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The hyperspectral image has the advantages of wide spectral range and the high spectral resolution, and is widely applied in the terrain classification. In this paper, we study the airborne hyperspectral image classification methods using the airborne hyperspectral image. Considering the hyperspectral image has amounts of bands and there is redundancy among the bands, the principle component analysis...
In this paper the Supervised Locally Linear Embedding (SLLE) algorithm is introduced into polarimetric SAR (PolSAR) feature dimensionality reduction (DR) and land cover classification. SLLE technique, as a supervised nonlinear manifold learning method, can obtain a low-dimensional embedding space which preserves both the local geometric property of high-dimensional data and discriminative information...
This paper proposes a novel solution to solve the problem of imbalanced training samples in hyperspectral image classification. It consists of two parts: one is for large-size sample sets and the other is for small-size sets. We exploit an orthogonal projection based algorithm to select samples from large-size ones; meanwhile, we propose an algorithm based on the orthogonal complementary subspace...
In classification, a large number of features often make it difficult to select appropriate classification features. In such situations, feature selection or dimensionality reduction methods play an important role in classification. ReliefF algorithm is one of the most successful filtering feature selection methods. In this paper, some shortcomings of the ReliefF algorithm are improved, on the problem...
Nowadays, hyperspectral images have been an attractive subject for many researches in remote sensing area since they provide abundant information due to their wide range of spectral bands. On the one hand, classification plays a significant role in extraction of information for different applications. On the other hand, providing a huge amount of data by hyperspectral images may lead to complexity...
The lack of a comprehensive solution for image information mining has often brought confusion and misunderstanding when Earth Observation data based application scenarios were addressed. Considering the variety of dedicated sensors available nowadays, the particularities of the recorded data raises serious issues when explored. Most of the proposed methodologies for data analysis integrate algorithms...
QUEST (Quick, Unbiased, and Efficient Statistical Tree) algorithm has been widely applied to the remote sensing characteristic classification, for its high speed and precision compared to other decision tree algorithms. This paper took Chaoyang, western Liaoning province as the study area and integrated RS (Remote Sensing), GIS (Geographic Information System) and QUEST to classify the groundwater...
Nonnegative Matrix Factorization (NMF) factorizes a nonnegative matrix into product of two positive matrixes, which is widely used in hyperspectral unmixing. However, the convergence speed of NMF is comparatively slower, and a large number of local minimum will be existed when it is directly adopted in the factorization of hyperspectral image mixed pixels. A modified hyperspectral unmixing method...
Although the graph-based machine learning has received considerable attention in the remote sensing area and it has been widely used for terrain classification, the construction of graph in most existing algorithms still takes large memory and plenty of computational time especially for large Polarimetric Synthetic Aperture Radar (PolSAR) data. Addressing these issues, we propose a fast semi-supervised...
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...
Statistically Homogeneous Pixels (SHPs) selection is a significant step of multi-temporal interferometric synthetic aperture radar (InSAR) for Distributed Scatterers (DS). A series of studies namely, Anderson-Darling test (AD test) and its variants, have demonstrated their advantages. However, these algorithms have a similar drawback that they put little attention on the spatial amplitude distributions...
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