Optical remote sensing instruments accumulate abundant data from across all Earth's land surfaces, making it possible both to understand the effects of climate change and to monitor, investigate and manage ground-level events in detail. Processing data using resources located near on-board satellite sensors can bring major benefits in terms of minimizing analysis time and quickly initiating active actions in critical situations. In satellite missions, long-term production on-board algorithms may encounter unexplored samples, i.e., abnormal ground-level events, and need to be able to discriminate and take the correct action. In this matter, the authors present a field programmable gate array (FPGA)-based solution for natural anomaly detection in multispectral imagery using deep convolutional neural networks. The effects of weather-induced hazards and natural disasters, considered anomalies in this sense, are discovered by modeling an anomaly detector on a hybrid system that is hardware efficient. The proposed approach is assembled on a Xilinx Zynq UltraScale+ XCZU9EG Multi-Processor System-on-Chip (MPSoC) device, where a deep convolutional model is scaled into the FPGA logic, followed by a downstream statistical meta-recognition predictor. The proposed anomaly detection accelerator has produced notable results on identifying a contemporary natural hazard, i.e., burned areas, in scenes acquired by Sentinel-2 over Europe, i.e., Spain and France. The implemented algorithm achieved on the FPGA accelerator an equivalent speedup of 4.46x and 4.5x lower power consumption than the equivalent implementation on the Tesla K80 GPU.

In the past decade, anomaly detection has experienced an expanding attraction in satellite data analysis. Monitoring wildfire dynamics plays a substantial part in global land management, i.e., to detect and determine the expansion of such areas, estimate the deterioration of forest regions, and assist the intervention plan. In this paper, we proposed an approach for Sentinel-2 scenes that detects anomalies in burned area contexts using a rank-ordered method on a single post-event image. We adopted a self-supervised paradigm in learning image representations by training a deep convolutional model to differentiate between a series of geometric transformations. Dirichlet distributions are selected as priors to characterize the variability of random multinomial distribution in multispectral data. Dirichlet precision parameters are computed from observed data and are used to construct a ranking function that quantifies the degree of anomaly in data based on softmax responses given by the trained classifier. We evaluated the performance of the proposed method on a cumulative effort of two remote sensing tasks, open-set detection (i.e., test datasets contain classes unseen at the training time) and location separation (i.e., test datasets include images from distinct spatial location than the training images). The experiments were performed on three different datasets, BigEarthNet and two actual burned area Sentinel-2 datasets, i.e., from predisposed zones to fire events, Australia, respectively Bolivia. We retained in all test datasets state-of-the-art performance, considering the substantial and diversified types of natural anomalies in multispectral data.

Land cover changes may have very different nature, e.g., vegetation development, soil erosion, variation of humidity, or damage of buildings, only to enumerate few cases. In addition, synthetic aperture radar (SAR) observations are a doppelganger of the scene, imaging the scene signature rather than the scene itself. To overcome these challenges, SAR change detection methods generally adapt to the particular situations. We present seamless methods based on normalized compression distance (NCD) estimation. NCD is a similarity metric applied directly to the data, thus with no biases induced by feature estimators or classifiers. Since the diversity of changes is huge and extremely hard to derive typical classes, we introduce paradigm based both on an unsupervised and a supervised method. The change detection procedure mainly consists in dividing image dataset in patches, computing a collection of similarities for pairs of tiles formed differently in each case, and usage of this collection in unsupervised and supervised forms to generate a change map. Both the threshold based histogram, unsupervised method, and the k-NN classifier algorithm, supervised method, have a distinct flow to obtain the change map. To use the NCD operator according to our proposed methods, a speckle resistance test is involved. The experimental results for the two methodologies are computed using two TerraSAR-X images over Sendai and surrounding areas, Japan.

The fast-paced adoption of smart devices has changed the Internet of Things (IoT) landscape, leading to the growth of smart environments and inclusion in many aspects of our society. In IoT applications, data collected from sensors and mobile devices are aggregated, processed, and analyzed to extract useful information and develop intelligent services. If the collected data is not trustworthy due to the damage or malicious input of some sensors, the quality of the service will be impacted. For reliable data collection and mining, it is mandatory to define robust security and trust models, suitable for the IoT application context. In this paper, we propose RESFIT, a platform that implements a reputation-based trust mechanism and an advanced application level firewall to cope with the above mentioned issues. Having a gateway-centric architecture, the proposed platform ensures minimal resource consumption at the node layer, and an integrated overview and control of the system state, through the cloud component and smartphone management application.

Visualization of multispectral images through band selection methods determine an information loss that in utmost cases proves to be critical for the adequate understanding of the represented scene. Mapping the visual bands to the R, G and B channels is highly used due to its great resemblance with the natural color one. However, despite the similarity in terms of color code, ambiguities between classes such as water and vegetation or atmospheric phenomena like fog, clouds and smoke that have been penetrated by other bands, remain visible and hinder the process of visualization of the Earth surface. This paper presents a set of 5 different methods to offset the effects caused by ambiguities, fog, light clouds and smoke by transferring relevant information between bands in order to visually reconstitute those parts of the image affected by atmospheric phenomena. The general concept shared by these methods implies a stacked autoencoder encompassing the information from all spectral bands into a latent representation used for visualization. Each proposed method is defined by different combination of input and error function formula. Spectral and polar coordinates features represent the possible options for the input, while error function is given by the mean squared error or angular spectral distances. In addition, angular spectral distance and polar coordinates transformation generate illumination invariance in 3 out of 5 methods. We evaluate the methods through spectral signature graphical comparison and visual comparison related to the RGB representation. We conduct experiments on multiple Sentinel 2 full images.

This paper analyzes a through-the-wall synthetic aperture radar (SAR) imaging system designed for indoor use with imprecise positioning systems. Due to uncertainties in transceiver positions at scanning timestamps and the defocusing effects caused by multi-medium (air-wall-air) wave propagation, an autofocus algorithm is essential to achieve high-quality SAR imaging for further detection purposes. We propose a fast and accurate autofocus procedure integrated with the back-projection algorithm, outperforming conventional SAR autofocus algorithms (e.g., map drift algorithm, phase gradient algorithm). Furthermore, we present extensive simulated and experimental results for two indoor positioning systems: a stereoscopic camera and a radio frequency localization system based on the off-the-shelf Pulson P440 transceiver. The primary contributions of this paper include a novel SAR autofocus algorithm based on a prominent scatterer located in front of or behind the wall, along with a fast, recursive routine for solving the nonlinear system of equations associated with the 3D localization system.