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Informal settlement upgrading projects require high-resolution and up-to-date thematic maps in order to plan and design effective interventions. To this end, Unmanned Aerial Vehicles (UAVs) provide the opportunity to obtain very high resolution 2D orthomosaics and 3D point clouds where and when needed. The heterogeneous, dense structures which typically make up an informal settlement motivate the...
This paper presents a region-based composite kernel framework for spatial-spectral hyperspectral image classification, referred as RCK, by exploiting the local similarities of both the spectral and spatial features via superpixel segmentation. The proposed framework consists of three steps. In the first step, the original hyperspectral image together with its spatial feature image are segmented into...
This paper analyzes and compares different Multiple Kernel Learning (MKL) algorithms for the classification of remote sensing (RS) images. The main purpose of the comparison is to identify advantages and disadvantages of different MKL algorithms in terms of their computational time and classification accuracy. Furthermore, some guidelines on the proper selection of the MKL algorithms associated with...
Remote sensing is the method used to detect and measure target characteristics using electromagnetic energy in the form of heat, light and radio waves. Different applications where remote sensing is used are agriculture, disaster management, urban planning, water resource management, etc. The process of producing thematic map from remotely sensed imagery is called image classification. In one or more...
One kind of Deep Learning models-convolutional neural network, which can reduce the complexity of network structure and the number of parameters to be determined through local receptive fields, weight sharing and pooling operation has achieved state of art results in image classification problems. But this model has gradient diffusion problem, which can cause slow updating of the underlying parameters...
Sparse regression methods have been proven effective in a wide range of signal processing problems such as image compression, speech coding, channel equalization, linear regression and classification. In this paper we develop a new method of hyperspectral image classification based on the sparse unmixing algorithm SUnSAL for which a pixel adaptive L1-norm regularization term is introduced. Our algorithm...
In the study on sports image classification, the characteristics of human pose increasingly raise concerns of researchers. However, the same posture for human may be resulted from different scenes and scene objects that express diverse action states and meanings. Thus, combination of human pose and event scenes shall be considered so as to improve performance of sports image classification. In recent...
Support Vector Machine (SVM) is widely recognized as a potent data mining technique for solving supervised learning problems. The technique has practical applications in many domains such as e-commerce product classification. However, data sets of large sizes in this application domain often present a negative repercussion for SVM coverage because its training complexity is highly dependent on input...
For hyperspectral image classification, feature extraction is a crucial pre-process for avoiding the Hughes phenomena. Some feature extraction methods such as linear discriminant analysis (LDA), nonparametric weighted feature extraction (NWFE), and their kernel versions, generalized discriminant analysis (GDA) and kernel nonparametric weighted feature extraction method (KNWFE) have been shown that...
Many research shows that we will encounter the Highes phenomenon when dealing with the high-dimensional data classification problem. In addition, non-linear support vector machine (SVM) has been shown that it can conquer the problem efficiently. However, the SVM is a black-box model based on the whole features and does not provide the feature importance or “good” feature subset for classification...
Classification of high-resolution remote-sensing images is a challenging research area. In this paper we proposed a novel decision fusion framework to combine bag of features (BOF) based classifiers. The proposed framework, can also be used in multi category image classification applications. A single voting algorithm is used for decision fusion and an ambiguity detection module is used to determine...
The Spatial Pyramid Matching approach has become very popular to model images as sets of local bag-of-words. The image comparison is then done region-by-region with an intersection kernel. Despite its success, this model presents some limitations: the grid partitioning is predefined and identical for all images and the matching is sensitive to intra- and inter-class variations. In this paper, we propose...
This paper introduces a regularization method called Correlative Filter (CF) for Convolutional Neural Network (CNN), which takes advantage of the relevance between the convolutional kernels belonging to the same convolutional layer. During the process of training with the proposed CF method, several pairs of filters are designed in a manner of randomness to contain opposite weights in low-level layers...
This paper presents a novel multikernel based Sparse representation for the classification of Remotely sensed images. The sparse representation based feature extraction are in a run which is a signal dependent feature extraction and thus more accurate. Multikernel Sparse representation was also had proved to be more accurate and less computationally complex while implemented in other applications...
This paper develops a new algorithm based on Bag-of-Word to reflect spatial relationship of objects for visual object categorization. Beyond existing spatial pyramid for image representation, our contributions are the following: 1) we propose a combinational detector based on Maximally Stable Extremal Regions detector and Hessian-Laplacian detector to extract more discriminative features; 2) for object...
Texture is the vital feature for remote sensing image classification, however, it is hard to be described and recognized by computer vision. As a result, lots of approaches have been presented to identify texture image. Among these methods, support vector machine (SVM) is the most successfully used one, which takes advantages of avoiding local optimum, conquering dimension disaster with small samples...
The high-resolution remote sensing image classification is an important research topic in pattern recognition, and its computational complexity grows exponentially with the increase of the dimension. Hence, it is necessary to perform feature dimension reduction. This paper presents a comparative study on state-of-the-art feature selection and feature transformation methods for the task of high-resolution...
Eminence of learning algorithm applied for computer vision tasks depends on the features engineered from image. It's premise that different representations can interweave and ensnare most of the elucidative genes that are responsible for variations in images, be it rigid, affine or projective. Hence researches give at most attention in hand-engineering features that capture these variations. But problem...
Image classification is currently a vital and challenging topic in computer vision. Although it has been achieved many classification algorithms so far, the classification of natural images still remains great difficulties in image processing. In this paper, we propose a semantic linear-time graph kernel for image classification. Each image is represented by a graph and the vertex of each graph corresponds...
Spatial pyramid (SP) representation is an extension of bag-of-feature model which embeds spatial layout information of local features by pooling feature codes over pre-defined spatial shapes. However, the uniform style of spatial pooling shapes used in standard SP is an ad-hoc manner without theoretical motivation, thus lacking the generalization power to adapt to different distribution of geometric...
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