The expected increasing availability of remote sensing satellite hyperspectral (HS) images provides an important and unique data source for Earth observation (EO). HS images are characterized by a detailed spectral sampling (i.e., very high spectral resolution) over a wide spectral wavelength range, which makes it possible to monitor land-cover dynamics at a fine spectral scale. This is due to its capability of detecting subtle spectral variations in multitemporal images associated with land-cover changes that are not detectable in traditional multispectral (MS) images because of their limited spectral resolution (i.e., sufficient for representing only abrupt, strong changes in the spectral signature, as a rule). To fully exploit the available multitemporal HS images and their rich information content in change detection (CD), it is necessary to develop advanced automatic techniques that can address the complexity of the extraction of change information in an HS space. This article provides a comprehensive overview of the CD problem in HS images, as well as a survey on the main CD techniques available for multitemporal HS images. We review both widely used methods and new techniques proposed in the recent literature. The basic concepts, categories, open issues, and challenges related to CD in HS images are discussed and analyzed in detail. Experimental results obtained using state-of-the-art approaches are shown, to illustrate relevant concepts and problems.

Recent works highlighted the significant potential of Lung Ultrasound (LUS) imaging in the management of subjects affected by COVID-19. In general, the development of objective, fast, and accurate automatic methods for LUS data evaluation is still at an early stage. This is particularly true for COVID- 19 diagnostic. In this paper, we propose an automatic and unsupervised method for the detection and localization of the pleural line in LUS data based on the Hidden Markov Model and Viterbi Algorithm. The pleural line localization step is followed by a supervised classification procedure based on the Support Vector Machine (SVM). The classifier evaluates the healthiness level of a patient and, if present, the severity of the pathology, i.e., the score value for each image of a given LUS acquisition. The experiments performed on a variety of LUS data acquired in Italian hospitals with both linear and convex probes highlight the effectiveness of the proposed method. The average overall accuracy in detecting the pleura is 84% and 94% for convex and linear probes, respectively. The accuracy of the SVM classification in correctly evaluating the severity of COVID-19 related pleural line alterations is about 88% and 94% for convex and linear probes, respectively. The results as well as the visualization of the detected pleural line and the predicted score chart, provide a significant support to medical staff for further evaluating the patient condition.

Light Detection and Ranging (LiDAR) data have been widely used to characterize the 3-D structure of the forest. However, their use in a multitemporal framework has been quite limited due to the relevant challenges introduced by the comparison of pairs of point clouds. Because of the irregular sampling of the laser scanner and the complex structure of forest areas, it is not possible to perform a point-to-point comparison between the two data. To overcome these challenges, a novel hierarchical approach to the detection of 3-D changes in forest areas is proposed. The method first detects the large changes (e.g., cut trees) by comparing the Canopy Height Models derived from the two LiDAR data. Then, according to an object-based change detection approach, it identifies the single-tree changes by monitoring both the treetop and the crown volume growth. The proposed approach can compare LiDAR data with significantly different pulse densities, thus allowing the use of many data available in real applications. Experimental results pointed out that the method can accurately detect large changes, exhibiting a low rate of false and missed alarms. Moreover, it can detect changes in terms of single-tree growth, which are consistent with the expected growth rates of the considered areas.

This letter proposes a novel method based on Deep Learning (DL) to forest species classification in airborne Light Detection and Ranging (LiDAR) data. Differently from the state-of- the-art approaches, the proposed method: (1) does not assume any prior knowledge either on the forest to be classified or on the sensor used to acquire the LiDAR data, and (2) can be applied to heterogeneous forest characterized by mixed species. First, the 3D point cloud of each individual tree is decomposed into 8 angular sectors to generate a multi-slices representation of the vertical structure of the tree. This representation models the foliage, the stem and the branches of the tree crown as well as depicts the internal and external crown properties. Then, a Multi-View CNN (MVCNN) DL automatically extracts features used to discriminate the different tree species. This network is pre-trained on the massive ImageNet database, thus guaranteeing fast convergence with a relatively small number of ground reference data. Experiments were carried out on high density airborne LiDAR data collected over a multi-layer multi-age forest characterized by four conifers and three broadleaf species. The proposed method outperformed the state-of-the-art approaches increasing the Overall Accuracy (OA) up to 16% and 18.9% compared to a DL and a shallow tree species classification methods, respectively. When applied to coniferous or broadlaef forests, the proposed method showed an increase of OA 10.1% and 15.9% (for conifers), and 9.5% and 21.6% (for broadleafs) compared to the DL and shallow methods, respectively.

The current remote sensing (RS) open data policy for multispectral (MS) missions such as Sentinel-2 and Landsat-8, together with the availability of free cloud distributed processing platforms such as Google Earth Engine, makes it possible the quick generation of burned area (BA) products even for nonexperts in the field. Indeed, fires and BAs can be detected using burn severity indices, which are usually obtained by simple band algebra operations. However, simple approaches can aid BA estimation only if typical error patterns are known and accounted for, especially when working at large (e.g., continental) scales. This article proposes an automatic BA detection system based on burn severity index thresholding, which integrates dedicated false and missed alarm mitigation strategies to improve the detection accuracy. The system is tested on Sentinel-2 and Landsat-8 data over ten different locations in Europe and spanning year 2018. Three known burn severity indices plus a custom one defined to improve the performance in the considered study area are under study. Results show that burned index thresholding is possible within accuracy bounds slightly larger than the state of the art, which is acceptable by considering the proposed simplified processing framework.