Ji Zhao

A Hybrid Object-Oriented Conditional Random Field Classification Framework for High Spatial Resolution Remote Sensing Imagery

High spatial resolution (HSR) remote sensing imagery provides abundant geometric and detailed information, which is important for classification. In order to make full use of the spatial contextual information, object-oriented classification and pairwise conditional random fields (CRFs) are widely used. However, the segmentation scale choice is a challenging problem in object-oriented classification, and the classification result of pairwise CRF always has an oversmooth appearance. In this paper, a hybrid object-oriented CRF classification framework for HSR imagery, namely, CRF + OO, is proposed to address these problems by integrating object-oriented classification and CRF classification. In CRF + OO, a probabilistic pixel classification is first performed, and then, the classification results of two CRF models with different potential functions are used to obtain the segmentation map by a connected-component labeling algorithm. As a result, an object-level classification fusion scheme can be used, which integrates the object-oriented classifications using a majority voting strategy at the object level to obtain the final classification result. The experimental results using two multispectral HSR images (QuickBird and IKONOS) and a hyperspectral HSR image (HYDICE) demonstrate that the proposed classification framework has a competitive quantitative and qualitative performance for HSR image classification when compared with other state-of-the-art classification algorithms.

Detail-Preserving Smoothing Classifier Based on Conditional Random Fields for High Spatial Resolution Remote Sensing Imagery

In the field of high spatial resolution (HSR) remote sensing imagery classification, object-oriented classification and conditional random field (CRF) approaches are widely used due to their ability to incorporate the spatial contextual information. However, the selection of the optimal segmentation scale in object-oriented classification is not an easy task, and some pairwise CRF models always show an oversmooth performance. In this paper, a detail-preserving smoothing classifier based on conditional random fields (DPSCRF) for HSR imagery is proposed to apply the object-oriented strategy in the CRF classification framework, thus integrating the merits of both approaches to consider the spatial contextual information and preserve the detail information in the classification. The DPSCRF model defines suitable potential functions based on the CRF model for HSR image classification, which comprise the spatial smoothing and local class label cost terms. Both terms favor spatial smoothing in a local neighborhood to consider the spatial information. In addition, the local class label cost also considers the different label information of neighboring pixels at each iterative step in the classification to preserve the detail information. In order to deal with the spectral variability of HSR imagery, a segmentation prior is used by the object-oriented processing strategy. This models the probability of each pixel based on the segmentation regions obtained by the connected-component labeling algorithm. The experimental results with three HSR images demonstrate that the proposed classification algorithm shows a competitive performance in both the quantitative and the qualitative evaluation when compared to the other state-of-the-art classification algorithms.

High-Resolution Image Classification Integrating Spectral-Spatial-Location Cues by Conditional Random Fields

With the increase in the availability of high-resolution remote sensing imagery, classification is becoming an increasingly useful technique for providing a large area of detailed land-cover information by the use of these high-resolution images. High-resolution images have the characteristics of abundant geometric and detail information, which are beneficial to detailed classification. In order to make full use of these characteristics, a classification algorithm based on conditional random fields (CRFs) is presented in this paper. The proposed algorithm integrates spectral, spatial contextual, and spatial location cues by modeling the probabilistic potentials. The spectral cues modeled by the unary potentials can provide basic information for discriminating the various land-cover classes. The pairwise potentials consider the spatial contextual information by establishing the neighboring interactions between pixels to favor spatial smoothing. The spatial location cues are explicitly encoded in the higher order potentials. The higher order potentials consider the nonlocal range of the spatial location interactions between the target pixel and its nearest training samples. This can provide useful information for the classes that are easily confused with other land-cover types in the spectral appearance. The proposed algorithm integrates spectral, spatial contextual, and spatial location cues within a CRF framework to provide complementary information from varying perspectives, so that it can address the common problem of spectral variability in remote sensing images, which is directly reflected in the accuracy of each class and the average accuracy. The experimental results with three high-resolution images show the validity of the algorithm, compared with the other state-of-the-art classification algorithms.

