Learning Spectral-Spatial-Temporal Features via a Recurrent Convolutional Neural Network for Change Detection in Multispectral Imagery

Change detection is one of the central problems in earth observation and was extensively investigated over recent decades. In this paper, we propose a novel recurrent convolutional neural network (ReCNN) architecture, which is trained to learn a joint spectral-spatial-temporal feature representation in a unified framework for change detection in multispectral images. To this end, we bring together a convolutional neural network (CNN) and a recurrent neural network (RNN) into one end-to-end network. The former is able to generate rich spectral-spatial feature representations, while the latter effectively analyzes temporal dependency in bi-temporal images. In comparison with previous approaches to change detection, the proposed network architecture possesses three distinctive properties: 1) It is end-to-end trainable, in contrast to most existing methods whose components are separately trained or computed; 2) it naturally harnesses spatial information that has been proven to be beneficial to change detection task; 3) it is capable of adaptively learning the temporal dependency between multitemporal images, unlike most of algorithms that use fairly simple operation like image differencing or stacking. As far as we know, this is the first time that a recurrent convolutional network architecture has been proposed for multitemporal remote sensing image analysis. The proposed network is validated on real multispectral data sets. Both visual and quantitative analysis of experimental results demonstrates competitive performance in the proposed mode

Image fusion techniqes for remote sensing applications

Image fusion refers to the acquisition, processing and synergistic combination of information provided by various sensors or by the same sensor in many measuring contexts. The aim of this survey paper is to describe three typical applications of data fusion in remote sensing. The first study case considers the problem of the Synthetic Aperture Radar (SAR) Interferometry, where a pair of antennas are used to obtain an elevation map of the observed scene; the second one refers to the fusion of multisensor and multitemporal (Landsat Thematic Mapper and SAR) images of the same site acquired at different times, by using neural networks; the third one presents a processor to fuse multifrequency, multipolarization and mutiresolution SAR images, based on wavelet transform and multiscale Kalman filter. Each study case presents also results achieved by the proposed techniques applied to real data

Organic matter modeling at the landscape scale based on multitemporal soil pattern analysis using RapidEye data

This study proposes the development of a landscape-scale multitemporal soil pattern analysis (MSPA) method for organic matter (OM) estimation using RapidEye time series data analysis and GIS spatial data modeling, which is based on the methodology of Blasch et al. The results demonstrate (i) the potential of MSPA to predict OM for single fields and field composites with varying geomorphological, topographical, and pedological backgrounds and (ii) the method conversion of MSPA from the field scale to the multi-field landscape scale. For single fields, as well as for field composites, significant correlations between OM and the soil pattern detecting first standardized principal components were found. Thus, high-quality functional OM soil maps could be produced after excluding temporal effects by applying modified MSPA analysis steps. A regional OM prediction model was developed using four representative calibration test sites. The MSPA-method conversion was realized applying the transformation parameters of the soil-pattern detection algorithm used at the four calibration test sites and the developed regional prediction model to a multi-field, multitemporal, bare soil image mosaic of all agrarian fields of the Demmin study area in Northeast Germany. Results modeled at the landscape scale were validated at an independent test site with a resulting prediction error of 1.4 OM-% for the main OM value range of the Demmin study area

Studies of recognition with multitemporal remote sensor data

Characteristics of multitemporal data and their use in recognition processing were investigated. Principal emphasis was on satellite data collected by the LANDSAT multispectral scanner and on temporal changes throughout a growing season. The effects of spatial misregistration on recognition performance with multitemporal data were examined. A capability to compute probabilities of detection and false alarm was developed and used with simulated distributions for misregistered pixels. Wheat detection was found to be degraded and false alarms increased by misregistration effects. Multitemporal signature characteristics and multitemporal recognition processing were studied to gain insights into problems associated with this approach and possible improvements. Recognition performance with one multitemporal data set displayed marked improvements over results from single-time data

A landsat remote sensing study of vegetation growing on mineralized terrain

Imperial Users onl

Crop identification technology assessment for remote sensing (CITARS). Volume 10: Interpretation of results

The CITARS was an experiment designed to quantitatively evaluate crop identification performance for corn and soybeans in various environments using a well-defined set of automatic data processing (ADP) techniques. Each technique was applied to data acquired to recognize and estimate proportions of corn and soybeans. The CITARS documentation summarizes, interprets, and discusses the crop identification performances obtained using (1) different ADP procedures; (2) a linear versus a quadratic classifier; (3) prior probability information derived from historic data; (4) local versus nonlocal recognition training statistics and the associated use of preprocessing; (5) multitemporal data; (6) classification bias and mixed pixels in proportion estimation; and (7) data with differnt site characteristics, including crop, soil, atmospheric effects, and stages of crop maturity

Spatiotemporal dynamics of stress factors in wheat analysed by multisensoral remote sensing and geostatistics

