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Discussion on the surface rupture in the south segment of the Minjiang fault inferred from remote sensing images 11

Discussion on the surface rupture in the south segment of th...

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SPIE 11th international symposium on multispectral image processing and pattern recognition (MIPPR) - Remote Sensing image processing, Geographic Information Systems, and Other Applications

作者： Jia Qichao Liu Huaguo Li Feng China Earthquake Disaster Prevent Ctr Beijing 100029 Peoples R China

ISBN: (纸本)9781510636422

Whether the surface rupture of the south segment of Minjiang fault is present remains a controversial issue in recent years. In previous work, interpretation of remote sensing images from Google suggests that this fault section exposes on slopes of the eastern bank of the Mingjiang River, expressing as surface ruptures, implying its activity during Holocene. While the features of fault scarps seen in the field challenges the existence of these ruptures. By virtue of exhaustive field investigations, this paper attempts to further address this issue. Our analysis of geology and geomorphology suggests that the topographic characteristics from remote sensing data are not traces of surface ruptures, instead resulted from a big landslide at the river. Thus it reminds us that there may be a great uncertainty when using remote sensing images interpretation to infer surface ruptures associated with faults.

关键词： satellite image active fault surface rupture Diexi earthquake seismic hazard

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Transfer learning for Hyperspectral image classification using convolutional neural network 11

Transfer learning for Hyperspectral image classification usi...

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SPIE 11th international symposium on multispectral image processing and pattern recognition (MIPPR) - Remote Sensing image processing, Geographic Information Systems, and Other Applications

作者： Liu, Yao Xiao, Chenchao Minist Nat Resources PR China Land Satellite Remote Sensing Applicat Ctr Beijing 100048 Peoples R China

ISBN: (纸本)9781510636422

Deep-learning (DL) based classification methods have been successfully used for hyperspectral image classification in recent years. Among various DL-based methods, convolutional neural network (CNN) has attracted a lot of attention. However, limited number of samples restricts the DL-based methods for widespread application. To deal with this problem, we propose a classification framework that can be transfer-learned between hyperspectral data with different number of bands. First, band selection is conducted to retain same number of bands for imagery of different hyperspectral sensors. Second, we simplify the typical 1D-CNN architecture by removing max-pooling layers. third, modified CNN is trained on a source data, and this pretrained CNN is then fine-tuned with the target data. In the experiment, we pretrain the proposed network using the Indian Pines scene, and then fine-tune parameters to classify pixels in the Botswana scene. According to classification results, this proposed method obtains the highest overall accuracy, compared to KNN, SVM and its corresponding original 1D-CNN model, and even spend the least time training. Therefore, it can be concluded that this proposed method indicate transfer learning can be used between different hyperspectral images, and be helpful to improve the classification efficiency.

关键词： hyperspectral image classification transfer learning convolutional neural network band selection

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Adaptive Locality Preserving Projection for Hyperspectral image Classification 11

Adaptive Locality Preserving Projection for Hyperspectral Im...

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SPIE 11th international symposium on multispectral image processing and pattern recognition (MIPPR) - Remote Sensing image processing, Geographic Information Systems, and Other Applications

作者： He, Lin Chen, Xianjun Xie, Xiaofeng Luo, Haokun South China Univ Technol Sch Automat Sci & Engn Guangzhou 510640 Guangdong Peoples R China Hainan Univ Mech & Elect Engn Coll Haikou 570228 Hainan Peoples R China

ISBN: (纸本)9781510636422

In hyperspectral image classification, small number of labeled samples versus high dimensional data is one of major challenges. Semi-supervised learning has shown potential to relief the dilemma Compared with its supervised learning counterpart, semi-supervised learning exploits both intrinsic structure of labeled and unlabeled samples. In this work, we proposed a graph fusion based semi-supervised learning method for hyperspectral image classification. More specially, two graphs are constructed from spectral-spatial Gabor features and original spectral signatures, respectively, and then are integrated using an affine combination. Experimental results from an AVIRIS hyperspectral dataset verify the excellent classification performance of our method.

关键词： Hyperspectral image local preserving projection superpixel segmentation covariance feature Riemannian manifold

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False positive reduction in lymph node detection by using Convolutional Neural Network with Multi-view Input 11

False positive reduction in lymph node detection by using Co...

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11th international symposium on multispectral image processing and pattern recognition (MIPPR) - Parallel processing of images and Optimization Techniques;and Medical Imaging

作者： Wang, Jiaqi Xu, Li Zhejiang Univ Coll Elect Engn Hangzhou Zhejiang Peoples R China

ISBN: (纸本)9781510636408

The presence of enlarged lymph nodes is a signal of malignant disease or infection. Lymph nodes detection plays an important role in clinical diagnostic tasks. Previous lymph nodes detection methods achieve high sensitivity at the cost of a high false positive rate. In this paper, we propose a method that helps reject false positives. Features are extracted separately from 2D CT slices by using a deep convolutional neural network with multi-view input. Separated feature layers can extract the most suitable features from each input slice individually. We validate the approach on a public dataset and improve the sensitivity by reducing the false positive rate.

