Microscopic hyperspectral images provide a new way for blood cell examination. The hyperspectral imagery can greatly facilitate the classification of different blood cells. In this paper, the microscopic hyperspectral images are acquired by connecting the microscope and the hyperspectral imager, and then tested for blood cell classification. For combined use of the spectral and spatial information provided by hyperspectral images, a spatial-spectral classification method is improved from the classical extreme learning machine (ELM) by integrating spatial context into the image classification task with Markov random field (MRF) model. Comparisons are done among ELM, ELM-MRF, support vector machines(SVM) and SVMMRF methods. Results show the spatial-spectral classification methods(ELM-MRF, SVM-MRF) perform better than pixel-based methods(ELM, SVM), and the proposed ELM-MRF has higher precision and show more accurate location of cells.
A one-class sparse representation classifier (OCSRC) is proposed to solve the multitemporal change detection problem for identifying disaster affected areas. The OCSRC method, which is adapted from a sparse representation classifier (SRC), incorporates the one-class strategy from a one-class support vector machine (OCSVM) to seek accurate representation for the class of changed areas. It assumes that pixels from the changed areas can be well represented by samples from this class, thus the representation errors are taken as the possibilities of change. Performances of OCSRC and OCSVM are tested and compared with multitemporal multispectral HJ-1A images acquired in Heilongjiang Province before and after the flood in 2013. The entire image, together with two subimages, are used for overall comparison and detailed discussion. Receiver-operating-characteristics curve results show that OCSRC outperforms OCSVM by a lower false-positive rate at a defined true-positive rate (TPR), and the gap is more obvious with high TPR values. The same outcome is also manifested in the change detection image results, with less misclassified pixels for OCSRC at certain TPR values, which implies a more accurate description of the changed area.
Sparse representation-based classification (SRC) has gained great interest recently.
A pixel to be classified is sparsely approximately by labeled samples, and it is assigned to the
class whose labeled samples provide the smallest representation error. In this paper, we extend
SRC by exploiting the benefits of using a smoothing filter based on sparse gradient
minimization. The smoothing filter is expected to provide less intra class variability and more
spatial regularity, which eliminating the inherent variations within a small neighborhood.
Classification performance on two real hyperspectral datasets demonstrates that our proposed
method has improved classification accuracy and the resulting accuracies are persistently
higher at all small training sample size situations compared to some traditional classifiers.
An efficient classification framework for mapping agricultural tillage practice using hyperspectral remote sensing imagery is proposed, which has the potential to be implemented practically to provide rapid, accurate, and objective surveying data for precision agricultural management and appraisal from large-scale remote sensing images. It includes a local region filter [i.e., Gaussian low-pass filter (GLF)] to extract spatial-spectral features, a dimensionality reduction process [i.e., local fisher’s discriminate analysis (LFDA)], and the traditional k-nearest neighbor (KNN) classifier, and is denoted as GLF-LFDA-KNN. Compared to our previously used local average filter and adaptive weighted filter, the GLF also considers spatial features in a small neighborhood, but it emphasizes the central pixel itself and is data-independent; therefore, it can achieve the balance between classification accuracy and computational complexity. The KNN classifier has a lower computational complexity compared to the traditional support vector machine (SVM). After classification separability is enhanced by the GLF and LFDA, the less powerful KNN can outperform SVM and the overall computational cost remains lower. The proposed framework can also outperform the SVM with composite kernel (SVM-CK) that uses spatial-spectral features.
New imaging mode has been brought up for collecting multiple scenes in one pass, as is implemented on World View-II.
This greatly helps for acquiring high spatial resolution images that cover urban areas, and is to be adopted in the coming
Chinese satellites. This paper is to discuss the mosaic characteristic and propose a mosaic line generation method by
integrating correlation and the road information. The mosaic line is formed by linking the unique mosaic point on each
line restricted within the road. We position the starting point by connectivity analysis of the road lines, and then locate
the adjacent point along the road with connectivity analysis. A weighed vector, combining correlation and distance to
centre of the road, is used to pick the best point. The points are located on the road unless it is unavoidable, for example,
the road ends or the line touches edge of the image. This method provides instant mosaic line generation for urban areas
with road information available in most cases. By resorting to the road, the mosaic line is more applicable since many
problems for mosaic of high spatial resolution images are solved, for example, tilting of the buildings, the shadows,
motions of the vehicles etc. Experiments have been done with WV-II images and gained favorable results.
Two improved local-region filters, adaptive weighted filter (AWF) and collaborative representation filter (CoRF), are proposed for feature extraction and classification in hyperspectral imagery. The local-region filters generate spatial-spectral features of a hyperspectral pixel by incorporating its surrounding pixels. The work of this paper is an extension of our previously introduced local average filter (LAF). Unlike LAF, which gives the surrounding pixels the same weight, AWF and CoRF explore the internal similarity in the local region with an adaptive weight. More specifically, AWF is set up considering the spatial distance to the central pixel, and CoRF is constructed with spectral similarities adopting the idea of collaborative representation. The two improved local-region filters adaptively extract spectral-spatial features from neighboring pixels and are proven to be effective in many aspects, such as edge information preservation and classification performance, with experiments on two real hyperspectral datasets.
Recently, representation-based classifications have gained increasing interest in hyperspectral imagery, such as the newly proposed sparse-representation classification and nearest-regularized subspace (NRS). These classifiers provide excellent performance that is comparable to or even better than the classic support vector machine. However, all these representation-based methods were originally designed to be pixel-wise classifiers which only consider the spectral signature while ignoring the spatial-contextual information. A Markov random field (MRF), providing a basis for modeling contextual constraints, has currently been successfully applied for hyperspectral image analysis. We mainly investigate the benefits of combining these representation-based classifications with an MRF model in order to acquire better classification results. Two real hyperspectral images are used to validate the proposed classification scheme. Experimental results demonstrated that the proposed method significantly outperforms other state-of-the-art approaches. For example, NRS-MRF performed with an accuracy of 94.92% for the Reflective Optics System Imaging Spectrometer data with 60 training samples per class, while the original NRS obtained an accuracy of 81.95%, an improvement of approximately 13%.
This paper presents whole pre-processing streamline of Beijing-1 small satellite images and focuses on some of the key
issues in specific or improved data acquisition and processing. Characteristics of small satellite and peculiarities in the
image pre-processing are analyzed, design and skeleton of the
pre-processing system is expounded, and then, some of the
key issues encountered and explored during processing of Beijing-1 small satellite data are discussed. The discussed
issues include relative calibration, onboard compression, jitter removal and exposure control. The works in this paper are
done with integration exploration based on systematic consideration of whole imaging and processing process, and are
all testified with practical implementation.
Striping noise and image degradation are the main factors that reduce the quality of Beijing-1 small satellite raw data
images, thus noise removal and image restoration are the two important tasks in processing and application of the
images. This paper presents efficient noise removal and image restoration methods on analysis of the imaging system
characteristics and the image quality reduction principles. The proposed methods evidently improve quality of the
images, and are employed in practical processing procedures of Beijing-1 small satellite images.