Cloud Removal in Sentinel-2 Imagery using a Deep Residual Neural Network and SAR-Optical Data Fusion

Optical remote sensing imagery is at the core of many Earth observation activities. The regular, consistent and global-scale nature of the satellite data is exploited in many applications, such as cropland monitoring, climate change assessment, land-cover and land-use classification, and disaster assessment. However, one main problem severely affects the temporal and spatial availability of surface observations, namely cloud cover. The task of removing clouds from optical images has been subject of studies since decades. The advent of the Big Data era in satellite remote sensing opens new possibilities for tackling the problem using powerful data-driven deep learning methods. In this paper, a deep residual neural network architecture is designed to remove clouds from multispectral Sentinel-2 imagery. SAR-optical data fusion is used to exploit the synergistic properties of the two imaging systems to guide the image reconstruction. Additionally, a novel cloud-adaptive loss is proposed to maximize the retainment of original information. The network is trained and tested on a globally sampled dataset comprising real cloudy and cloud-free images. The proposed setup allows to remove even optically thick clouds by reconstructing an optical representation of the underlying land surface structure

Cybergis-enabled remote sensing data analytics for deep learning of landscape patterns and dynamics

Mapping landscape patterns and dynamics is essential to various scientific domains and many practical applications. The availability of large-scale and high-resolution light detection and ranging (LiDAR) remote sensing data provides tremendous opportunities to unveil complex landscape patterns and better understand landscape dynamics from a 3D perspective. LiDAR data have been applied to diverse remote sensing applications where large-scale landscape mapping is among the most important topics. While researchers have used LiDAR for understanding landscape patterns and dynamics in many fields, to fully reap the benefits and potential of LiDAR is increasingly dependent on advanced cyberGIS and deep learning approaches. In this context, the central goal of this dissertation is to develop a suite of innovative cyberGIS-enabled deep-learning frameworks for combining LiDAR and optical remote sensing data to analyze landscape patterns and dynamics with four interrelated studies. The first study demonstrates a high-accuracy land-cover mapping method by integrating 3D information from LiDAR with multi-temporal remote sensing data using a 3D deep-learning model. The second study combines a point-based classification algorithm and an object-oriented change detection strategy for urban building change detection using deep learning. The third study develops a deep learning model for accurate hydrological streamline detection using LiDAR, which has paved a new way of harnessing LiDAR data to map landscape patterns and dynamics at unprecedented computational and spatiotemporal scales. The fourth study resolves computational challenges in handling remote sensing big data and deep learning of landscape feature extraction and classification through a cutting-edge cyberGIS approach

Cloud and Cloud Shadow Segmentation for Remote Sensing Imagery via Filtered Jaccard Loss Function and Parametric Augmentation

Cloud and cloud shadow segmentation are fundamental processes in optical remote sensing image analysis. Current methods for cloud/shadow identification in geospatial imagery are not as accurate as they should, especially in the presence of snow and haze. This paper presents a deep learning-based framework for the detection of cloud/shadow in Landsat 8 images. Our method benefits from a convolutional neural network, Cloud-Net+ (a modification of our previously proposed Cloud-Net) that is trained with a novel loss function (Filtered Jaccard Loss). The proposed loss function is more sensitive to the absence of foreground objects in an image and penalizes/rewards the predicted mask more accurately than other common loss functions. In addition, a sunlight direction-aware data augmentation technique is developed for the task of cloud shadow detection to extend the generalization ability of the proposed model by expanding existing training sets. The combination of Cloud-Net+, Filtered Jaccard Loss function, and the proposed augmentation algorithm delivers superior results on four public cloud/shadow detection datasets. Our experiments on Pascal VOC dataset exemplifies the applicability and quality of our proposed network and loss function in other computer vision applications