Plant stresses, in particular fungal diseases, basically show a high variability in space and time with respect to their impact on the host. Recent ‘Precision Agriculture’ techniques allow for a spatially and temporally adjusted pest control that might reduce the amount of cost-intensive and ecologically harmful agrochemicals. Conventional stress detection techniques such as random monitoring do not meet demands of such optimally placed management actions. The prerequisite is a profound knowledge about the controlled phenomena as well as their accurate sensor-based detection. Therefore, the present study focused on spatiotemporal dynamics of stress factors in wheat, Europe’s main crop. Primarily, the spatiotemporal characteristics of the fungal diseases, powdery mildew (Blumeria graminis) and leaf rust (Puccinia recondita), were analysed by remote sensing techniques and geo-statistics on leaf and field scale. Basically, there are two different approaches to sensor-based detection of crop stresses: near-range sensors and airborne-/satellite-borne sensors. In order to assess the potential of both approaches, various experiments in field and laboratory were carried out with the use of multiple sensors operated at different scales. Besides the spatial dimension of crop stresses, all studies focussed on the temporal dimension of these phenomena, since this is the key question for an operational use of these techniques. In addition, a comparison between multispectral and hyperspectral data gave an indication of their suitability for this purpose. The results exhibit very high spatiotemporal dynamics for both fungal diseases. However, powdery mildew and leaf rust showed different characteristics, with leaf rust showing a more systematic temporal progress. The physiological behaviours of the phenomena, which are strongly influenced by various environmental factors, define the optimal disease detection date as well as the temporal resolution required for sensor-based disease detection. Due to the high spatiotemporal dynamics of the investigated diseases, a general recommendation of optimal detection periods can not be given, but critical periods are highlighted for each pathogen. The results indicate that multispectral remote sensing data with high spatial resolution shows a high potential for quantifying crop vigour by using spectral mixture analyses. Simulated endmembers for the identification of stressed wheat areas were utilized, whereby promising results could be achieved. However, due to the low spectral resolution of these data, a discrimination of stress factors or early disease detection is not possible. Hyperspectral data was therefore used to point out the potential of early detection of crop diseases, which is a crucial and restrictive factor for Precision Agriculture applications. In a laboratory experiment, leaf rust infections could be detected by hyperspectral data five days after inoculation. In a field experiment with respect to early stress detection, it could be demonstrated that hyperspectral data outperformed multispectral data. High accuracy for the detection of powdery mildew infections in the field was thereby achieved. Due to the fact that typical spatiotemporal characteristics for each pathogen were found, there is a high potential for decision support systems, considering all variables that affect the disease progress. Besides the further analysis of hyperspectral data for disease detection, the development of a decision support system is the subject of the upcoming last period of the Research Training Group 722

Results from the Crop Identification Technology Assessment for Remote Sensing (CITARS) project

The author has identified the following significant results. It was found that several factors had a significant effect on crop identification performance: (1) crop maturity and site characteristics, (2) which of several different single date automatic data processing procedures was used for local recognition, (3) nonlocal recognition, both with and without preprocessing for the extension of recognition signatures, and (4) use of multidate data. It also was found that classification accuracy for field center pixels was not a reliable indicator of proportion estimation performance for whole areas, that bias was present in proportion estimates, and that training data and procedures strongly influenced crop identification performance

Multitemporal mapping of post-fire land cover using multiplatform PRISMA hyperspectral and Sentinel-UAV multispectral data: Insights from case studies in Portugal and Italy

Wildfires have affected global forests and the Mediterranean area with increasing recurrency and intensity in the last years, with climate change resulting in reduced precipitations and higher temperatures. To assess the impact of wildfires on the environment, burned area mapping has become progressively more relevant. Initially carried out via field sketches, the advent of satellite remote sensing opened new possibilities, reducing the cost uncertainty and safety of the previous techniques. In the present study an experimental methodology was adopted to test the potential of advanced remote sensing techniques such as multispectral Sentinel-2, PRISMA hyperspectral satellite, and UAV (unmanned aerial vehicle) remotely-sensed data for the multitemporal mapping of burned areas by soil–vegetation recovery analysis in two test sites in Portugal and Italy. In case study one, innovative multiplatform data classification was performed with the correlation between Sentinel-2 RBR (relativized burn ratio) fire severity classes and the scene hyperspectral signature, performed with a pixel-by-pixel comparison leading to a converging classification. In the adopted methodology, RBR burned area analysis and vegetation recovery was tested for accordance with biophysical vegetation parameters (LAI, fCover, and fAPAR). In case study two, a UAV-sensed NDVI index was adopted for high-resolution mapping data collection. At a large scale, the Sentinel-2 RBR index proved to be efficient for burned area analysis, from both fire severity and vegetation recovery phenomena perspectives. Despite the elapsed time between the event and the acquisition, PRISMA hyperspectral converging classification based on Sentinel-2 was able to detect and discriminate different spectral signatures corresponding to different fire severity classes. At a slope scale, the UAV platform proved to be an effective tool for mapping and characterizing the burned area, giving clear advantage with respect to filed GPS mapping. Results highlighted that UAV platforms, if equipped with a hyperspectral sensor and used in a synergistic approach with PRISMA, would create a useful tool for satellite acquired data scene classification, allowing for the acquisition of a ground truth