The low spatial resolution associated with imaging spectrometers has caused subpixel target detection to become a special problem in hyperspectral image (HSI) processing that poses considerable challenges. In subpixel target detection, the size of the target is smaller than that of a pixel, making the spatial information of the target almost useless so that a detection algorithm must rely on the spectral information of the image. To address this problem, this article proposes a subpixel target detection algorithm for hyperspectral remote sensing imagery based on background endmember extraction. First, we propose a background endmember extraction algorithm based on robust nonnegative dictionary learning to obtain the background endmember spectrum of the image. Next, we construct a hyperspectral subpixel target detector based on pixel reconstruction (HSPRD) to perform pixel-by-pixel target detection on the image to be tested using the background endmember spectral matrix and the spectra of known ground targets. Finally, the subpixel target detection results are obtained. The experimental results show that, compared with other existing subpixel target detection methods, the algorithm proposed here can provide the optimum target detection results for both synthetic and real-world data sets.

Processing and applications of hyperspectral images (HSI) are limited by the noise component. This paper establishes an HSI denoising algorithm by applying dictionary learning and sparse coding theory, which is extended into the spectral domain. First, the HSI noise model under additive noise assumption was studied. Considering the spectral information of HSI data, a novel dictionary learning method based on an online method is proposed to train the spectral dictionary for denoising. With the spatial–contextual information in the noisy HSI exploited as a priori knowledge, the total variation regularizer is introduced to perform the sparse coding. Finally, sparse reconstruction is implemented to produce the denoised HSI. The performance of the proposed approach is better than the existing algorithms. The experiments illustrate that the denoising result obtained by the proposed algorithm is at least 1 dB better than that of the comparison algorithms. The intrinsic details of both spatial and spectral structures can be preserved after significant denoising.

Due to the sparsity of hyperspectral images, the dictionary learning framework has been applied in hyperspectral endmember extraction. However, current endmember extraction methods based on dictionary learning are not robust enough in noisy environments. To solve this problem, this paper proposes a novel endmember extraction approach based on online robust dictionary learning, termed EEORDL. Because of the large scale of the hyperspectral image (HSI) data, an online scheme is introduced to reduce the computational time of dictionary learning. In the proposed algorithm, a new form of the objective function is introduced into the dictionary learning process to improve the robustness for noisy HSI data. The experimental results, conducted with both synthetic and real-world hyperspectral datasets, illustrate that the proposed EEORDL outperforms the state-of-the-art approaches under different signal-to-noise ratio (SNR) conditions, especially for high-level noise.

To address the problems of defective pixels and strips in hyperspectral images affecting subsequent processing and applications, we modeled the hyperspectral image (HSI) inpainting problem as a sparse signal reconstruction problem with incomplete observations using the theory of sparse representation, and proposed an HSI inpainting algorithm based on spectral dictionary learning. First, we studied the HSI observation model under the assumption of additive noise. We subsequently proposed a new algorithm for constructing a spectral dictionary directly from hyperspectral data by introducing an online learning optimization method and performing dictionary learning using a robust function. Afterwards, the image was sparsely encoded by applying the variable decomposition and augmented Lagrangian sparse regression method. Finally, the inpainted HSI was obtained by sparse reconstruction. The experimental results showed that compared with the existing algorithms, the algorithm proposed herein could effectively inpaint the defective HSI under different noise conditions with a shorter calculation time than those of existing methods and other dictionary learning inpainting algorithms.

Considering the sparsity of hyperspectral images (HSIs), dictionary learning frameworks have been widely used in the field of unsupervised spectral unmixing. However, it is worth mentioning here that existing dictionary learning method-based unmixing methods are found to be short of robustness in noisy contexts. To improve the performance, this study specifically puts forward a new unsupervised spectral unmixing solution. For the reason that the solution only functions in a condition that both endmembers and the abundances meet non-negative constraints, a model is built to solve the unsupervised spectral unmixing problem on the account of the dictionary learning method. To raise the screening accuracy of final members, a new form of the target function is introduced into dictionary learning practice, which is conducive to the growing robustness of noisy HSI statistics. Then, by introducing the total variation (TV) terms into the proposed spectral unmixing based on robust nonnegative dictionary learning (RNDLSU), the context information under HSI space is to be cited as prior knowledge to compute the abundances when performing sparse unmixing operations. According to the final results of the experiment, this method makes favorable performance under varying noise conditions, which is especially true under low signal to noise conditions.