GETNET: A General End-to-end Two-dimensional CNN Framework for Hyperspectral Image Change Detection

Change detection (CD) is an important application of remote sensing, which provides timely change information about large-scale Earth surface. With the emergence of hyperspectral imagery, CD technology has been greatly promoted, as hyperspectral data with the highspectral resolution are capable of detecting finer changes than using the traditional multispectral imagery. Nevertheless, the high dimension of hyperspectral data makes it difficult to implement traditional CD algorithms. Besides, endmember abundance information at subpixel level is often not fully utilized. In order to better handle high dimension problem and explore abundance information, this paper presents a General End-to-end Two-dimensional CNN (GETNET) framework for hyperspectral image change detection (HSI-CD). The main contributions of this work are threefold: 1) Mixed-affinity matrix that integrates subpixel representation is introduced to mine more cross-channel gradient features and fuse multi-source information; 2) 2-D CNN is designed to learn the discriminative features effectively from multi-source data at a higher level and enhance the generalization ability of the proposed CD algorithm; 3) A new HSI-CD data set is designed for the objective comparison of different methods. Experimental results on real hyperspectral data sets demonstrate the proposed method outperforms most of the state-of-the-arts

Historical forest biomass dynamics modelled with Landsat spectral trajectories

Acknowledgements National Forest Inventory data are available online, provided by Ministerio de Agricultura, Alimentación y Medio Ambiente (España). Landsat images are available online, provided by the USGS.Peer reviewedPostprin

A landsat remote sensing study of vegetation growing on mineralized terrain

Imperial Users onl

Assessment of multi-temporal, multi-sensor radar and ancillary spatial data for grasslands monitoring in Ireland using machine learning approaches

Accurate inventories of grasslands are important for studies of carbon dynamics, biodiversity conservation and agricultural management. For regions with persistent cloud cover the use of multi-temporal synthetic aperture radar (SAR) data provides an attractive solution for generating up-to-date inventories of grasslands. This is even more appealing considering the data that will be available from upcoming missions such as Sentinel-1 and ALOS-2. In this study, the performance of three machine learning algorithms; Random Forests (RF), Support Vector Machines (SVM) and the relatively underused Extremely Randomised Trees (ERT) is evaluated for discriminating between grassland types over two large heterogeneous areas of Ireland using multi-temporal, multi-sensor radar and ancillary spatial datasets. A detailed accuracy assessment shows the efficacy of the three algorithms to classify different types of grasslands. Overall accuracies ≥ 88.7% (with kappa coefficient of 0.87) were achieved for the single frequency classifications and maximum accuracies of 97.9% (kappa coefficient of 0.98) for the combined frequency classifications. For most datasets, the ERT classifier outperforms SVM and RF

Classification and modelling of urban micro-climates using multisensoral and multitemporal remote sensing data

Remote sensing has widely been used in urban climatology since it has the advantage of a simultaneous synoptic view of the full urban surface. Methods include the analysis of surface temperature patterns, spatial (biophysical) indicators for urban heat island modelling, and flux measurements. Another approach is the automated classification of urban morphologies or structural types. In this study it was tested, whether Local Climate Zones (a new typology of thermally 'rather' homogenous urban morphologies) can be automatically classified from multisensor and multitemporal earth observation data. Therefore, a large number of parameters were derived from different datasets, including multitemporal Landsat data and morphological profiles as well as windowed multiband signatures from an airborne IFSAR-DHM. The results for Hamburg, Germany, show that different datasets have high potential for the differentiation of urban morphologies. Multitemporal thermal data performed very well with up to 96.3 % overall classification accuracy with a neuronal network classifier. The multispectral data reached 95.1 % and the morphological profiles 83.2 %.The multisensor feature sets reached up to 97.4 % with 100 selected features, but also small multisensoral feature sets reached good results. This shows that microclimatic meaningful urban structures can be classified from different remote sensing datasets. Further, the potential of the parameters for spatiotemporal modelling of the mean urban heat island was tested. Therefore, a comprehensive mobile measurement campaign with GPS loggers and temperature sensors on public buses was conducted in order to gain in situ data in high spatial and temporal resolution

Master of Science

thesisVegetation phenology results in seasonal changes in spectral reflectance. Phenology is often underutilized in hyperspectral vegetation mapping due to a lack of repeat imagery of the same region over time. Vegetation classification at the species level could benefit from introducing phenological information to spectral libraries. New missions, such as the proposed Hysperspectral Infrared Imager (HyspIRI) mission, could potentially provide easy access to multi-temporal datasets. The availability of these data will require new approaches to building spectral libraries for species classification. This paper explores the use of Iterative Endmember Selection (IES), an automated method for selecting endmembers from an image-derived spectral library, to create single-date and multitemporal endmember libraries. Multiple Endmember Spectral Mixture Analysis (MESMA) was used to classify vegetation species and land cover, applying single-date and multitemporal libraries to Airborne Visible Infrared Imaging Spectrometer (AVIRIS) data acquired on five dates in the same year. Three applications of endmember libraries were tested for their ability to classify single date AVIRIS images: 1) single-date libraries that matched the image date (same-date libraries), 2) single-date libraries that were not matched to the image date (mismatched-date libraries), and 3) a combined multitemporal library containing spectra from all dates applied to all image dates. Results indicate that multitemporal, seasonally-mixed spectral libraries achieved similar overall classification accuracy compared to single-date libraries, and in some cases, resulted in improved classification accuracy. Several species had increased producer's or user accuracy using a multitemporal library, while others had reduced accuracy compared to same-date classifications. The image dates of selected endmembers from the multitemporal library were examined to determine if this information could improve our understanding of phenological spectral differences for specific species. Results demonstrate that multitemporal endmember libraries may provide a more robust alternative to single-date endmember libraries for mapping vegetation species across time and space. Multitemporal endmember libraries could provide a means for mapping species in data where phenology, climatic variability, or spatial gradients are not known in advance or may not be easily accounted for by endmembers from a single date