Zhiguo Jiang

Ship Detection in High-Resolution Optical Imagery Based on Anomaly Detector and Local Shape Feature

Ship detection in high-resolution optical imagery is a challenging task due to the variable appearances of ships and background. This paper aims at further investigating this problem and presents an approach to detect ships in a “coarse-to-fine” manner. First, to increase the separability between ships and background, we concentrate on the pixels in the vicinities of ships. We rearrange the spatially adjacent pixels into a vector, transforming the panchromatic image into a “fake” hyperspectral form. Through this procedure, each produced vector is endowed with some contextual information, which amplifies the separability between ships and background. Afterward, for the “fake” hyperspectral image, a hyperspectral algorithm is applied to extract ship candidates preliminarily and quickly by regarding ships as anomalies. Finally, to validate real ships out of ship candidates, an extra feature is provided with histograms of oriented gradients (HOGs) to generate a hypothesis using AdaBoost algorithm. This extra feature focuses on the gray values rather than the gradients of an image and includes some information generated by very near but not closely adjacent pixels, which can reinforce HOG to some degree. Experimental results on real database indicate that the hyperspectral algorithm is robust, even for the ships with low contrast. In addition, in terms of the shape of ships, the extended HOG feature turns out to be better than HOG itself as well as some other features such as local binary pattern.

Subspace Matching Pursuit for Sparse Unmixing of Hyperspectral Data

Sparse unmixing assumes that each mixed pixel in the hyperspectral image can be expressed as a linear combination of only a few spectra (endmembers) in a spectral library, known a priori. It then aims at estimating the fractional abundances of these endmembers in the scene. Unfortunately, because of the usually high correlation of the spectral library, the sparse unmixing problem still remains a great challenge. Moreover, most related work focuses on the l1 convex relaxation methods, and little attention has been paid to the use of simultaneous sparse representation via greedy algorithms (GAs) (SGA) for sparse unmixing. SGA has advantages such as that it can get an approximate solution for the l0 problem directly without smoothing the penalty term in a low computational complexity as well as exploit the spatial information of the hyperspectral data. Thus, it is necessary to explore the potential of using such algorithms for sparse unmixing. Inspired by the existing SGA methods, this paper presents a novel GA termed subspace matching pursuit (SMP) for sparse unmixing of hyperspectral data. SMP makes use of the low-degree mixed pixels in the hyperspectral image to iteratively find a subspace to reconstruct the hyperspectral data. It is proved that, under certain conditions, SMP can recover the optimal endmembers from the spectral library. Moreover, SMP can serve as a dictionary pruning algorithm. Thus, it can boost other sparse unmixing algorithms, making them more accurate and time efficient. Experimental results on both synthetic and real data demonstrate the efficacy of the proposed algorithm.

PDE-based unsupervised repair of hair-occluded information in dermoscopy images of melanoma

The repair of hair-occluded information is one of the key problems for the precise segmentation and analysis of the skin malignant melanoma image with hairs. Aimed at dermoscopy images of pigmented skin lesions, an unsupervised repair algorithm for the hair-occluded information is proposed in this paper. This algorithm includes three steps: first, the melanoma image with hairs are enhanced by morphologic closing-based top-hat operator and then segmented through statistic threshold; second, the hairs are extracted based on the elongate of connected region; third, the hair-occluded information is repaired by the PDE-based image inpainting. As a matter of fact, with the morphologic closing-based top-hat operator both strong and weak hairs can be enhanced simultaneously, and the elongate state of band-like connected region can be correctly described by the elongate function proposed in this paper so as to measure the hair effectively. Therefore, the unsupervised repair problem of the hair-occluded information can be resolved very well through combining the hair extracting with the image inpainting technology. The experiment results show that the repaired images can satisfy the requirement of medical diagnosis by the proposed algorithm and the segmentation veracity is effectively improved after repairing the hair-occluded information.

Haze Removal for a Single Remote Sensing Image Based on Deformed Haze Imaging Model

The contrast of remote sensing images captured in haze condition is poor, which influences their interpretation. In this letter, a novel dehazing algorithm based on the deformed haze imaging model is proposed. First, the model is deformed by introducing a translation term. Second, the atmospheric light and transmission are estimated according to the new model combined with dark channel prior. Lastly, the haze is successfully removed from remote sensing images using the proposed estimation algorithm. The estimated transmission is insensitive to the texture of ground objects, and the dehazing effect for nonuniform haze is more satisfactory than the compared method. Moreover, our approach can be used for general haze removal through adjusting the translation term. Experimental results reveal that the proposed method can recover the real scene clearly from haze remote sensing images along with the advantage of good color consistency.

Hyperspectral image fusion by the similarity measure-based variational method

Hyperspectral remote sensing is widely used in many fields suchas agriculture, military detection, mineral exploration, and so on. Hyperspectral data has very high spectral resolution, but much lower spatial resolution than the data obtained by other types of sensors. The low spatial resolution restrains its wide applications. On the contrary, we easily obtain images with high spatial resolution but insufficient spectral resolution (like panchromatic images). Naturally, people expect to obtain images that have high spatial and spectral resolution at the same time by the hyperspectral image fusion. In this paper, a similarity measure-based variational method is proposed to achieve the fusion process. The main idea is to transform the image fusion problem to an optimization problem based on the variational model. We first establish a fusion model that constrains the spatial and spectral information of the original data at the same time, then use the split bregman iteration to obtain the final fused data. Also, we analyze the convergence of the method. The experiments on the synthetic and real data show that the fusion method preserves the information of the original images efficiently, especially on the spectral information.

