- 标题
- 摘要
- 关键词
- 实验方案
- 产品
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Coarse-to-Fine Extraction of Small-Scale Lunar Impact Craters From the CCD Images of the Chang'E Lunar Orbiters
摘要： Lunar impact craters form the basis for lunar geological stratigraphy, and small-scale craters further enrich the basic statistical data for the estimation of local geological ages. Thus, the extraction of lunar impact craters is an important branch of modern planetary studies. However, few studies have reported on the extraction of small-scale craters. Therefore, this paper proposes a coarse-to-fine resolution method to automatically extract small-scale impact craters from charge-coupled device (CCD) images using histogram of oriented gradient (HOG) features and a support vector machine (SVM) classifier. First, large-scale craters are extracted as samples from the Chang'E-1 images with spatial resolutions of 120 m. The SVM classifier is then employed to establish the criteria for classifying craters and noncraters from the HOG features of the extracted samples. The criteria are then used to extract small-scale craters from higher resolution Chang'E-2 CCD images with spatial resolutions of 1.4, 7, and 50 m. The sample database is updated with the newly extracted small-scale craters for the purpose of the progressive optimization of the extraction. The proposed method is tested on both simulated images and multiple resolutions of real CCD images acquired by the Chang'E orbiters and provides high accuracy results in the extraction of the small-scale impact craters, the smallest of which is 20 m.
关键词： small-scale impact craters,Chang'E satellites,charge-coupled device (CCD) images,support vector machine (SVM) classifier,histogram of oriented gradient (HOG) feature
更新于2025-09-11 14:15:04
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[IEEE 2018 20th International Conference on Transparent Optical Networks (ICTON) - Bucharest (2018.7.1-2018.7.5)] 2018 20th International Conference on Transparent Optical Networks (ICTON) - Examples of Machine Learning Algorithms for Optical Network Control and Management
摘要： Machine learning (ML) offers a great variety of algorithms that can be used in the context of optical networks. In particular, ML algorithms might be applied for classification and to detect patterns, among others. Both, can help to facilitate improving its performance, as well as to understand the behavior of optical networks. In this paper, we review two of these ML algorithms, one for classification and the other for clustering. Illustrative examples of the application of such supervised and unsupervised ML algorithms applied to optical networks are presented.
关键词： support vector machine,machine learning,data visualization,clustering
更新于2025-09-10 09:29:36
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[IEEE IGARSS 2018 - 2018 IEEE International Geoscience and Remote Sensing Symposium - Valencia, Spain (2018.7.22-2018.7.27)] IGARSS 2018 - 2018 IEEE International Geoscience and Remote Sensing Symposium - Stacked Autoencoders for Multiclass Change Detection in Hyperspectral Images
摘要： Change detection (CD) in multitemporal datasets is a key task in remote sensing. In this paper, a scheme to perform multi-class CD for remote sensing hyperspectral datasets extracting features by means of Stacked Autoencoders (SAEs) is introduced. The scheme combines multiclass and binary CD to obtain an accurate multiclass change map. The multiclass CD begins with the fusion of the multitemporal data followed by Feature Extraction (FE) by SAEs. The binary CD is based on the spectral information by calculating pixel-wise distances and thresholding, and it also incorporates spatial information through watershed segmentation. The processed image is filtered by using the binary CD map and later classified by a Support Vector Machine or an Extreme Learning Machine algorithm. The scheme was evaluated over a multitemporal hyperspectral dataset obtained from the Hyperion sensor. Experimental results show the effectiveness of the proposed scheme using a SAE for extracting the relevant features of the fused information when compared to other published FE methods.
关键词： Change Detection,Stacked Autoencoder,Feature Extraction,Hyperspectral,Support Vector Machine,Extreme Learning Machine
更新于2025-09-10 09:29:36
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[IEEE IGARSS 2018 - 2018 IEEE International Geoscience and Remote Sensing Symposium - Valencia, Spain (2018.7.22-2018.7.27)] IGARSS 2018 - 2018 IEEE International Geoscience and Remote Sensing Symposium - Wind Field Retrieving Under Rainy Conditions Based on Support Vector Machine for Combined Active/Passive Observations of HY-2A
摘要： Wind fields over ocean surface can be effectively retrieved by scatterometer measurements with GMFs (geophysical model functions) relating them and observing geometry with wind speeds. However, existed GMFs are not suitable for rainy conditions which require extra information for rain induced factors in observations that vary with rain conditions. The combined observations of scatterometer and radiometer are suitable for the problem. In this paper, this was realized by the method based on SVM (support vector machine) with kernel function established in this paper considering the form of existed GMFs. Experiments had been conducted over HY-2A scatterometer (HSCAT) and radiometer (HRAD) who give observations at almost the same time and operation frequencies suitable for this problem. The training progress was achieved by taken Windsat wind fields as true values. The method had been verified by comparison made between the wind fields retrieved with that of Buoy data. Discussion were made for further researches exploring the underlying physical progress of the SVM established.
