- 标题
- 摘要
- 关键词
- 实验方案
- 产品
-
[IEEE IGARSS 2018 - 2018 IEEE International Geoscience and Remote Sensing Symposium - Valencia (2018.7.22-2018.7.27)] IGARSS 2018 - 2018 IEEE International Geoscience and Remote Sensing Symposium - Ship Discrimination with Deep Convolutional Neural Networks in Sar Images
摘要： With the advantages of all-time, all-weather, and wide coverage, synthetic aperture radar (SAR) systems are widely used for ship detection to ensure marine surveillance. However, the azimuth ambiguity and buildings exhibit similar scattering mechanisms of ships, which cause false alarms in the detection of ships. To address this problem, self-designed deep convolutional neural networks with the capability to automatically learn discriminative features is applied in this paper. Two datasets, including one dataset reconstructed from IEEEDataPort SARSHIPDATA and the other constructed from 10 scenes of Sentinel-1 SAR images, are used to evaluate our approach. Experimental results reveal that our model achieves more than 95% classification accuracy on both datasets, demonstrating the effectiveness of our approach.
关键词： ship discrimination,Sentinel-1 images,synthetic aperture radar,deep convolutional neural networks
更新于2025-09-23 15:23:52
-
[IEEE 2018 3rd International Conference on Computer Science and Engineering (UBMK) - Sarajevo, Bosnia and Herzegovina (2018.9.20-2018.9.23)] 2018 3rd International Conference on Computer Science and Engineering (UBMK) - Hyperspectral Image Classification Using Reduced Extreme Learning Machine
摘要： In the classification of hyperspectral images, kernel based approaches have been shown to be successful results. Too much training or testing data in the images increases the computation time and memory requirements in the kernel computations. Extreme learning machines that can be used with the kernel approach also need the same requirements in kernel computations. In this study, improvements were made in terms of computation time and memory using reduced kernel extreme learning machines (RKELM). The obtained results are presented comparatively through the tables of performance and time information with kernel extreme learning machine (KELM).
关键词： classification,spectral information,Hyperspectral images,reduced kernel extreme learning machine
更新于2025-09-23 15:23:52
-
Computer-Assisted Diagnosis for Diabetic Retinopathy Based on Fundus Images Using Deep Convolutional Neural Network
摘要： Diabetic retinopathy (DR) is a complication of long-standing diabetes, which is hard to detect in its early stage because it only shows a few symptoms. Nowadays, the diagnosis of DR usually requires taking digital fundus images, as well as images using optical coherence tomography (OCT). Since OCT equipment is very expensive, it will benefit both the patients and the ophthalmologists if an accurate diagnosis can be made, based solely on reading digital fundus images. In the paper, we present a novel algorithm based on deep convolutional neural network (DCNN). Unlike the traditional DCNN approach, we replace the commonly used max-pooling layers with fractional max-pooling. Two of these DCNNs with a different number of layers are trained to derive more discriminative features for classification. After combining features from metadata of the image and DCNNs, we train a support vector machine (SVM) classifier to learn the underlying boundary of distributions of each class. For the experiments, we used the publicly available DR detection database provided by Kaggle. We used 34,124 training images and 1,000 validation images to build our model and tested with 53,572 testing images. The proposed DR classifier classifies the stages of DR into five categories, labeled with an integer ranging between zero and four. The experimental results show that the proposed method can achieve a recognition rate up to 86.17%, which is higher than previously reported in the literature. In addition to designing a machine learning algorithm, we also develop an app called 'Deep Retina.' Equipped with a handheld ophthalmoscope, the average person can take fundus images by themselves and obtain an immediate result, calculated by our algorithm. It is beneficial for home care, remote medical care, and self-examination.
