Spectral-spatial Feature Extraction for Hyperspectral Image Classification

As an emerging technology, hyperspectral imaging provides huge opportunities in both remote sensing and computer vision. The advantage of hyperspectral imaging comes from the high resolution and wide range in the electromagnetic spectral domain which reflects the intrinsic properties of object materials. By combining spatial and spectral information, it is possible to extract more comprehensive and discriminative representation for objects of interest than traditional methods, thus facilitating the basic pattern recognition tasks, such as object detection, recognition, and classification. With advanced imaging technologies gradually available for universities and industry, there is an increased demand to develop new methods which can fully explore the information embedded in hyperspectral images. In this thesis, three spectral-spatial feature extraction methods are developed for salient object detection, hyperspectral face recognition, and remote sensing image classification. Object detection is an important task for many applications based on hyperspectral imaging. While most traditional methods rely on the pixel-wise spectral response, many recent efforts have been put on extracting spectral-spatial features. In the first approach, we extend Itti's visual saliency model to the spectral domain and introduce the spectral-spatial distribution based saliency model for object detection. This procedure enables the extraction of salient spectral features in the scale space, which is related to the material property and spatial layout of objects. Traditional 2D face recognition has been studied for many years and achieved great success. Nonetheless, there is high demand to explore unrevealed information other than structures and textures in spatial domain in faces. Hyperspectral imaging meets such requirements by providing additional spectral information on objects, in completion to the traditional spatial features extracted in 2D images. In the second approach, we propose a novel 3D high-order texture pattern descriptor for hyperspectral face recognition, which effectively exploits both spatial and spectral features in hyperspectral images. Based on the local derivative pattern, our method encodes hyperspectral faces with multi-directional derivatives and binarization function in spectral-spatial space. Compared to traditional face recognition methods, our method can describe distinctive micro-patterns which integrate the spatial and spectral information of faces. Mathematical morphology operations are limited to extracting spatial feature in two-dimensional data and cannot cope with hyperspectral images due to so-called ordering problem. In the third approach, we propose a novel multi-dimensional morphology descriptor, tensor morphology profile~(TMP), for hyperspectral image classification. TMP is a general framework to extract multi-dimensional structures in high-dimensional data. The n-order morphology profile is proposed to work with the n-order tensor, which can capture the inner high order structures. By treating a hyperspectral image as a tensor, it is possible to extend the morphology to high dimensional data so that powerful morphological tools can be used to analyze hyperspectral images with fused spectral-spatial information. At last, we discuss the sampling strategy for the evaluation of spectral-spatial methods in remote sensing hyperspectral image classification. We find that traditional pixel-based random sampling strategy for spectral processing will lead to unfair or biased performance evaluation in the spectral-spatial processing context. When training and testing samples are randomly drawn from the same image, the dependence caused by overlap between them may be artificially enhanced by some spatial processing methods. It is hard to determine whether the improvement of classification accuracy is caused by incorporating spatial information into the classifier or by increasing the overlap between training and testing samples. To partially solve this problem, we propose a novel controlled random sampling strategy for spectral-spatial methods. It can significantly reduce the overlap between training and testing samples and provides more objective and accurate evaluation

Spectral-spatial classification of hyperspectral images: three tricks and a new supervised learning setting