Melanoma Classification on Dermoscopy Images Using a Neural Network Ensemble Model

We develop a novel method for classifying melanocytic tumors as benign or malignant by the analysis of digital dermoscopy images. The algorithm follows three steps: first, lesions are extracted using a self-generating neural network (SGNN); second, features descriptive of tumor color, texture and border are extracted; and third, lesion objects are classified using a classifier based on a neural network ensemble model. In clinical situations, lesions occur that are too large to be entirely contained within the dermoscopy image. To deal with this difficult presentation, new border features are proposed, which are able to effectively characterize border irregularities on both complete lesions and incomplete lesions. In our model, a network ensemble classifier is designed that combines back propagation (BP) neural networks with fuzzy neural networks to achieve improved performance. Experiments are carried out on two diverse dermoscopy databases that include images of both the xanthous and caucasian races. The results show that classification accuracy is greatly enhanced by the use of the new border features and the proposed classifier model.

No Reference Uneven Illumination Assessment for Dermoscopy Images

For the dermoscopy image, uneven illumination will influence segmentation accuracy and lead to wrong aided diagnosis result. In this paper, a no reference uneven illumination assessment metric is proposed for dermoscopy images. Firstly, the distorted image is decomposed to illumination and reflectance components through variational framework for Retinex (VFR). Then, the illumination component is extracted by basis function fitting. Lastly, average gradient of the illumination component (AGIC) is calculated as the uneven illumination metric. A series of experiments show that, the proposed illumination extraction method is insensitive to the image content, and the proposed metric delivers an accurate illumination assessment result.

A Hierarchical Connection Graph Algorithm for Gable-Roof Detection in Aerial Image

In this letter, we present a hierarchical connection graph (HCG) algorithm based on a self-avoiding polygon (SAP) model for detecting and extracting gable roofs from aerial imagery. The SAP model is a deformable shape model that is capable of representing gable roofs of various shapes and appearances. The model is composed of a sequence of roof-corner templates that are connected into a SAP, which serves as a flexible shape prior. An energy function that combines features from three channels (corner, boundary, and interior area) is defined over the sequence to quantify the variability in appearances of gable roofs. To infer the most probable state of the corner sequence for an input image, we use an efficient algorithm-called HCG algorithm. The algorithm converts the solution space of a SAP model into a directed graph (which we call “HCG”) and searches for the best path using dynamic programming (DP). It is efficient for two reasons: 1) By constructing an HCG, the algorithm can quickly prune out a large amount of invalid solutions using only geometric constraints, which are inexpensive to compute, and 2) by employing DP, the algorithm decomposes the searching problem into smaller overlapping subproblems and reuses energy scores, which are expensive to compute. Experimental results on a set of challenging gable roofs show that our algorithm has good performance and is computationally effective.

A Novel Spectral-Unmixing-Based Green Algae Area Estimation Method for GOCI Data

Geostationary Ocean Color Imager (GOCI) data have been widely used in the detection and area estimation of green algae blooms. However, due to the low spatial resolution of GOCI data, pixels in an image are usually “mixed,” which means that the region a pixel covers may include many different materials. Traditional index-based methods can detect whether there are green algal blooms in each pixel, whereas it is still challenging to determine the proportion that green algae blooms occupy in a pixel. In this paper, we propose a novel subpixel-level area estimation method for green algae blooms based on spectral unmixing, which can not only detect the existence of green algae but also determine their proportion in each pixel. A fast endmember extraction method is proposed to automatically calculate the endmember spectral matrix, and the abundance map of green algae which could be regarded as the area estimation is obtained by nonnegatively constrained least squares. This new fast endmembers extraction technique outperforms the classical N-FINDR method by applying two models: candidates location and distance-based vertices determination. In the first model, we propose a medium-distance-based candidates location strategy, which could reduce the searching space during vertices selection. In the second model, we replace the simplex volume measure with a more simple distance measure, thus complex matrix determinant calculation is avoided. We have theoretically proven the equivalency of volume and distance measure. Experiments on GOCI data and synthetic data demonstrate the superiority of the proposed method compared with some state-of-art approaches.

No Reference Quality Assessment for Multiply-Distorted Images Based on an Improved Bag-of-Words Model

Multiple distortion assessment is a big challenge in image quality assessment (IQA). In this letter, a no reference IQA model for multiply-distorted images is proposed. The features, which are sensitive to each distortion type even in the presence of other distortions, are first selected from three kinds of NSS features. An improved Bag-of-Words (BoW) model is then applied to encode the selected features. Lastly, a simple yet effective linear combination is used to map the image features to the quality score. The combination weights are obtained through lasso regression. A series of experiments show that the feature selection strategy and the improved BoW model are effective in improving the accuracy of quality prediction for multiple distortion IQA. Compared with other algorithms, the proposed method delivers the best result for multiple distortion IQA.