Spectral–Spatial Classification of Hyperspectral Imagery with 3D Convolutional Neural Network

Recent research has shown that using spectral–spatial information can considerably improve the performance of hyperspectral image (HSI) classification. HSI data is typically presented in the format of 3D cubes. Thus, 3D spatial filtering naturally offers a simple and effective method for simultaneously extracting the spectral–spatial features within such images. In this paper, a 3D convolutional neural network (3D-CNN) framework is proposed for accurate HSI classification. The proposed method views the HSI cube data altogether without relying on any preprocessing or post-processing, extracting the deep spectral–spatial-combined features effectively. In addition, it requires fewer parameters than other deep learning-based methods. Thus, the model is lighter, less likely to over-fit, and easier to train. For comparison and validation, we test the proposed method along with three other deep learning-based HSI classification methods—namely, stacked autoencoder (SAE), deep brief network (DBN), and 2D-CNN-based methods—on three real-world HSI datasets captured by different sensors. Experimental results demonstrate that our 3D-CNN-based method outperforms these state-of-the-art methods and sets a new record

Large kernel spectral and spatial attention networks for hyperspectral image classification.

Currently, long-range spectral and spatial dependencies have been widely demonstrated to be essential for hyperspectral image (HSI) classification. Due to the transformer superior ability to exploit long-range representations, the transformer-based methods have exhibited enormous potential. However, existing transformer-based approaches still face two crucial issues that hinder the further performance promotion of HSI classification: 1) treating HSI as 1D sequences neglects spatial properties of HSI, 2) the dependence between spectral and spatial information is not fully considered. To tackle the above problems, a large kernel spectral-spatial attention network (LKSSAN) is proposed to capture the long-range 3D properties of HSI, which is inspired by the visual attention network (VAN). Specifically, a spectral-spatial attention module is first proposed to effectively exploit discriminative 3D spectral-spatial features while keeping the 3D structure of HSI. This module introduces the large kernel attention (LKA) and convolution feed-forward (CFF) to flexibly emphasize, model, and exploit the long-range 3D feature dependencies with lower computational pressure. Finally, the features from the spectral-spatial attention module are fed into the classification module for the optimization of 3D spectral-spatial representation. To verify the effectiveness of the proposed classification method, experiments are executed on four widely used HSI data sets. The experiments demonstrate that LKSSAN is indeed an effective way for long-range 3D feature extraction of HSI

Fast Numerical Algorithms for 3-D Scattering from PEC and Dielectric Random Rough Surfaces in Microwave Remote Sensing

abstract: We present fast and robust numerical algorithms for 3-D scattering from perfectly electrical conducting (PEC) and dielectric random rough surfaces in microwave remote sensing. The Coifman wavelets or Coiflets are employed to implement Galerkin’s procedure in the method of moments (MoM). Due to the high-precision one-point quadrature, the Coiflets yield fast evaluations of the most off-diagonal entries, reducing the matrix fill effort from O(N^2) to O(N). The orthogonality and Riesz basis of the Coiflets generate well conditioned impedance matrix, with rapid convergence for the conjugate gradient solver. The resulting impedance matrix is further sparsified by the matrix-formed standard fast wavelet transform (SFWT). By properly selecting multiresolution levels of the total transformation matrix, the solution precision can be enhanced while matrix sparsity and memory consumption have not been noticeably sacrificed. The unified fast scattering algorithm for dielectric random rough surfaces can asymptotically reduce to the PEC case when the loss tangent grows extremely large. Numerical results demonstrate that the reduced PEC model does not suffer from ill-posed problems. Compared with previous publications and laboratory measurements, good agreement is observed.Dissertation/ThesisDoctoral Dissertation Electrical Engineering 201

Low-Shot Learning for the Semantic Segmentation of Remote Sensing Imagery

Deep-learning frameworks have made remarkable progress thanks to the creation of large annotated datasets such as ImageNet, which has over one million training images. Although this works well for color (RGB) imagery, labeled datasets for other sensor modalities (e.g., multispectral and hyperspectral) are minuscule in comparison. This is because annotated datasets are expensive and man-power intensive to complete; and since this would be impractical to accomplish for each type of sensor, current state-of-the-art approaches in computer vision are not ideal for remote sensing problems. The shortage of annotated remote sensing imagery beyond the visual spectrum has forced researchers to embrace unsupervised feature extracting frameworks. These features are learned on a per-image basis, so they tend to not generalize well across other datasets. In this dissertation, we propose three new strategies for learning feature extracting frameworks with only a small quantity of annotated image data; including 1) self-taught feature learning, 2) domain adaptation with synthetic imagery, and 3) semi-supervised classification. ``Self-taught\u27\u27 feature learning frameworks are trained with large quantities of unlabeled imagery, and then these networks extract spatial-spectral features from annotated data for supervised classification. Synthetic remote sensing imagery can be used to boot-strap a deep convolutional neural network, and then we can fine-tune the network with real imagery. Semi-supervised classifiers prevent overfitting by jointly optimizing the supervised classification task along side one or more unsupervised learning tasks (i.e., reconstruction). Although obtaining large quantities of annotated image data would be ideal, our work shows that we can make due with less cost-prohibitive methods which are more practical to the end-user

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