Spectral-spatial classification of hyperspectral images has been the subject of many studies in recent years. In the presence of only very few labeled pixels, this task becomes challenging. In this paper we address the following two research questions: 1) Can a simple neural network with just a single hidden layer achieve state of the art performance in the presence of few labeled pixels? 2) How is the performance of hyperspectral image classification methods affected when using disjoint train and test sets? We give a positive answer to the first question by using three tricks within a very basic shallow Convolutional Neural Network (CNN) architecture: a tailored loss function, and smooth- and label-based data augmentation. The tailored loss function enforces that neighborhood wavelengths have similar contributions to the features generated during training. A new label-based technique here proposed favors selection of pixels in smaller classes, which is beneficial in the presence of very few labeled pixels and skewed class distributions. To address the second question, we introduce a new sampling procedure to generate disjoint train and test set. Then the train set is used to obtain the CNN model, which is then applied to pixels in the test set to estimate their labels. We assess the efficacy of the simple neural network method on five publicly available hyperspectral images. On these images our method significantly outperforms considered baselines. Notably, with just 1% of labeled pixels per class, on these datasets our method achieves an accuracy that goes from 86.42% (challenging dataset) to 99.52% (easy dataset). Furthermore we show that the simple neural network method improves over other baselines in the new challenging supervised setting. Our analysis substantiates the highly beneficial effect of using the entire image (so train and test data) for constructing a model.Comment: Remote Sensing 201

Object identification and characterization with hyperspectral imagery to identify structure and function of Natura 2000 habitats

Habitat monitoring of designated areas under the EU Habitats Directive requires every 6 years information on area, range, structure and function for the protected (Annex I) habitat types. First results from studies on heathland areas in Belgium and the Netherlands show that hyperspectral imagery can be an important source of information to assist the evaluation of the habitat conservation status. Hyperspectral imagery can provide continuous maps of habitat quality indicators (e.g., life forms or structure types, management activities, grass, shrub and tree encroachment) at the pixel level. At the same time, terrain managers, nature conservation agencies and national authorities responsible for the reporting to the EU are not directly interested in pixels, but rather in information at the level of vegetation patches, groups of patches or the protected site as a whole. Such local level information is needed for management purposes, e.g., exact location of patches of habitat types and the sizes and quality of these patches within a protected site. Site complexity determines not only the classification success of remote sensing imagery, but influences also the results of aggregation of information from the pixel to the site level. For all these reasons, it is important to identify and characterize the vegetation patches. This paper focuses on the use of segmentation techniques to identify relevant vegetation patches in combination with spectral mixture analysis of hyperspectral imagery from the Airborne Hyperspectral Scanner (AHS). Comparison with traditional vegetation maps shows that the habitat or vegetation patches can be identified by segmentation of hyperspectral imagery. This paper shows that spectral mixture analysis in combination with segmentation techniques on hyperspectral imagery can provide useful information on processes such as grass encroachment that determine the conservation status of Natura 2000 heathland areas to a large extent. A limitation is that both advanced remote sensing approaches and traditional field based vegetation surveys seem to cause over and underestimations of grass encroachment for specific categories, but the first provides a better basis for monitoring if specific species are not directly considered

Locality and Structure Regularized Low Rank Representation for Hyperspectral Image Classification

Hyperspectral image (HSI) classification, which aims to assign an accurate label for hyperspectral pixels, has drawn great interest in recent years. Although low rank representation (LRR) has been used to classify HSI, its ability to segment each class from the whole HSI data has not been exploited fully yet. LRR has a good capacity to capture the underlying lowdimensional subspaces embedded in original data. However, there are still two drawbacks for LRR. First, LRR does not consider the local geometric structure within data, which makes the local correlation among neighboring data easily ignored. Second, the representation obtained by solving LRR is not discriminative enough to separate different data. In this paper, a novel locality and structure regularized low rank representation (LSLRR) model is proposed for HSI classification. To overcome the above limitations, we present locality constraint criterion (LCC) and structure preserving strategy (SPS) to improve the classical LRR. Specifically, we introduce a new distance metric, which combines both spatial and spectral features, to explore the local similarity of pixels. Thus, the global and local structures of HSI data can be exploited sufficiently. Besides, we propose a structure constraint to make the representation have a near block-diagonal structure. This helps to determine the final classification labels directly. Extensive experiments have been conducted on three popular HSI datasets. And the experimental results demonstrate that the proposed LSLRR outperforms other state-of-the-art methods.Comment: 14 pages, 7 figures, TGRS201