Hyperspectral Image Classification Using K-means Clustering

Hyperspectral Image stores the reflectance of objects across the electromagnetic spectrum. Each object is identified by its spectral signature. Hyperspectral Sensors records these images from airborne devices. By processing these Images we can get various information about the land-form, seabed etc. This thesis presents an efficient and accurate classification technique for Hyperspectral Images. The approach consists of three steps. Firstly, dimension reduction of the Hyperspectral Image using Principal Component Analysis. This is done in order to reduce the time complexity of the further process. Secondly, the reduced features are clustered by k-means clustering. Lastly, the clusters are individually trained by Support Vector Machine. This scheme was tested with Pavia University Data-set taken by ROSIS sensor. Using the above scheme overall accuracy of 90.2% was achieved which is very promising in comparison to conventional Support Vector Machine classification which had an overall accuracy of 78.67% with the same data-set

An Efficient Classification of Hyperspectral Remotely Sensed Data Using Support Vector Machine

This work present an efficient hardware architecture of Support Vector Machine (SVM) for the classification of Hyperspectral remotely sensed data using High Level Synthesis (HLS) method. The high classification time and power consumption in traditional classification of remotely sensed data is the main motivation for this work. Therefore presented work helps to classify the remotely sensed data in real-time and to take immediate action during the natural disaster. An embedded based SVM is designed and implemented on Zynq SoC for classification of hyperspectral images. The data set of remotely sensed data are tested on different platforms and the performance is compared with existing works. Novelty in our proposed work is extend the HLS based FPGA implantation to the onboard classification system in remote sensing. The experimental results for selected data set from different class shows that our architecture on Zynq 7000 implementation generates a delay of 11.26 µs and power consumption of 1.7 Watts, which is extremely better as compared to other Field Programmable Gate Array (FPGA) implementation using Hardware description Language (HDL)  and Central Processing Unit (CPU) implementation

Using hyperspectral remote sensing data in urban mapping over Kuala Lumpur

Hyperspectral remote sensing has great application potential for analyzing complex urban scenes. In this study, airborne hyperspectral data over part of Kuala Lumpur, Malaysia were used to classify 14 urban classes. In order to do the classification, Support Vector Machine (SVM) was used. Some filters (Lee and Enhanced Lee) were used before performing the classification. Consequently, the results showed that the overall accuracy is improved (3%-4%) when the filters were applied to the image. The overall accuracy for classification of the study area using SVM is 89% with Kappa coefficient 0.88 without filtering. The use of Lee and Enhanced Lee filters improved the accuracy to 92 and 93.6% respectively. This study serves as a pioneering effort in the application of hyperspectral sensing for urban area in Malaysia

A novel information gain-based approach for classification and dimensionality reduction of hyperspectral images

Recently, the hyperspectral sensors have improved our ability to monitor the earth surface with high spectral resolution. However, the high dimensionality of spectral data brings challenges for the image processing. Consequently, the dimensionality reduction is a necessary step in order to reduce the computational complexity and increase the classification accuracy. In this paper, we propose a new filter approach based on information gain for dimensionality reduction and classification of hyperspectral images. A special strategy based on hyperspectral bands selection is adopted to pick the most informative bands and discard the irrelevant and noisy ones. The algorithm evaluates the relevancy of the bands based on the information gain function with the support vector machine classifier. The proposed method is compared using two benchmark hyperspectral datasets (Indiana, Pavia) with three competing methods. The comparison results showed that the information gain filter approach outperforms the other methods on the tested datasets and could significantly reduce the computation cost while improving the classification accuracy. Keywords: Hyperspectral images; dimensionality reduction; information gain; classification accuracy. Keywords: Hyperspectral images; dimensionality reduction; information gain; classification accuracy

Spectral-Spatial Hyperspectral Image Classification Using Subspace-Based Support Vector Machines and Adaptive Markov Random Fields

This paper introduces a new supervised classification method for hyperspectral images that combines spectral and spatial information. A support vector machine (SVM) classifier, integrated with a subspace projection method to address the problems of mixed pixels and noise, is first used to model the posterior distributions of the classes based on the spectral information. Then, the spatial information of the image pixels is modeled using an adaptive Markov random field (MRF) method. Finally, the maximum posterior probability classification is computed via the simulated annealing (SA) optimization algorithm. The combination of subspace-based SVMs and adaptive MRFs is the main contribution of this paper. The resulting methods, called SVMsub-eMRF and SVMsub-aMRF, were experimentally validated using two typical real hyperspectral data sets. The obtained results indicate that the proposed methods demonstrate superior performance compared with other classical hyperspectral image classification methods.Ritrýnt tímaritPeer Reviewe

Towards many-class classification of materials based on their spectral fingerprints

