As the volume of remotely sensed data grows significantly, content-based image retrieval (CBIR) becomes increasingly important, especially for cloud computing platforms that facilitate processing and storing big data in a parallel and distributed way. This paper proposes a novel parallel CBIR system for hyperspectral image (HSI) repository on cloud computing platforms under the guide of unmixed spectral information, i.e., endmembers and their associated fractional abundances, to retrieve hyperspectral scenes. However, existing unmixing methods would suffer extremely high computational burden when extracting meta-data from large-scale HSI data. To address this limitation, we implement a distributed and parallel unmixing method that operates on cloud computing platforms in parallel for accelerating the unmixing processing flow. In addition, we implement a global standard distributed HSI repository equipped with a large spectral library in a software-as-a-service mode, providing users with HSI storage, management, and retrieval services through web interfaces. Furthermore, the parallel implementation of unmixing processing is incorporated into the CBIR system to establish the parallel unmixing-based content retrieval system. The performance of our proposed parallel CBIR system was verified in terms of both unmixing efficiency and accuracy.

Hyperspectral image super-resolution aims to fuse a low spatial resolution hyperspectral image (LR-HSI) and a high spatial resolution multispectral image (HR-MSI) to obtain a high-resolution hyperspectral image (HR-HSI). Tensor-based methods have demonstrated their outstanding ability in constructing the relationship between the LR-HSI and the HR-MSI. This paper introduces a nonlocal hierarchical Tucker decomposition (HTD) model for hyperspectral and multispectral image (HSI-MSI) fusion. First, similar nonlocal patch tensors are clustered according to their similarity in the HR-MSI. Next, the spatial/spectral relationship between the LR-HSI and the HR-MSI is extracted through HTD. The alternating direction method of multipliers (ADMM) is employed to solve the proposed model. Furthermore, to overcome the high computational complexity of the model solver, we propose an efficient distributed and parallel method to accelerate the fusion process. Experimental results demonstrate that the proposed method not only substantially outperforms state-of-the-art HSI-MSI fusion methods, but also achieves a significant acceleration rate.

Low-rank representation is a popularly used method in remote sensing and image processing systems. Existing low-rank representation-based algorithms often neglect the varying sensitivities of different pixels to the dictionary which may lead to inaccurate detection. Also, the dense iterative computations involved in these algorithms could induce high computation overheads. This paper proposes an efficient low-rank representation method for hyperspectral anomaly detection on a CPU-FPGA hybrid computing platform. The proposed method starts with segmenting the hyperspectral image into superpixels based on the selected number of dictionary categories. The image is divided into center and edge parts using a sliding window along the segmentation boundaries. Then, we use a low-rank and sparse representation to process center pixels that are well-adapted to the dictionary, and a weighted low-rank and collaborative representation model to process edge pixels that are less adaptive to the dictionary. The aforementioned two representation models are integrated to obtain the final reconstructed image. Moreover, we propose the Nesterov acceleration method by incorporating an adaptive step size into the linearized alternating direction method with adaptive penalty. At the hardware level, the acceleration method is deployed on CPU-FPGA platform to speed up the proposed hyperspectral anomaly detection flow. Experimental results demonstrate that the proposed method outperforms most existing methods in detection accuracy and achieves significant improvement in computational efficiency.

Hyperspectral image (HSI) super-resolution, which aims at improving the spatial quality of HSIs by fusing a low spatial resolution HSI (LR-HSI) with a high spatial resolution multispectral image (HR-MSI), has drawn significant attention. Numerous LR-HSI and HR-MSI (HSI–MSI) fusion algorithms have emerged in recent times, yet they suffer from a lack of generality and integration, which hampers their usability for nonexpert users. Moreover, these algorithms encounter significant challenges due to the exponential increase in remote sensing data volume. In this study, we propose a unified cloud-based framework for HSI–MSI fusion based on the general distributed alternating direction method of multipliers that incorporates nonlocal principles and tensor decomposition. The framework not only provides end-users with visualization modeling capabilities equipped with standard and comprehensive components but also enhances the parallel processing capabilities of cloud computing. We use a newly proposed nonlocal adaptive low-rank coupled tensor canonical polyadic (CP) decomposition algorithm as a case study to evaluate the performance of this framework. Specifically, we establish the LR-HSIs and HR-MSIs’ relationship using order-4 coupled tensor CP decomposition and suggest an adaptive CP rank estimation method for achieving better super-resolution results. The experimental results on publicly available datasets demonstrate that the proposed parallel distributed optimization algorithm can achieve significant speedup with guaranteed accuracy. The proposed framework enables convenient and efficient processing of large-scale remote sensing data, effectively addressing the challenges associated with handling large data volumes. The source code of our method is released and available online at https://github.com/ZpWaitingForSunshine/DNAC4TCP/ .

As the volume and value of hyperspectral remote sensing images increasing, a common hyperspectral imagery repository is urgently required. This paper presents a novel distributed content retrieval method for hyperspectral imagery repository based on cloud computing platform. The proposed method uses a software-as-a-service (SaaS) mode, which provides users with services such as hyperspectral image management, storage and retrieval through a web interface. In order to accelerate the acquisition of the spectral feature information needed in the retrieval process, we optimize the procedure of endmember extraction and the abundance estimation in a distributed way on Spark platform. The generated spectral features meta-data are stored in the MySQL database for imagery retrieval. Finally, we introduce the parallel implementation of distributed retrieval of hyperspectral images based on N-FINDR in details, and evaluate the effectiveness and stability of our proposed method through experiments on real datasets.

In this letter, we propose a patch-wise temporal-spatial feature aggregation (PTFA) network for object detection in satellite video. Firstly, the feature extractor processes the key frame along with its support frames to ensure comprehensive spatial coverage of potential objects. Subsequently, we model the semantic similarities among instance-level proposals to exploring robust interaction between temporally adjacent support frames and key frame. Furthermore, due to the extremely small size of objects in satellite video, we crop the input frames to different patches by the fixed criterion. Then, the temporal-spatial feature aggregation operations are performed on instance-level RoI features, which attains more nuanced and comprehensive descriptors from the explicit high-resolution temporal-spatial features. The patch-features are reconstructed to the original one for complementing more valid feature responses. Finally, we compare our PTFA network with many recent works on SAT-MTB dataset. Extensive experiments demonstrate that our method achieves the state-of-the-art performance than various static image and video object detection approaches.