Sub-Pixel Mapping Based on Conditional Random Fields for Hyperspectral Remote Sensing Imagery

Sub-pixel mapping is a useful technique for providing land-cover information at the sub-pixel scale by the use of the input fraction image at a coarse resolution. Some sub-pixel mapping algorithms with strict consideration of the abundance constraint have difficulty in obtaining a satisfactory performance in sub-pixel mapping since the fraction image obtained by spectral unmixing always contains errors. In this paper, in order to make full use of the input fraction image and alleviate the effect of fraction errors, a sub-pixel mapping algorithm based on conditional random fields (CRFSM) is proposed for hyperspectral remote sensing imagery. The CRFSM algorithm fuses the local spatial prior at the fine scale and the downscaled coarse fraction at the coarse scale by potential functions to obtain more detailed land-cover distribution information. The local spatial prior models the local spatial structure to obtain the local land-cover features at the fine scale. The downscaled coarse fraction considers the fraction values to maintain the holistic land-cover features at the coarse scale. In addition, the abundance constraint is considered as a soft constraint by the probability class determination strategy in the CRFSM algorithm, to help with the class label determination of sub-pixels and alleviate the effect of the fraction errors and noise. The experimental results with two synthetic hyperspectral images and a real Nuance hyperspectral image show that the proposed sub-pixel mapping algorithm has a competitive performance in both the quantitative and qualitative evaluations, compared with the other state-of-the-art sub-pixel mapping algorithms.

Change Detection Based on Pulse-Coupled Neural Networks and the NMI Feature for High Spatial Resolution Remote Sensing Imagery

In this letter, a change detection algorithm based on pulse-coupled neural networks (PCNN) and the normalized moment of inertia (NMI) feature is proposed for high spatial resolution (HSR) remote sensing imagery. To better analyze a large remote sensing image, the whole image is divided into blocks by the use of a deblocking mechanism. The PCNN model is utilized to obtain the initial binary image, and the NMI feature is calculated based on the binary image to detect the hot spot changed areas. Finally, the changed areas are processed by expectation-maximization to obtain the final change map. The experimental results using QuickBird and IKONOS images demonstrate that the proposed algorithm has the ability to provide better change detection results for HSR images than the traditional PCNN change detection algorithms.

Change Detection Based on a Multifeature Probabilistic Ensemble Conditional Random Field Model for High Spatial Resolution Remote Sensing Imagery

In this letter, a multifeature probabilistic ensemble conditional random field (MFPECRF) model is proposed to perform the task of change detection for high spatial resolution (HSR) remote sensing imagery. MFPECRF not only considers the spectral feature of single pixels but also the interaction between neighborhood pixels and the structural property of the ground objects in HSR imagery to give a higher detection accuracy than the traditional random field methods, which only utilize spectral and label information. In the unary potential, the spectral and morphological features of the difference image are combined using a probabilistic ensemble strategy, and the pairwise potential considers the contextual information of the observed field. The parameters of MFPECRF are estimated using a piecewise strategy, and the final result is obtained by the use of the loopy belief propagation algorithm. The experimental results of two groups of HSR multispectral images confirm the potential of the proposed method in improving the detection accuracy for HSR imagery.

