An Improved Remote Sensing Image Classification Based on K-Means Using HSV Color Feature

Wu, Shulei; Chen, Huandong; Zhao, Zhizhong; Long, Haixia; Song, Chunhui

doi:10.1109/cis.2014.90

Cited by 13 publications

(5 citation statements)

References 14 publications

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“…Finally, they used K-means clustering for unsupervised learning of the input data clusters and compared the results by labelling the clusters using ground truth data. Shulei Wu et al [20] introduced a novel classification method based on K-means using hue, saturation, value (HSV) colour features. Their novel method with HSV data produced higher classification accuracy results when tested with Landsat satellite data than K-means method with RGB data.…”

Section: Unsupervised Clusteringmentioning

confidence: 99%

A Cognitive Digital-Optical Architecture for Object Recognition Applications in Remote Sensing

Kypraios¹

2023

Vision Sensors - Recent Advances

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From coastal landscapes to biodiversity remote sensing can on the one hand capture all the natural heritage elements and on the other hand can help in maintaining protected species. In a typical remote sensing application, a few thousands of super high-resolution images are captured and need to be processed. The next step of the processing involves converting those images to an appropriate format for visual display of the data. Then, the image analyst needs to define the regions of interests (ROIs) in each captured image. Next, ROIs need to be defined for identifying specific objects or extracting the required information. First drawback of this processing cycle is the use of image analysis tools which provide them only with scaling or zooming features. Second, there is no conceptual connection between the image analysis tools and the actual processing cycle. Third, such existing tools do not usually automate any steps in the processing cycle. We combine an optical correlator with a supervised or an unsupervised classifier learning algorithm and show how our proposed novel cognitive architecture is conceptually connected with the image analysis processing cycle. We test the architecture with captured images and describe how it can automate the processing cycle.

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Section: Unsupervised Clusteringmentioning

confidence: 99%

A Cognitive Digital-Optical Architecture for Object Recognition Applications in Remote Sensing

Kypraios¹

2023

Vision Sensors - Recent Advances

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“…It can be further expressed as: (7) Therefore, the likelihood term corresponding to the -th data can be written as: (8) Where: represents the class label of the -th data.…”

Section: Solve the Logistic Regression Loss Functionmentioning

confidence: 99%

“…Diao [5] analyzed the wind erosion performance of glass through artificial acceleration and studied the mechanism of wind erosion. Some reports provided a comprehensive review of the factors affecting the durability of different types of glass, including weathering products and weathering processes [6,7]. Huang [8] used infrared reflection spectroscopy to study the weathering of silicate and phosphate glass surfaces and analyzed structural changes during the weathering process.…”

Section: Introductionmentioning

confidence: 99%

Research on classification of weathered glass based on logistic regression

Zhang,

Li,

Zheng

2023

Second International Conference on Electronic Information Technology (EIT 2023)

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In order to classify glass artifacts into different categories based on two characteristics: the chemical composition of the sampling points and whether they are weathered or not, this paper is divided into two parts. In the first part, the entire dataset is divided using data preprocessing, and a logistic regression model is constructed for binary classification. The optimal parameters of the model are estimated using maximum likelihood estimation and gradient descent algorithm. The aim is to explore the classification patterns of the two types of glass artifacts. In the second part, unsupervised learning kmeans algorithm is used for clustering. Indicators are selected based on mean square error and confidence level. The same model as in the first part is used to test the effectiveness of sub-classification. The results show that glass artifacts can be divided into two main categories: high-potassium glass and lead-barium glass, and further subdivided into 14 subcategories.

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“…In detail, the spectral features extracted were HSV features and GLCM textural features while several geometric were extracted from DSM. HSV are simply hue, saturation and value that according to (Wu et al, 2015) give better results in image classification than RGB colour space. Textural features are features that has spatial distribution information of tonal variations within an image and that can be categorized as being fine, coarse, smooth, rippled, mulled, irregular or lineated as described by (Haralick et al, 1973).…”

Section: Pre-classification Change Detectionmentioning

confidence: 99%

Multi-Temporal Classification and Change Detection Using Uav Images

Makuti¹,

Nex²,

Yang³

2018

Int. Arch. Photogramm. Remote Sens. Spatial Inf. Sci.

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In this paper different methodologies for the classification and change detection of UAV image blocks are explored. UAV is not only the cheapest platform for image acquisition but it is also the easiest platform to operate in repeated data collections over a changing area like a building construction site. Two change detection techniques have been evaluated in this study: the pre-classification and the post-classification algorithms. These methods are based on three main steps: feature extraction, classification and change detection. A set of state of the art features have been used in the tests: colour features (HSV), textural features (GLCM) and 3D geometric features. For classification purposes Conditional Random Field (CRF) has been used: the unary potential was determined using the Random Forest algorithm while the pairwise potential was defined by the fully connected CRF. In the performed tests, different feature configurations and settings have been considered to assess the performance of these methods in such challenging task. Experimental results showed that the post-classification approach outperforms the pre-classification change detection method. This was analysed using the overall accuracy, where by post classification have an accuracy of up to 62.6% and the pre classification change detection have an accuracy of 46.5%. These results represent a first useful indication for future works and developments.Orthophoto DSM Figure 1: orthophoto and DSM for epoch one.

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An Improved Remote Sensing Image Classification Based on K-Means Using HSV Color Feature

Cited by 13 publications

References 14 publications

A Cognitive Digital-Optical Architecture for Object Recognition Applications in Remote Sensing

A Cognitive Digital-Optical Architecture for Object Recognition Applications in Remote Sensing

Research on classification of weathered glass based on logistic regression

Multi-Temporal Classification and Change Detection Using Uav Images

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