Multiple Feature Learning for Hyperspectral Image Classification

Li, Jun; Huang, Xin; Gamba, Paolo; Bioucas-Dias, José M.; Zhang, Liangpei; Benediktsson, Jón Atli; Plaza, Antonio

doi:10.1109/tgrs.2014.2345739

Cited by 296 publications

(103 citation statements)

References 35 publications

Supporting

Mentioning

101

Contrasting

Unclassified

Order By: Relevance

“…We conduct different experiments using training sets of 50, 100, 150 and 200 random samples from each class respectively. A similar training map selection was made in [35] for the Indian Pines and Pavia University dataset. Please note that if for a specific class the number of samples are less than the number required for the experiment, then we select randomly 50% of the total available class samples.…”

Section: High-order Nonlinear Classification Modelmentioning

confidence: 99%

Tensor-Based Classification Models for Hyperspectral Data Analysis

Makantasis

Doulamis

Nikitakis

2018

IEEE Trans. Geosci. Remote Sensing

110

View full text Add to dashboard Cite

Abstract-In this paper, we present tensor-based linear and nonlinear models for hyperspectral data classification and analysis. By exploiting principles of tensor algebra, we introduce new classification architectures, the weight parameters of which satisfy the rank-1 canonical decomposition property. Then, we propose learning algorithms to train both linear and non-linear classifiers. The advantages of the proposed classification approach are that i) it significantly reduces the number of weight parameters required to train the model (and thus the respective number of training samples), ii) it provides a physical interpretation of model coefficients on the classification output and iii) it retains the spatial and spectral coherency of the input samples. The linear tensor-based model exploits principles of logistic regression assuming the rank-1 canonical decomposition property among its weights. For the non-linear classifier, we propose a modification of a feedforward neural network (FNN), called rank-1 FNN, since its weights satisfy again the rank-1 canonical decomposition property. An appropriate learning algorithm is also proposed to train the network. Experimental results and comparisons with state of the art classification methods, either linear (e.g., Linear SVM) or non-linear (e.g., deep learning) indicates the outperformance of the proposed scheme, especially in cases where a small number of training samples is available.

show abstract

Section: High-order Nonlinear Classification Modelmentioning

confidence: 99%

Tensor-Based Classification Models for Hyperspectral Data Analysis

Makantasis

Doulamis

Nikitakis

2018

IEEE Trans. Geosci. Remote Sensing

110

View full text Add to dashboard Cite

show abstract

“…Furthermore, we present an efficient combination of early and late fusion of color and texture based on CBPT. There are many feature fusion methods in the literature [1][2][3][4][5], most of which being characterized by late fusion of color and texture; i.e., the multiple cues are combined in the classification process. Particularly, CTS implements an efficient combination of color, texture and structure based on CBPT, and achieves the early fusion of local regions and late fusion in the classification process.…”

Section: Discussionmentioning

confidence: 99%

“…However, the efficient combination of fine spectral, textural and structural information toward achieving reliable and consistent HRS satellite image classification remains problematic [1][2][3][4][5]. This article addresses this challenge by presenting a new descriptor for object categorization and scene classification using HRS images.…”

Section: Introductionmentioning

confidence: 99%

See 1 more Smart Citation

A Color-Texture-Structure Descriptor for High-Resolution Satellite Image Classification

Yang

Xia

et al. 2016

Remote Sensing

View full text Add to dashboard Cite

Scene classification plays an important role in understanding high-resolution satellite (HRS) remotely sensed imagery. For remotely sensed scenes, both color information and texture information provide the discriminative ability in classification tasks. In recent years, substantial performance gains in HRS image classification have been reported in the literature. One branch of research combines multiple complementary features based on various aspects such as texture, color and structure. Two methods are commonly used to combine these features: early fusion and late fusion. In this paper, we propose combining the two methods under a tree of regions and present a new descriptor to encode color, texture and structure features using a hierarchical structure-Color Binary Partition Tree (CBPT), which we call the CTS descriptor. Specifically, we first build the hierarchical representation of HRS imagery using the CBPT. Then we quantize the texture and color features of dense regions. Next, we analyze and extract the co-occurrence patterns of regions based on the hierarchical structure. Finally, we encode local descriptors to obtain the final CTS descriptor and test its discriminative capability using object categorization and scene classification with HRS images. The proposed descriptor contains the spectral, textural and structural information of the HRS imagery and is also robust to changes in illuminant color, scale, orientation and contrast. The experimental results demonstrate that the proposed CTS descriptor achieves competitive classification results compared with state-of-the-art algorithms.

show abstract

“…Incorporating the spatial information into pixel-wise classifiers has also demonstrated potential improvement recently due to its noticeable advantages in exploiting additional relevant information from the spatial domain [18]. On the one hand, several refined versions of SVM have been proposed.…”

Section: Introductionmentioning

confidence: 99%

Tensor Block-Sparsity Based Representation for Spectral-Spatial Hyperspectral Image Classification

Liu

2016

Remote Sensing

Self Cite

View full text Add to dashboard Cite

Abstract:Recently, sparse representation has yielded successful results in hyperspectral image (HSI) classification. In the sparse representation-based classifiers (SRCs), a more discriminative representation that preserves the spectral-spatial information can be exploited by treating the HSI as a whole entity. Based on this observation, a tensor block-sparsity based representation method is proposed for spectral-spatial classification of HSI in this paper. Unlike traditional vector/matrix-based SRCs, the proposed method consists of tensor block-sparsity based dictionary learning and class-dependent block sparse representation. By naturally regarding the HSI cube as a third-order tensor, small local patches centered at the training samples are extracted from the HSI to maintain the structural information. All the patches are then partitioned into a number of groups, on which a dictionary learning model is constructed with a tensor block-sparsity constraint. A test sample is also expressed as a small local patch and the block sparse representation is then performed in a class-wise manner to take advantage of the class label information. Finally, the category of the test sample is determined by using the minimal residual. Experimental results of two real-world HSIs show that our proposed method greatly improves the classification performance of SRC.

show abstract

Multiple Feature Learning for Hyperspectral Image Classification

Cited by 296 publications

References 35 publications

Tensor-Based Classification Models for Hyperspectral Data Analysis

Tensor-Based Classification Models for Hyperspectral Data Analysis

A Color-Texture-Structure Descriptor for High-Resolution Satellite Image Classification

Tensor Block-Sparsity Based Representation for Spectral-Spatial Hyperspectral Image Classification

Contact Info

Product

Resources

About