Hyperspectral sensors are becoming cheaper and more available to the public. It is reasonable to assume that in the near future they will become more and more ubiquitous. This gives rise to many interesting applications, for example identification of pharmaceutical products and classification of food stuffs. Such applications require a precise models of the underlying classes, but hand-crafting these models is not feasible. In this paper, we propose to instead learn the model from the data using machine learning techniques. We investigate the use of two popular methods: support vector machines and random forest classifiers. In contrast to similar approaches, we restrict ourselves to linear support vector machines. Furthermore, we train the classifiers by solving the primal, instead of dual optimization problem. Our experiments on a large dataset show that the support vector machine approach is superior to random forest in classification accuracy as well as training time

Accelerating Relevance-Vector-Machine-Based Classification of Hyperspectral Image with Parallel Computing

Benefiting from the kernel skill and the sparse property, the relevance vector machine (RVM) could acquire a sparse solution, with an equivalent generalization ability compared with the support vector machine. The sparse property requires much less time in the prediction, making RVM potential in classifying the large-scale hyperspectral image. However, RVM is not widespread influenced by its slow training procedure. To solve the problem, the classification of the hyperspectral image using RVM is accelerated by the parallel computing technique in this paper. The parallelization is revealed from the aspects of the multiclass strategy, the ensemble of multiple weak classifiers, and the matrix operations. The parallel RVMs are implemented using the C language plus the parallel functions of the linear algebra packages and the message passing interface library. The proposed methods are evaluated by the AVIRIS Indian Pines data set on the Beowulf cluster and the multicore platforms. It shows that the parallel RVMs accelerate the training procedure obviously

Analysis of important grass species distribution in the Krkonoše Mts. tundra using remote sensing

Analysis of important grass species distribution in the Krkonoše Mts. tundra using remote sensing Abstract The aim of this thesis was to test the application of maximum likelihood classification, Random forest, Support vector machine and object-oriented classification with the Support vector machine classifier on selected areas in the Krkonoše Mts. relict arctic-alpine tundra for the purpose of mapping the distribution of vegetation with a focus on conservation-important grass species. The research used pre-processed multitemporal hyperspectral data and multispectral data from UAS with a spatial resolution of 0.03 m and 0.06 m and hyperspectral aerial data with a spatial resolution of 0.6 m together with training and validation data collected by botanists directly from the fields using GPS (all data are from 2019-2021). The classifications achieved excellent results. The best overall accuracies were achieved by the object-oriented classification, with accuracies ranging between 80-95 %. Similarly, good results were also achieved by pixel methods - Random forest and Support vector machine (highest overall accuracy 94 %). Of the important grass species, Calamagrostis villosa (producer's accuracy 99.73 %, user's accuracy 99.95 %) and Deschampsia cespitosa (producer's accuracy 99.98 %, user's accuracy 99.33 %)...Analýza rozšíření ochranářsky důležitých travních druhů v krkonošské tundře s využitím dálkového průzkumu Země Abstrakt Cílem této práce bylo testovat aplikaci klasifikačních metod maximální věrohodnosti, Random forest, Support vector machine a objektově orientované klasifikační metody s klasifikátorem Support vector machine na vybraných plochách v krkonošské reliktní arkto-alpínské tundře za účelem mapování rozšíření vegetace se zaměřením na ochranářsky důležité travní druhy. Pro výzkum byla využita předzpracovaná multitemporální hyperspektrální data a multispektrální data z UAS s prostorovým rozlišením 0,03 m resp. 0,06 m a hyperspektrální letecká data s prostorovým rozlišením 0,6 m společně s trénovacími a validačními daty nasbíranými botaniky přímo v terénu s využitím GPS (všechna data pocházela z let 2019-2021). Klasifikace dosáhly výborných výsledků. Nejlepších celkových přesností bylo dosaženo objektově orientovanou metodou klasifikace, jejíž přesnost se pohybovala mezi 80-95 %. Podobně dobrých výsledků bylo dosaženo také pixelovými metodami Random forest a Support vector machine (nejvyšší celková přesnost 94 %). Z důležitých travních druhů byly nejlépe vyklasifikovány Calamagrostis villosa (zpracovatelská přesnost 99,73 %, uživatelská přesnost 99,95 %) a Deschampsia cespitosa (zpracovatelská...Department of Applied Geoinformatics and CartographyKatedra aplikované geoinformatiky a kartografiePřírodovědecká fakultaFaculty of Scienc

Spatial and spectral features utilization on a hyperspectral imaging system for rice seed varietal purity inspection

A conventional method to inspect the varietal purity of rice seeds is based on evaluating human visual inspection where a random sample is drawn from a batch. This is a tedious, laborious, time consuming and extremely inefficient task. This paper presents an automatic rice seed inspection method using Hyperspectral imaging and machine learning, to automatically detect unwanted seeds from other varieties which may be contained in a batch. Hyperspectral image data from Near-infrared (NIR) and Visible cameras are acquired for six common rice seed varieties. The results of applying two classifiers are presented, a Support Vector Machine (SVM) and a Random Forest (RF), where each consists of six one-versus-rest binary classifiers. The results show that combining spectral and shape- based features derived from the rice seeds, increase precision of the multi-label classification to 84% compared 74% when only visual features are used