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Change detection in multitemporal hyperspectral images (HSI) can be regarded as a classification task, consisting of two steps: change feature extraction and identification. To extract clean change features from heavily corrupted spectral change vectors (SCV) of multitemporal HSI, this paper proposes a novel spectrally-spatially regularized low-rank and sparse decomposition model (LRSDSS). It exploits...
Change detection (CD) for multitemporal hyperspectral images (HSI) consists of two steps, change feature extraction and identification. This paper proposes a novel spectrally-spatially regularized low-rank and sparse decomposition model (LRSD_SS), to extract clean change features from corrupted spectral change vectors (SCV) of multitemporal HSI. It decomposes SCV into spatially smoothed low-rank data,...
Classification has been among the central issues of hyperspectral application. However, due to the well-known Hughes phenomenon, most of the methods suffer from the curse of dimensionality and deeply rely on traditional dimensional reduction like Principle Component Analysis (PCA). In this paper, combining spatial and spectral information jointly, we propose a novel deep classification framework....
To combat the well-known Hughes phenomenon occurred in hyperspectral classification, most of the previous works adopt dimensionality reduction or manifold learning technique before supervised learning. While in this paper, we propose a different scheme: First, we design a pixel-wise classifier based on Convolutional Neural Network that could directly mapping observed spectrum to class distribution...
Recently, spectral-spatial classification for hyperspectral imagery (HSI) has become popular since it addresses the issues of limited prior knowledge and spectral internal-class variability. To provide simple and effective approaches in this area, we propose a novel supervised spectral-spatial measurement, affinity score (AS). It considers three factors: local spatial consistency, spectral similarity,...
Change detection for multitemporal hyperspectral images (HSIs) involves two major steps: change feature extraction and classification. For the first part, conventional methods mostly consider spectral features but neglect spatial patterns. Since multitemporal HSIs consist of four dimensions (one for time, one for spectral domain and two for spatial domain), we propose using 4-dimensional Higher Order...
Security risks brought by web page information has been a matter that can no longer be ignored. Malicious script is a major challenge the web sites security is facing currently. According to the data from the Google Research Centre, more than 10% of web pages is malicious. Especially in China, the proportion of malicious web pages has reached 43.21%. This paper presents a detection system which is...
It needs both spectral and spatial information to refine classification of hyperspectral images. There is a general spectral-spatial framework to address the issue. It consists of three major steps: classification, segmentation and combination, to which we have made two improvements. First, superpixels generated by over-segmentation are clustered according to superpixel-wise distances as to balance...
A fast and robust colorectal polyp detection framework in CT colonography was proposed. In order to speed the detection of polyp in CT colonography, a cascade-Adaboost framework was employed, and a lot of candidates were rejected quickly in the first stages of the cascade framework. To improve the performance of cascade-Adaboost, cascade indifference curve was explored to determine detection rate...
This report is carried out on the basic analysis data of the Wuhan TM thermal infrared data, and the results were qualitatively analyzed. With the bands of 1998 and 2005, the basic factors affecting the thermal field, namely buildings, greenbelts and water bodies, were identified by modeling the feature extraction. On the basis of that, the data was analyzed for the features of the underlying changing...
In this paper, we propose a novel face authentication scheme using the active appearance model (AAM) and the hidden Markov model (HMM). The proposed face authentication system can be divided into two parts. First, the AAM is used to extract the low-dimensional feature vectors including combined texture and shape information of individual face images. The extracted feature vectors are further classified...
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