Due to the advantages of deep learning, in this paper, a regularized deep feature extraction (FE) method is presented for hyperspectral image (HSI) classification using a convolutional neural network (CNN). The proposed approach employs several convolutional and pooling layers to extract deep features from HSIs, which are nonlinear, discriminant, and invariant. These features are useful for image classification and target detection. Furthermore, in order to address the common issue of imbalance between high dimensionality and limited availability of training samples for the classification of HSI, a few strategies such as L2 regularization and dropout are investigated to avoid overfitting in class data modeling. More importantly, we propose a 3-D CNN-based FE model with combined regularization to extract effective spectral-spatial features of hyperspectral imagery. Finally, in order to further improve the performance, a virtual sample enhanced method is proposed. The proposed approaches are carried out on three widely used hyperspectral data sets: Indian Pines, University of Pavia, and Kennedy Space Center. The obtained results reveal that the proposed models with sparse constraints provide competitive results to state-of-the-art methods. In addition, the proposed deep FE opens a new window for further research.

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Deep Feature Extraction and Classification of Hyperspectral Images Based on Convolutional Neural Networks

Deep-learning (DL) algorithms, which learn the representative and discriminative features in a hierarchical manner from the data, have recently become a hotspot in the machine-learning area and have been introduced into the geoscience and remote sensing (RS) community for RS big data analysis. Considering the low-level features (e.g., spectral and texture) as the bottom level, the output feature representation from the top level of the network can be directly fed into a subsequent classifier for pixel-based classification. As a matter of fact, by carefully addressing the practical demands in RS applications and designing the input?output levels of the whole network, we have found that DL is actually everywhere in RS data analysis: from the traditional topics of image preprocessing, pixel-based classification, and target recognition, to the recent challenging tasks of high-level semantic feature extraction and RS scene understanding.

Deep Learning for Remote Sensing Data: A Technical Tutorial on the State of the Art

Hyperspectral remote sensing technology has advanced significantly in the past two decades. Current sensors onboard airborne and spaceborne platforms cover large areas of the Earth surface with unprecedented spectral, spatial, and temporal resolutions. These characteristics enable a myriad of applications requiring fine identification of materials or estimation of physical parameters. Very often, these applications rely on sophisticated and complex data analysis methods. The sources of difficulties are, namely, the high dimensionality and size of the hyperspectral data, the spectral mixing (linear and nonlinear), and the degradation mechanisms associated to the measurement process such as noise and atmospheric effects. This paper presents a tutorial/overview cross section of some relevant hyperspectral data analysis methods and algorithms, organized in six main topics: data fusion, unmixing, classification, target detection, physical parameter retrieval, and fast computing. In all topics, we describe the state-of-the-art, provide illustrative examples, and point to future challenges and research directions.

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Hyperspectral Remote Sensing Data Analysis and Future Challenges

https://rslab.disi.unitn.it/papers/R63-RSE-Plaza.pdf

Recent Advances in Techniques for Hyperspectral Image Processing

Classification of hyperspectral data with high spatial resolution from urban areas is investigated. A method based on mathematical morphology for preprocessing of the hyperspectral data is proposed. In this approach, opening and closing morphological transforms are used in order to isolate bright (opening) and dark (closing) structures in images, where bright/dark means brighter/darker than the surrounding features in the images. A morphological profile is constructed based on the repeated use of openings and closings with a structuring element of increasing size, starting with one original image. In order to apply the morphological approach to hyperspectral data, principal components of the hyperspectral imagery are computed. The most significant principal components are used as base images for an extended morphological profile, i.e., a profile based on more than one original image. In experiments, two hyperspectral urban datasets are classified. The proposed method is used as a preprocessing method for a neural network classifier and compared to more conventional classification methods with different types of statistical computations and feature extraction.

/pdf/classification-of-hyperspectral-data-from-urban-areas-based-5fhmcdwtol.pdf

Classification of hyperspectral data from urban areas based on extended morphological profiles

Very high resolution hyperspectral data should be very useful to provide detailed maps of urban land cover. In order to provide such maps, both accurate and precise classification tools need, however, to be developed. In this letter, new methods for classification of hyperspectral remote sensing data are investigated, with the primary focus on multiple classifications and spatial analysis to improve mapping accuracy in urban areas. In particular, we compare spatial reclassification and mathematical morphology approaches. We show results for classification of DAIS data over the town of Pavia, in northern Italy. Classification maps of two test areas are given, and the overall and individual class accuracies are analyzed with respect to the parameters of the proposed classification procedures.

Exploiting spectral and spatial information in hyperspectral urban data with high resolution

Classification of high-resolution hyperspectral data is investigated. Previously, in classification of high-resolution panchromatic data, simple morphological profiles have been constructed with a repeated use of morphological opening and closing operators with a structuring element of increasing size, starting with the original panchromatic image. This approach has recently been extended for hyperspectral data. In the extension, principal components of the hyperspectral imagery have been computed in order to produce an extended morphological profile. In this paper, we investigate the use of independent components instead of principal components in extended morphological profiles, i.e., selected independent components are used as base images for an extended morphological profile. In the proposed approach, the extended morphological profiles based on the independent components are used as inputs to a neural network classifier. In experiments, a hyperspectral data sets from an urban area in Pavia, Italy is classified.

Classification of hyperspectral data from urban areas using morphological preprocessing and independent component analysis

The classification of urban data with high spectral and spatial resolution is considered. For processing, a morphological profile is constructed. The morphological profile is based on the repeated use of opening and closings with a differently sized structuring element. Morphological profiles have been shown to contain redundancies. Therefore, feature extraction is applied on the profile. The morphological approach is applied in experiments on high resolution DAIS remote sensing data from an urban area. To apply the morphological approach on the DAIS data, the first principal component is used as a basis for the morphological transformations. In experiments, the use of the morphological method performs well in terms of classification accuracies. With feature extraction, it is observed that classification on reduced features gives higher accuracies than in the original feature space.

Morphological transformations and feature extraction of urban data with high spectral and spatial resolution

Classification of hyperspectral data with high spatial resolution from urban areas is discussed. An approach has been proposed which is based on using several principal components from the hyperspectral data and build morphological profiles. These profiles are used all together in one extended morphological profile, which is then classified by a neural network. A shortcoming of the approach is that it is primarily designed for classification of urban structures and it does not fully utilize the spectral information in the data. An extension is proposed in this paper in order to overcome the problems with the extended morphological profile approach. The proposed method is based on the data fusion of the morphological information and the original data. The proposed approach is tested in experiments on two different high resolution remote sensing data sets from urban areas. The results are excellent and significant improvements are achieved in terms of accuracies when compared to results of approaches based on the use of morphological profiles based on PCs only and conventional statistical approaches.

J.A. Palmason

Papers

Classification of hyperspectral data from urban areas based on extended morphological profiles

Exploiting spectral and spatial information in hyperspectral urban data with high resolution

Classification of hyperspectral data from urban areas using morphological preprocessing and independent component analysis

Morphological transformations and feature extraction of urban data with high spectral and spatial resolution

Fusion of Morphological and Spectral Information for Classification of Hyperspectal Urban Remote Sensing Data