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Classification is a well known of the significant tools used to recognize and examine most sharp information in images. Satellite image processing has become popular in these days because of benefits that those are giving. In any remote sensing particularly, the decision-making way mainly rely on the efficiency of the classification process. Image classification was performed generally and the classification...
Classification is one step of image processing which aims to obtain information from remote sensing data. Object oriented classification is a classification algorithm which can be used to process a high resolution image because it uses the object elements as spectral, spatial, and texture. The purpose of this study is to examine the application of object oriented classification on two types of images...
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...
In this paper, we propose a new method for hyperspectral image (HSI) classification using multi-layer superpixel graph and loopy belief propagation. A merging algorithm using graph based representation of image is applied to generate multi-scale superpixels in hyperspectral image at first. Then, we build a multi-layer superpixel graph and use loopy belief propagation to transmit messages between the...
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...
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...
Due to heavy clutters and occlusions of complex background, natural images contain complex features in data structure which often cause errors in image classification. In this paper, we propose semi-supervised bi-dictionary learning for image classification with smooth representation-based label propagation (SRLP) which extends reconstruction-based classification in a probabilistic manner. First,...
In this letter, a simple, yet very powerful local descriptor called local pattern descriptor (LPD) is proposed for synthetic aperture radar (SAR) images classification. The descriptor aims at exploiting the underlying properties of SAR image texture. Specifically, LPD consists of two parts: image quantization and statistical features extraction. The method of image quantization is based on recent...
This paper presents a novel road/terrain classification system based on the analysis of volunteered geographic information gathered by bikers. By ubiquitous collection of multi-sensor bike data, consisting of visual images, accelerometer information and GPS coordinates of the bikers' smartphone, the proposed system is able to distinguish between 6 different road/terrain types. In order to perform...
A heuristic utilizing both spectral and spatial information is proposed for active learning. It addresses the issue of iteratively querying most informative training samples with a special focus on spatial-contextual image classification. With the aim to utilize all information during the learning process, the proposed heuristic queries unlabeled pixels considering spectral-spatial inconsistency (SSI),...
With multiple channels, Polarimetric SAR (PolSAR) contains abundant target information and anti-jamming ability, which can improve the ability of target discrimination and image interpretation. The classification problem of PolSAR has become one of the most urgent problems to be solved in PolSAR application with the improvement of PolSAR technology. Due to the complexity of multiple-dimensional classification,...
An important aspect of research in the remote sensing field is to objectively compare different classifiers. This is the foundation of hundreds of research projects and in this paper we will address some raising concerns when evaluating solutions for classification of data sets with skewed class distributions. The quality of assessment is based on the problem specified by the user and the corresponding...
Hyperspectral image classification based on low-rank representation is considered. It is often assumed that major signals occupy a low-rank subspace, and the remaining component is sparse. Due to the mixed nature of hyperspectral data, the underlying data structure may include multiple subspaces instead of a single subspace. Therefore, in this paper, we propose to use low-rank subspace representation...
Remote sensing has much to gain from citizen sensing. This is particularly evident in relation to the provision of ground reference data for use in the training and testing stages of supervised image classification analyses used to generate thematic maps from remotely sensed data. Citizens are able to provide data over large geographical areas inexpensively, addressing potential problems connected...
We present a method based on Markov Random Fields (MRFs) for conducting decision level fusion of segments derived from multiple images of the same region. These images are not required to share the same resolution or sensor characteristics. By working at the segment level we preserve the advantages of segment based image classification while also incorporating the benefits of using multiple image...
Urban impervious surface mapping using moderate-resolution optical images such as Landsat images could be challenging due to the complexity of urban land cover. The study aims to combine optical and PolSAR images to improve accuracy of impervious surface classification. A scene of Landsat-5 TM image and a scene of RADARSAT-2 full-polarized imagery of Kitchener-Waterloo were used. The classification...
The present paper aims to review the role satellite remote sensing played during the response phase to the largest (in terms of mortality) natural disaster occurred in 2013, i.e. the tropical typhoon Haiyan that struck the Philippines in November 2013. The outcomes of a thorough analysis of the emergency mapping products (about 750 maps) released in the aftermath of the event and in the following...
This paper addresses the problem of parameter optimization for Markov random field (MRF) models for supervised classification of remote sensing images. MRF model parameters generally impact on classification accuracy, and their automatic optimization is still an open issue especially in the supervised case. The proposed approach combines a mean square error (MSE) formulation with Platt's sequential...
In this paper, we propose a theoretically new and effective feature for SAR image classification. The new feature combines traditional gray level co-occurrence matrix (GLCM) textural feature and the recent multilevel local pattern histogram (MLPH) feature. It can not only describe intrinsic property of land-cover/land-use surfaces, corresponding to textural information, but it also captures both local...
A novel polarimetric synthetic aperture radar (PolSAR) image classification method based on Deep Belief Networks (DBNs) is proposed in this paper. First, the coherency matrix data are converted to a 9-dimentional data. Second, many patches are randomly selected from each dimension in the 9-dimentional data, and many filters can be obtained from a Restricted Boltzmann Machine (RBM) trained by using...
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