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Multispectral (MS) and hyperspectral (HS) images have been successfully and widely used in remote sensing applications such as target detection, change detection, and anomaly detection. In this paper, we aim at reviewing recent change detection papers and raising some challenges and opportunities in the field from a practitioner's viewpoint using MS and HS images. For example, can we perform change detection using synthetic hyperspectral images? Can we use temporally-fused images to perform change detection? Some of these areas are ongoing and will require more research attention in the coming years. Moreover, in order to understand the context of our paper, some recent and representative algorithms in change detection using MS and HS images are included, and their advantages and disadvantages will be highlighted. [ABSTRACT FROM AUTHOR]

Hyperspectral images (HSIs), captured by different Earth observation airborne and space-borne systems, provide rich spectral information in hundreds of bands, enabling far better discrimination between ground materials that are often indistinguishable in visible and multi-spectral images. Clustering of HSIs, which aims to unveil class patterns in an unsupervised way, is highly important in the interpretation of HSI, especially when labelled data are not available. A number of HSI clustering methods have been proposed. Among them, model-based optimization algorithms, which learn the cluster structure of data by solving convex/non-convex optimization problems, have achieved the current state-of-the-art performance. Recent works extend the model-based algorithms to deep versions with deep neural networks, obtaining huge breakthroughs in clustering performance. However, a systematic survey on the topic is absent. This article provides a comprehensive overview of clustering methods of HSI and tracked the latest techniques and breakthroughs in the domain, including the traditional model-based optimization algorithms and the emerging deep learning based clustering methods. With a new taxonomy, we elaborated on the main ideas, technical details, advantages, and disadvantages of different types of clustering methods of HSIs. We provided a systematic performance comparison between different clustering methods by conducting extensive experiments on real HSIs. Unsolved problems and future research trends in the domain are pointed out. Moreover, we provided a toolbox that contains implementations of representative clustering algorithms to help researchers to develop their own models. [ABSTRACT FROM AUTHOR]

Images gathered from different satellites are vastly available these days due to the fast development of remote sensing (RS) technology. These images significantly enhance the data sources of change detection (CD). CD is a technique of recognizing the dissimilarities in the images acquired at distinct intervals and are used for numerous applications, such as urban area development, disaster management, land cover object identification, etc. In recent years, deep learning (DL) techniques have been used tremendously in change detection processes, where it has achieved great success because of their practical applications. Some researchers have even claimed that DL approaches outperform traditional approaches and enhance change detection accuracy. Therefore, this review focuses on deep learning techniques, such as supervised, unsupervised, and semi-supervised for different change detection datasets, such as SAR, multispectral, hyperspectral, VHR, and heterogeneous images, and their advantages and disadvantages will be highlighted. In the end, some significant challenges are discussed to understand the context of improvements in change detection datasets and deep learning models. Overall, this review will be beneficial for the future development of CD methods. [ABSTRACT FROM AUTHOR]

Hyperspectral sensors that are mounted in unmanned aerial vehicles (UAVs) offer many benefits for different remote sensing applications by combining the capacity of acquiring a high amount of information that allows for distinguishing or identifying different materials, and the flexibility of the UAVs for planning different kind of flying missions. However, further developments are still needed to take advantage of the combination of these technologies for applications that require a supervised or semi-supervised process, such as defense, surveillance, or search and rescue missions. The main reason is that, in these scenarios, the acquired data typically need to be rapidly transferred to a ground station where it can be processed and/or visualized in real-time by an operator for taking decisions on the fly. This is a very challenging task due to the high acquisition data rate of the hyperspectral sensors and the limited transmission bandwidth. This research focuses on providing a working solution to the described problem by rapidly compressing the acquired hyperspectral data prior to its transmission to the ground station. It has been tested using two different NVIDIA boards as on-board computers, the Jetson Xavier NX and the Jetson Nano. The Lossy Compression Algorithm for Hyperspectral Image Systems (HyperLCA) has been used for compressing the acquired data. The entire process, including the data compression and transmission, has been optimized and parallelized at different levels, while also using the Low Power Graphics Processing Units (LPGPUs) embedded in the Jetson boards. Finally, several tests have been carried out to evaluate the overall performance of the proposed design. The obtained results demonstrate the achievement of real-time performance when using the Jetson Xavier NX for all the configurations that could potentially be used during a real mission. However, when using the Jetson Nano, real-time performance has only been achieved when using the less restrictive configurations, which leaves room for further improvements and optimizations in order to reduce the computational burden of the overall design and increase its efficiency. [ABSTRACT FROM AUTHOR]

This article presents a semi-fragile image tampering detection method for multi-band images. In the proposed scheme, a mark is embedded into remote sensing images, which have multiple frequential values for each pixel, applying tree-structured vector quantization. The mark is not embedded into each frequency band separately, but all the spectral values (known as signature) are used. The mark is embedded in the signature as a means to detect if the original image has been forged. The image is partitioned into three-dimensional blocks with varying sizes. The size of these blocks and the embedded mark is determined by the entropy of each region. The image blocks contain areas that have similar pixel values and represent smooth regions in multispectral or hyperspectral images. Each block is first transformed using the discrete wavelet transform. Then, a tree-structured vector quantizer (TSVQ) is constructed from the low-frequency region of each block. An iterative algorithm is applied to the generated trees until the resulting tree fulfils a requisite criterion. More precisely, the TSVQ tree that matches a particular value of entropy and provides a near-optimal value according to Shannon's rate-distortion function is selected. The proposed method is shown to be able to preserve the embedded mark under lossy compression (above a given threshold) but, at the same time, it detects possibly forged blocks and their positions in the whole image. Experimental results show how the scheme can be applied to detect forgery attacks, and JPEG2000 compression of the images can be applied without removing the authentication mark. The scheme is also compared to other works in the literature. [ABSTRACT FROM AUTHOR]