关键词： deep convolutional neural network,mobile app,fractional max-pooling,support vector machine,diabetic retinopathy,fundus images,teaching-learning-based optimization
更新于2025-09-23 15:23:52
-
[Communications in Computer and Information Science] Advances in Computing and Data Sciences Volume 905 (Second International Conference, ICACDS 2018, Dehradun, India, April 20-21, 2018, Revised Selected Papers, Part I) || Two Stage Histogram Enhancement Schemes to Improve Visual Quality of Fundus Images
摘要： A fundus image plays a signi?cant role to analyze a wide variety of ophthalmic conditions. One of the major challenges faced by ophthalmologist in the analysis of fundus images is its low contrast nature. In this paper, two stage histogram enhancement schemes to improve the visual quality of fundus images are proposed. Fuzzy logic and Histogram Based Enhancement algorithm (FHBE) and Contrast Limited Adaptive Histogram Equalization (CLAHE) algorithm are cascaded one after the other to accomplish the two stage enhancement task. This results in two new enhancement schemes, namely FHBE-CLAHE and CLAHE-FHBE. The analysis of the results based on its visual quality shows that two stage enhancement schemes outperforms individual enhancement schemes.
关键词： Fundus images,FHBE,CLAHE,Image enhancement
更新于2025-09-23 15:23:52
-
Enhancing multispectral remote sensing data interpretation for historical gold mines in Egypt: a case study from Madari gold mine
摘要： In the last decade, most of the outcrops around the historic gold mines in Egypt had been damaged by the local miners, a situation that complicated remote sensing-based exploration research activities. Madari gold mine area was no more fortunate than other mines in the region. This study identifies a new integrated remote sensing workflow that emphasizes the spectral variations related to differences in chemical and mineralogical compositions of the investigated rock units and deemphasizes the spectral variations introduced by the local miners. All combinations of ratio images are first generated from Landsat 8 Operational Land Imager (OLI) data, then a suite of ratio images that best differentiates between the investigated units is selected, and finally the selected ratio images were stacked to substitute the original image bands in the further processing techniques. The PCA was then applied to the selected ratio images within the stack. Subsequently, a statistical analysis of the eigenvector matrix for each of the PC bands was conducted to select the optimum PC bands and a Principal Component False Color Composite image (PC-FCC) was created from the three selected PC bands. The PC-FCC image (PC3, PC11, PC4 in RGB) was chosen as a result of subtracting the average PC eigenvector negative weights from the average positive eigenvectors weights. Not only was the PC-FCC image used to distinguish the main rock units in the damaged area, but also, to identify the areas with intense alteration zones.
关键词： Eastern Desert,Principal component analysis (PCA),Landsat 8 (OLI),Madari gold mine,Egypt,Ratio images
更新于2025-09-23 15:23:52
-
Monitoring Land-Use/Land-Cover Changes at a Provincial Large Scale Using an Object-Oriented Technique and Medium-Resolution Remote-Sensing Images
摘要： An object-based image analysis (OBIA) technique is replacing traditional pixel-based methods and setting a new standard for monitoring land-use/land-cover changes (LUCC). To date, however, studies have focused mainly on small-scale exploratory experiments and high-resolution remote-sensing images. Therefore, this study used OBIA techniques and medium-resolution Chinese HJ-CCD images to monitor LUCC at the provincial scale. The results showed that while woodland was mainly distributed in the west, south, and east mountain areas of Hunan Province, the west had the largest area and most continuous distribution. Wetland was distributed mainly in the northern plain area, and cultivated land was distributed mainly in the central and northern plains and mountain valleys. The largest impervious surface was the Changzhutan urban agglomerate in the northeast plain area. The spatial distribution of land cover in Hunan Province was closely related to topography, government policy, and economic development. For the period 2000–2010, the areas of cultivated land transformed into woodland, grassland, and wetland were 183.87 km2, 5.57 km2, and 70.02 km2, respectively, indicating that the government-promoted ecologically engineered construction was yielding some results. The rapid economic growth and urbanization, high resource development intensity, and other natural factors offset the gains made in ecologically engineered construction and in increasing forest and wetland areas, respectively, by 229.82 km2 and 132.12 km2 from 2000 to 2010 in Hunan Province. The results also showed large spatial differences in change amplitude (LUCCA), change speed (LUCCS), and transformation processes in Hunan Province. The Changzhutan urban agglomerate and the surrounding prefectures had the largest LUCCA and LUCCS, where the dominant land cover accounted for the conversion of some 189.76 km2 of cultivated land, 129.30 km2 of woodland, and 6.12 km2 of wetland into impervious surfaces in 2000–2010. This conversion was attributed to accelerated urbanization and rapid economic growth in this region.