关键词： HY-2A,scatterometer,radiometer,wind field,support vector machine,kernel function
更新于2025-09-10 09:29:36
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[IEEE IGARSS 2018 - 2018 IEEE International Geoscience and Remote Sensing Symposium - Valencia, Spain (2018.7.22-2018.7.27)] IGARSS 2018 - 2018 IEEE International Geoscience and Remote Sensing Symposium - Innovative Multi Pcnn Based Network for Green Area Monitoring - Identification and Description of Nearly Indistinguishable Areas - In Hyperspectral Satellite Images
摘要： The paper presents an original neural network approach for region of interest detection and classification in multi-spectral satellite images. The proposed method uses a sequence of Pulse Coupled Neural Networks that identifies plausible regions of interest. These regions are passed to a dimension reduction algorithm, Principle Component Analysis, in order to generate the input data for a Support Vector Machine classifier, that validates the data. The algorithm's parameters are optimized using a Genetic Algorithm. The algorithm is designed to distinguish regions that are extremely similar, such as parks in a city that has entire districts made up of houses with yards. The algorithm has been tested on images provided by the Sentinel-2 satellite, and it proved that it can recall 76.85% of the pixels marked as park in the ground truth data, which was obtained from OpenStreetMap.
关键词： Genetic Algorithm (GA),Pulse Coupled Neural Network (PCNN),Principle Component Analysis (PCA),Support Vector Machine (SVM)
更新于2025-09-10 09:29:36
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Instantaneous brain stroke classification and localization from real scattering data
摘要： This work presents a 2-step Learning-by-Examples approach for the real-time classification of hemorrhagic/ischemic brain strokes and their successive localization from microwave scattering data collected around the human head. An experimental assessment against laboratory-controlled data is performed to assess the potentialities of the proposed approach towards a reliable monitoring and instantaneous diagnosis clinic protocol.
关键词： inverse scattering,experimental data,support vector machine (SVM),brain stroke microwave imaging,learning-by-examples (LBE)
更新于2025-09-10 09:29:36
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[Lecture Notes in Electrical Engineering] Microelectronics, Electromagnetics and Telecommunications Volume 521 (Proceedings of the Fourth ICMEET 2018) || Assessment of EO-1 Hyperion Imagery for Crop Discrimination Using Spectral Analysis
摘要： This paper outlines the research objectives to discriminate crop species using pure spectral-spatial reflectance of EO-1 Hyperion imagery. Vigorous encroachment in remote sensing unlocks the new avenues to investigate the hyper-spectral imagery for analysis and implication for crop-type classification and agricultural management. The investigated crop species were namely Sorghum, Wheat, and cotton located in West zone of Aurangabad, Maharashtra, India. The preprocessing algorithm namely quick atmospheric correction (QUAC) was applied to calibrate bad bands and construct precise data for crop discrimination. The machine learning classifiers applied to identify the pixels having a significant difference in pure spectral signatures based on Ground Control Point (GCP) and image spectral responses. The investigation was based on a binary encoding (BE) and support vector machine (SVM) learning approach in order to discriminate crop types. Crop discrimination followed land cover classes gives 73.35% accuracy using BE and SVM with polynomial third-degree order gives overall accuracy 90.44%. These results show that satellite data with 30 m spatial resolution (Hyperion) are able to identify crop species using Environment for Visualizing Images (ENVI) open source software.