Optimal Decision Fusion for Urban Land-Use/Land-Cover Classification Based on Adaptive Differential Evolution Using Hyperspectral and LiDAR Data

Hyperspectral images and light detection and ranging (LiDAR) data have, respectively, the high spectral resolution and accurate elevation information required for urban land-use/land-cover (LULC) classification. To combine the respective advantages of hyperspectral and LiDAR data, this paper proposes an optimal decision fusion method based on adaptive differential evolution, namely ODF-ADE, for urban LULC classification. In the ODF-ADE framework the normalized difference vegetation index (NDVI), gray-level co-occurrence matrix (GLCM) and digital surface model (DSM) are extracted to form the feature map. The three different classifiers of the maximum likelihood classifier (MLC), support vector machine (SVM) and multinomial logistic regression (MLR) are used to classify the extracted features. To find the optimal weights for the different classification maps, weighted voting is used to obtain the classification result and the weights of each classification map are optimized by the differential evolution algorithm which uses a self-adaptive strategy to obtain the parameter adaptively. The final classification map is obtained after post-processing based on conditional random fields (CRF). The experimental results confirm that the proposed algorithm is very effective in urban LULC classification.

Spatial-Spectral-Emissivity Land-Cover Classification Fusing Visible and Thermal Infrared Hyperspectral Imagery

High-resolution visible remote sensing imagery and thermal infrared hyperspectral imagery are potential data sources for land-cover classification. In this paper, in order to make full use of these two types of imagery, a spatial-spectral-emissivity land-cover classification method based on the fusion of visible and thermal infrared hyperspectral imagery is proposed, namely, SSECRF (spatial-spectral-emissivity land-cover classification based on conditional random fields). A spectral-spatial feature set is constructed considering the spectral variability and spatial-contextual information, to extract features from the high-resolution visible image. The emissivity is retrieved from the thermal infrared hyperspectral image by the FLAASH-IR algorithm and firstly introduced in the fusion of the visible and thermal infrared hyperspectral imagery; also, the emissivity is utilized in SSECRF, which contributes to improving the identification of man-made objects, such as roads and roofs. To complete the land-cover classification, the spatial-spectral feature set and emissivity are integrated by constructing the SSECRF energy function, which relates labels to the spatial-spectral-emissivity features, to obtain an improved classification result. The classification map performs a good result in distinguishing some certain classes, such as roads and bare soil. Also, the experimental results show that the proposed SSECRF algorithm efficiently integrates the spatial, spectral, and emissivity information and performs better than the traditional methods using raw radiance from thermal infrared hyperspectral imagery data, with a kappa value of 0.9137.

Aurora Image Classification Based on Multi-Feature Latent Dirichlet Allocation

Due to the rich physical meaning of aurora morphology, the classification of aurora images is an important task for polar scientific expeditions. However, the traditional classification methods do not make full use of the different features of aurora images, and the dimension of the description features is usually so high that it reduces the efficiency. In this paper, through combining multiple features extracted from aurora images, an aurora image classification method based on multi-feature latent Dirichlet allocation (AI-MFLDA) is proposed. Different types of features, whether local or global, discrete or continuous, can be integrated after being transformed to one-dimensional (1-D) histograms, and the dimension of the description features can be reduced due to using only a few topics to represent the aurora images. In the experiments, according to the classification system provided by the Polar Research Institute of China, a four-class aurora image dataset was tested and three types of features (MeanStd, scale-invariant feature transform (SIFT), and shape-based invariant texture index (SITI)) were utilized. The experimental results showed that, compared to the traditional methods, the proposed AI-MFLDA is able to achieve a better performance with 98.2% average classification accuracy while maintaining a low feature dimension.

Change detection based on structural conditional random field framework for high spatial resolution remote sensing imagery

In this paper, a structural conditional random field framework (SCRF) is proposed to detect the detailed change information from high spatial resolution (HSR) remote sensing imagery. Traditional random field based methods encounter the over-smoothing problem when deal with HSR images and the boundary of changed objects cannot be preserved well. To solve this problem, in SCRF, fuzzy c means (FCM) is used to model the unary potential while avoiding the independent assumption. Pairwise potentials with different shapes are selected as the structural set to model the spatial features of land cover such as buildings and roads. Based on SCRF, a set of change belief maps are generated to describe the observed image from different aspects. An object based fusion strategy is then followed to combine the belief maps to get the refined result. The results of the proposed method on two HSR data sets outperform some state-of-art algorithms.