关键词： change monitoring,object-based image analysis,provincial scale,HJ-CCD images
更新于2025-09-23 15:23:52
-
IRT image segmentation and enhancement using FCM-MALO approach
摘要： Infrared Thermography (IRT) is a method that has modernized the way for monitoring the thermal conditions, finding some potential faults or defects that could be available in electrical systems. In the proposed work, IRT electrical images are taken for diagnosing the faults by the image pre-processing and segmentation process. Initially, the IRT images are changed over into a grayscale image, trailed by image pre-processing is performed where histogram equalization is applied. With the intention of segmenting the faulty portion (high temperature zone) from the electrical equipment, Fuzzy C Means (FCM) strategy is introduced. For optimizing the centroid of FCM algorithm Modified Ant Lion Optimization (MALO) is proposed. From the segmented images, small size portions are removed by using Region Props function. This operation can remove the isolated pixels from the image and extract image components for better representation of images. The optimum results show that the proposed work accomplishes maximum segmentation accuracy compared to existing segmentation algorithms.
关键词： Pre-processing,Infrared thermography images,Fault diagnosis,Segmentation,Region props function,Electrical equipment
更新于2025-09-23 15:23:52
-
[Lecture Notes in Computational Vision and Biomechanics] Computer Aided Intervention and Diagnostics in Clinical and Medical Images Volume 31 || Retina as a Biomarker of Stroke
摘要： Stroke is one of the significant reasons of adult impairment in most of the developing nations worldwide. Various imaging modalities are used to diagnose stroke during its initial hours of occurrence. But early prediction of stroke is still a challenge in the field of biomedical research. Since retinal arterioles share similar anatomical, physiological, and embryological attributes with brain arterioles, analysis of retinal fundus images can be of great significance in stroke prognosis. This research work mainly analyzes the variations in retinal vasculature in predicting the risk of stroke. Fractal dimension, branching coefficients and angle, asymmetry factor and optimality ratio for both arteries and veins were computed from the processed input image and given to a support vector machine classifier which gives promising results.
关键词： Support vector machine,Stroke,Retinal fundus images
更新于2025-09-23 15:23:52
-
A top-down approach for semantic segmentation of big remote sensing images
摘要： The increasing amount of remote sensing data has opened the door to new challenging research topics. Nowadays, significant efforts are devoted to pixel and object based classification in case of massive data. This paper addresses the problem of semantic segmentation of big remote sensing images. To do this, we proposed a top-down approach based on two main steps. The first step aims to compute features at the object-level. These features constitute the input of a multi-layer feed-forward network to generate a structure for classifying remote sensing objects. The goal of the second step is to use this structure to label every pixel in new images. Several experiments are conducted based on real datasets and results show good classification accuracy of the proposed approach. In addition, the comparison with existing classification techniques proves the effectiveness of the proposed approach especially for big remote sensing data.
关键词： Neural networks,Remote sensing images,Big data,Semantic segmentation
更新于2025-09-23 15:23:52
-
Blind Stereoscopic Image Quality Assessment Based on Hierarchical Learning
摘要： We proposed a blind image quality assessment model which used classification and prediction for three-dimensional (3D) image quality assessment (denoted as CAP-3DIQA) that can automatically evaluate the quality of stereoscopic images. First, in the classification stage, the model separated the distorted images into several subsets according to the types of image distortions. This process will assign the images with the same distortion type to the same group. After the classification stage, the classified distorted image set is fed into the image quality predictor that contains five different perceptual channels which predict the image quality score individually. Lastly, we used the regression module of support vector machine to evaluate the final image quality score where the input of the regression model is the combination of five channel's outputs. The model we proposed is tested on three public and popular databases, which are LIVE 3D Image Quality Database Phase I, LIVE 3D Image Quality Database Phase II and MCL 3D Image Quality Database. The experimental results show that our proposed model leads to significant performance improvement on quality prediction for stereoscopic images compared with other existing state-of-the-art quality metrics.
关键词： image quality assessment,stereoscopic images,Hierarchical learning,no reference
更新于2025-09-23 15:23:52