关键词： lymph nodes medical image convolutional neural network

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A real - time noise suppression method for target image on unmanned underwater vehicle platform 11

A real - time noise suppression method for target image on u...

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SPIE 11th international symposium on multispectral image processing and pattern recognition (MIPPR) - Remote Sensing image processing, Geographic Information Systems, and Other Applications

作者： Xu, Zhuofan Hua, Xia Shi, Yu Hong, Hanyu Wuhan Inst Technol Hubei Key Lab Opt Informat & Pattern Recognit Wuhan 430205 Peoples R China Wuhan Inst Technol Hubei Engn Res Ctr Video Image & HD Project Wuhan 430205 Peoples R China Wuhan Inst Technol Sch Elect & Informat Engn Wuhan 430205 Peoples R China

ISBN: (纸本)9781510636422

This paper analyzes the causes of image noise in seawater and the influence of noise on the target image of UUV(unmanned underwater vehicle), and points out the shortcomings of existing methods of noise suppression. In view of the above problems, we propose a real-time noise suppression method for the target image of the UUV platform. The algorithm is divided into three steps: (1) Firstly, the image is binarized by finding an appropriate threshold based on the dispersion between classes. (2) Then, the binary image is subjected to rapid morphological processing to separate the sticky noise. (3) Finally, the target connected domain is calibrated by the four-neighbor method and the pixel values outside the target are gradually reduced based on the principle of human vision to achieve the purpose of noise suppression. Experiments and results show that the method is able to preserve the edge and details of the target well, suppress the noise, and the speed is fast, which satisfies the accuracy and timeliness required for underwater video processing.

关键词： Underwater unmanned vehicle real-time noise suppression inter-class dispersion rapid morphological processing human visual principle

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Dimensionality Reduction of Hyperspectral images Based on Subspace Combination Clustering and Adaptive Band Selection 11

Dimensionality Reduction of Hyperspectral Images Based on Su...

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SPIE 11th international symposium on multispectral image processing and pattern recognition (MIPPR) - Remote Sensing image processing, Geographic Information Systems, and Other Applications

作者： Zhang, Chunsen Liu, Hengheng Xian Univ Sci & Technol Coll Geomat Xian 710054 Shaanxi Peoples R China

ISBN: (纸本)9781510636422

This paper proposes a method of hyperspectral image dimensionality reduction based on automatic subspace partition, k-means clustering based on mutual information and adaptive band selection. This method first automatic subspace division method is used to determine the initial subspace, in various initial subspace through the mutual information between image variance and band and K - means to determine the clustering center and clustering center from two adjacent band selection and their mutual information between the difference between the absolute minimum band as a boundary to delimit the molecular space, and then in the subspace of division of each band is obtained by applying the method of adaptive band selection index, get the biggest index of each subspace of band and from big to small order according to the index, at last in the first three band is the selection of bands. OMIS hyperspectral data were used to conduct experiments, and this method has a higher classification accuracy than the previous band selection methods.

关键词： Automatic subspace partition method K-means clustering Adaptive band selection method Hyperspectral image dimensionality reduction

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Reference image generation for space target recognition on ground-based observation 11

Reference image generation for space target recognition on g...

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11th international symposium on multispectral image processing and pattern recognition (MIPPR) - Automatic Target recognition and Navigation

作者： Li, Hang China Acad Launch Vehicle Technol Beijing Peoples R China

ISBN: (纸本)9781510636361

Space object observation by using small ground-based telescopes has become a common supplementary means for usual space object observation because of its flexible operation and convenient carrying. However, due to atmospheric turbulence, urban lighting and other interference factors, the low resolution, blurred contour and scarce texture of the small ground-based telescope space target observation image seriously affect the space target recognition. At the same time, there are few real-time data for space target observation, thus the conventional deep learning method which needs lots of offline trainning data cannot be applicated. Therefore, it is necessary to prepare typical space target reference image by means of three-dimensional model and simulation image. Generative adversarial network provides a simulation way to prepare training data offline. In the preparation of generation images of space target recognition for ground-based observation, the 3D model data of space target are projected in two dimensions by setting elevation angle, roll angle and spin angle. And the two-dimensional generation imagesof space target are obtained. Then, GAN is trained by using real-time observation data of some existing space targets and corresponding two-dimensional generation images as input, and simulation generation images of typical space targets are obtained by using two-dimensional datum map on trained GAN. Experiments show that the reference images simulated by GAN are more authentic than those generated by traditional methods, and have higher correct rate, accuracy and efficiency on recognition mission.

