs is still susceptible to enhancement. To overcome this challenge, this letter presents a new loss function to improve the performance of unsupervised HSI segmentation models. The spectral loss function, <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">Sl</i> , which can be included in different models, is based on the purity of the unmixing endmembers and the spectral similarity of the clusters provided by the NN to determine the classes. It is incorporated into a 3D convolutional autoencoder to validate its performance on four standard HSI benchmarks. Furthermore, its performance has been qualitatively examined in a real case study, an oil spill without ground truth. The results show that <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">Sl</i> is a breakthrough in unsupervised HS segmentation, obtaining the best overall performance and highlighting the importance of spectral signatures. Additionally, dimensional reduction is also vital in compacting the spectral information, which facilitates its segmentation. Source code available at [https://github.com/mhaut/HSI-3DSpLoss].

Large-Scale Hyperspectral Image Restoration via a Superpixel Distributed Algorithm Based on Graph Signal Processing

Hyperspectral image (HSI) is often disturbed by various kinds of noise, which brings great challenges to subsequent applications. Many of the existing restoration algorithms do not scale well for HSI with large size. This paper proposes a novel mixed-noise removal method for HSI with large size, by leveraging the superpixel segmentation-based technology and distributed algorithm based on graph signal processing. First, the underlying structure of the HSI is modeled by a two-layer architecture graph. The upper layer, called skeleton graph, is a rough graph constructed by using the modified <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">k</i> -nearest-neighborhood algorithm and its nodes correspond to a series of superpixels formed by HSI segmentation. The skeleton graph can efficiently characterize the inter-correlations between superpixels, while preserving the boundary information and reducing the computational complexity. The lower layer, called detailed graph consisting of a series of local graphs which are constructed to model the similarities between pixels. Second, based on the two-layer graph architecture, the HSI restoration problem is formulated as a series of optimization problems each of which resides on a subgraph. In each optimization problem, a graph Laplacian regularization is defined and incorporated into a low-rank-based model. Third, a novel distributed algorithm is tailored for the restoration problem, by using the information interaction between the nodes of skeleton graph and subgraphs. Numerical experiments conducted on both synthetic and real-world datasets demonstrate the effectiveness of the proposed restoration algorithm compared with existing methods.

Fuzzy C-Multiple-Means Clustering for Hyperspectral Image

Currently, unsupervised hyperspectral image (HSI) segmentation methods are mainly implemented by clustering. Nevertheless, hyperspectral data contains a large amount of noise during the acquisition process, resulting in an abnormal distribution of many pixel points. Traditional clustering algorithms suffer from inaccurate segmentation when dealing with these data. For example, FCM is sensitive to anomalies in the clustering problem of HSI, that makes the clustering accuracy degraded. To address these problems, this paper proposes a method called Fuzzy C-Multiple-Means (FCMM). The method divides data points with multiple subclusters into defined <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">c</i> clusters. Different from the bottom-up coalescent strategy, the proposed FCMM transforms the problem of merging multiple subclusters into an optimisation problem for the fuzzy affiliation matrix, and updates the partitioning of the <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">q</i> subclusters and <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">c</i> classes by an alternating iterative update method. This enhances the robustness of the algorithm and reduces the effect of outliers in the HSI datasets on the FCMM, which provides superior clustering results. Experiments on several HSI datasets validate the effectiveness of FCMM.

Unsupervised Unmixing and Segmentation of Hyper Spectral Images Accounting for Soil Fertility

A crucial component of precision agriculture is the capability to assess the fertility of soil by looking at the precise distribution and composition of its different constituents. This study aims to investigate how different machine learning models may be used to assess soil fertility using hyperspectral pictures. The development of images using a random mixing of different soil components is the first phase, and the hyper spectral bands utilized to create the images are not used again during the analysis procedure. The resulting end members are then acquired by applying the NFINDR algorithm to the process of spectral unmixing this image. The comparison between these end members and the band values of the known elements is then quantified., i.e. it is represented as a graph of band values obtained through spectral unmixing. Finally we quantify the similarities between both graphs and proceed towards the classification of the hyper spectral image as fertile or infertile. In order to classify the hyper spectral image as fertile or infertile, we quantify the similarities between the two graphs. Clustering and picture segmentation algorithms have been devised to help with this process, and a comparison is then made to show which techniques are the most effective.