关键词： Atmospheric correction,Support vector machine,A spectral signature,Hyperspectral data
更新于2025-09-10 09:29:36
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[Advances in Intelligent Systems and Computing] International Conference on Intelligent Computing and Applications Volume 846 (Proceedings of ICICA 2018) || Feature Extraction Through Segmentation of Retinal Layers in SDOCT Images for the Assessment of Diabetic Retinopathy
摘要： Diabetic mellitus causes microvasculature changes in the retina which left unchecked. leads to diabetic retinopathy and may cause blindness if Spectral-Domain Optical Coherence Tomography (SDOCT) is a noninvasive imaging modality which could give precise information about the retinal layers. SDOCT retinal images of 75 subjects with uncontrolled diabetic mellitus for more than 2 years duration and images of 30 subjects with controlled diabetes or in normal condition are considered. The speckle noise in the images is smoothened using anisotropic diffusion ?ltering technique, and segmentation of Retinal Nerve Fiber layer (RNFL) along with Ganglion Cell Layer (GCL) and Inner Plexiform Layer (IPL) complex is performed using the axial gradient canny edge detection combined with a level set method. Textural features are obtained from the segmented layers, and classi?cation of abnormality is done using SVM. The results showed that the retinal nerve ?ber layer along with GCL+IPL complex thickness was reduced in subjects with even minimal diabetic retinopathy.
关键词： SDOCT,Inner plexiform layer,Support vector machine,Ganglion cell layer
更新于2025-09-10 09:29:36
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Computer aided diagnosis of glaucoma using discrete and empirical wavelet transform from fundus images
摘要： Glaucoma is a class of eye disorder; it causes progressive deterioration of optic nerve fibres. Discrete wavelet transforms (DWTs) and empirical wavelet transforms (EWTs) are widely used methods in the literature for feature extraction using image decomposition. However, to increase the accuracy for measuring features of images a hybrid and concatenation approach has been presented in the proposed research work. DWT decomposes images into approximate and detail coefficients and EWT decomposes images into its sub band images. The concatenation approach employs the combination of all features obtained using DWT and EWT and their combination. Extracted features from each of DWT, EWT, DWTEWT and EWTDWT are concatenated. Concatenated features are normalised, ranked and fed to singular value decomposition to find robust features. Fourteen robust features are used by support vector machine classifier. The obtained accuracy, sensitivity and specificity are 83.57, 86.40 and 80.80%, respectively, for tenfold cross validation which outperforms the existing methods of glaucoma detection.
关键词： glaucoma,empirical wavelet transform,support vector machine,discrete wavelet transform,feature extraction
更新于2025-09-10 09:29:36
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Deep Neural Network-Based Method for Detecting Central Retinal Vein Occlusion Using Ultrawide-Field Fundus Ophthalmoscopy
摘要： The aim of this study is to assess the performance of two machine-learning technologies, namely, deep learning (DL) and support vector machine (SVM) algorithms, for detecting central retinal vein occlusion (CRVO) in ultrawide-field fundus images. Images from 125 CRVO patients (n = 125 images) and 202 non-CRVO normal subjects (n = 238 images) were included in this study. Training to construct the DL model using deep convolutional neural network algorithms was provided using ultrawide-field fundus images. The SVM uses scikit-learn library with a radial basis function kernel. The diagnostic abilities of DL and the SVM were compared by assessing their sensitivity, specificity, and area under the curve (AUC) of the receiver operating characteristic curve for CRVO. For diagnosing CRVO, the DL model had a sensitivity of 98.4% (95% confidence interval (CI), 94.3–99.8%) and a specificity of 97.9% (95% CI, 94.6–99.1%) with an AUC of 0.989 (95% CI, 0.980–0.999). In contrast, the SVM model had a sensitivity of 84.0% (95% CI, 76.3–89.3%) and a specificity of 87.5% (95% CI, 82.7–91.1%) with an AUC of 0.895 (95% CI, 0.859–0.931). Thus, the DL model outperformed the SVM model in all indices assessed (P < 0.001 for all). Our data suggest that a DL model derived using ultrawide-field fundus images could distinguish between normal and CRVO images with a high level of accuracy and that automatic CRVO detection in ultrawide-field fundus ophthalmoscopy is possible. This proposed DL-based model can also be used in ultrawide-field fundus ophthalmoscopy to accurately diagnose CRVO and improve medical care in remote locations where it is difficult for patients to attend an ophthalmic medical center.
关键词： ultrawide-field fundus ophthalmoscopy,support vector machine,deep learning,central retinal vein occlusion,machine learning
更新于2025-09-09 09:28:46