关键词： ground-based observation object recognition GAN

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Underwater image dehazing and color correction algorithm based on scene depth estimation 11

Underwater image dehazing and color correction algorithm bas...

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SPIE 11th international symposium on multispectral image processing and pattern recognition (MIPPR) - Remote Sensing image processing, Geographic Information Systems, and Other Applications

作者： Sun, Dianjun Shi, Yu Hua, Xia Feng, Yayuan Hong, Hanyu Wuhan Inst Technol Hubei Key Lab Opt Informat & Pattern Recognit Wuhan 430205 Peoples R China Wuhan Inst Technol Hubei Res Ctr Video Image & High Definit Project Wuhan 430205 Peoples R China Wuhan Inst Technol Sch Elect & Informat Engn Wuhan 430205 Peoples R China

ISBN: (纸本)9781510636422

The problem of restoring images degraded by an underwater environment is challenging, in part because light traveling underwater suffers from two combined degradations, known as scattering and absorption, which leads to inaccurate transmittance estimation. In this work, we propose that underwater image dehazing and color correction algorithm based on scene depth estimation. Through scene depth estimation, we get accurate transmittance to achieve better dehazing effect. The experimental results show that our approach obtains good-quality images, with a visibility enhancement comparable or better than other recent methods. As for color recovery, We recovere among different images, regardless of the different water conditions. In this work we not only achieves the effect of underwater image dehazing, but also guarantees accuracy and timeliness of recovery results.

关键词： Underwater image image dehazing color correction depth estimation

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Smoke detection in infrared images based on superpixel segmentation 11

Smoke detection in infrared images based on superpixel segme...

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SPIE 11th international symposium on multispectral image processing and pattern recognition (MIPPR) - Remote Sensing image processing, Geographic Information Systems, and Other Applications

作者： Dai, Min Gao, Peng Sha, Mozhou Tian, Jinwen HUST Sch Artificial Intelligence & Automat 1037 Luoyu Rd Wuhan Hubei Peoples R China Inst Mech & Elect Engn Beijing Peoples R China

ISBN: (纸本)9781510636422

Infrared smoke interference technology seriously infected the combat effectiveness of photoelectric guided weapons in modern warfare. As a result of the occlusion caused by smoke screen, the robustness of image matching guidance algorithm will decrease. Thus, to judge whether there is smoke interference in images and smoke screen area extraction are of great importance for the accuracy of image matching guidance algorithm. However, most of the smoke detection methods aimed at fire early warning, so that they focused on whether smoke exists or not. While both of the discrimination of smoke interference and smoke screen area extraction are what we concern. In this paper, a smoke detection method based on superpixel segmentation and region merging is proposed. Firstly, over-segmentation regions of input infrared image with superpixel segmentation are obtained. Then, fusion texture feature of the image is computed. Finally, superpixel regions are merged based on the fusion features of each superpixel block obtained in the previous step and smoke screen area extraction is completed.

关键词： smoke detection infrared image superpixel segmentation fusion texture features

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Realization of Real-time Detection Algorithms for Key Parts of Unmanned Aerial Vehicle Based on Support Vector Machine 11

Realization of Real-time Detection Algorithms for Key Parts ...

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11th international symposium on multispectral image processing and pattern recognition (MIPPR) - Automatic Target recognition and Navigation

作者： Wang, Weixiang Hong, Hanyu Zhang, Yaozong Li, Shiyang Shi, Jiaowei Wuhan Inst Technol Hubei Key Lab Opt Informat & Pattern Recognit Wuhan 430205 Peoples R China Wuhan Inst Technol Hubei Engn Res Ctr Video Image & HD Project Wuhan 430205 Peoples R China Wuhan Inst Technol Sch Elect & Informat Engn Wuhan 430205 Peoples R China

ISBN: (纸本)9781510636361

Fixed-point attack on the key parts of small aerial vehicles is an important means of UAV (Unmanned Aerial Vehicle) countermeasure. Because of the fast speed and flexible attitude of fixed-wing aircraft, the detection accuracy of key points of fixed-wing aircraft in infrared images is low and the speed is slow. This paper presents an improved detection and tracking algorithm based on SVM. Firstly, the detection module extracts the fixed-wing aircraft area by image segmentation, then extracts the characteristics of the fixed-wing aircraft, then uses SVM to judge the flight direction of the fixed-wing aircraft, and then locates the key points according to the direction. The experimental results show that the proposed detection algorithm can process 30 frames per second on the platform of DSP (TSM320C6678), and still achieve a high detection rate (>93%) with very high practical value.

关键词： Target recognition SVM TMS320C6678 Multi-core parallel

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