Viljelykasvien tunnistaminen Sentinel-2 -satelliittikuvien avulla Suomessa

European Union member countries are obligated to control the validity of Common Agricultural Policy subsidy applications. Each member country performs manual inspection for at least 5% of these subsidy applications. This is both expensive and a considerable administrative burden. According to European Union, the crop type identifcation process in Common Agricultural Policy could be carried out using remote sensing or orthophoto imagery for an alternative to physical inspections by competent authorities. Automated crop type identifcation would reduce the costs signifcantly. This master’s thesis addressed the crop identifcation with optical Sentinel-2 satellite imagery in Finland. The aim was to investigate whether it was possible to reliably identify the crop growing in land parcels by using machine learning classifcation methods. This thesis presented an automated approach of identifying crops. Multiple different machine learning classifcation algorithms were trained and tested to find out the most suitable processing method, time period and classifcation algorithm by utilizing the land parcels obtained from the Finnish Agency for Rural Affairs. The developed processing method and most of the tested classifcation algorithms were able to perform relatively well in crop identifcation in cloudy growth period 2017 of Finland. Therefore, the developed method could be applied to different use cases and cloudy weather conditions. The further development and training of the classifcation algorithms could make it possible to utilize this approach in Finland as well as in other EU countries for the Common Agricultural Policy control and possibly in numerous other tasks.Euroopan Unionin jäsenmaiden on noudatettava yhteisen maatalouspolitiikan tarjoamien maataloustukihakemusten valvomista. Jokainen jäsenmaa suorittaa manuaalisen valvonnan vähintään 5% tukihakemuksista. Tämä on sekä kallista, että huomattava hallinnollinen taakka. Euroopan Unionin mukaan viljelykasvin tunnistamisprosessin voisi suorittaa kaukokartoitus- tai ortokuvien avulla paikan päällä tehtävien tarkastuksien sijaan. Automaattinen viljelykasvin tunnistaminen vähentäisi valvonnan kustannuksia huomattavasti. Tämä diplomityö käsitteli viljelykasvien tunnistamista optisten Sentinel-2 satelliittikuvien avulla Suomessa. Tarkoitus oli tutkia, pystyttäisiinkö koneoppimista hyödyntävien luokittelualgortimien avulla tunnistamaan pelloilla kasvavia maatalouskasveja. Tämä diplomityö esitteli automaattisen lähestymistavan viljelykasvin tunnistamiselle. Useaa erilaisia luokittelualgrotimia opetettiin ja testattiin kaikkein sopivimman prosessointimenetelmän, ajankohdan ja luokittelualgoritmin löytämiseksi Suomen oloihin Maaseutuviraston tarjoamien peltolohkojen avulla. Kehitetty prosessointimenetelmä ja suurin osa testatuista luokittelualgoritmeistä suoriutuivat suhteellisen hyvin viljelykasvin tunnistamisesta Suomen vuoden 2017 pilvisellä kasvukaudella. Tämän vuoksi on mahdollista, että kehitetty prosessointimenetelmää voisi hyödyntää myös erilaisissa ilmastoissa ja eri käyttötapauksissa. Jatkokehityksen ja lisäopetuksen avulla luokittelumenetelmät voisivat mahdollistaa tämän lähestymistavan hyödyntämistä yleisen maatalouspolitiikan maataloustukihakemusten valvontaan Suomessa ja myös muissa EU-maissa muiden käyttötapausten lisäksi

Improving Flood Detection and Monitoring through Remote Sensing

As climate-change- and human-induced floods inflict increasing costs upon the planet, both in terms of lives and environmental damage, flood monitoring tools derived from remote sensing platforms have undergone improvements in their performance and capabilities in terms of spectral, spatial and temporal extents and resolutions. Such improvements raise new challenges connected to data analysis and interpretation, in terms of, e.g., effectively discerning the presence of floodwaters in different land-cover types and environmental conditions or refining the accuracy of detection algorithms. In this sense, high expectations are placed on new methods that integrate information obtained from multiple techniques, platforms, sensors, bands and acquisition times. Moreover, the assessment of such techniques strongly benefits from collaboration with hydrological and/or hydraulic modeling of the evolution of flood events. The aim of this Special Issue is to provide an overview of recent advancements in the state of the art of flood monitoring methods and techniques derived from remotely sensed data

Utilizing the Landsat spectral-temporal domain for improved mapping and monitoring of ecosystem state and dynamics

Just as the carbon dioxide observations that form the Keeling curve revolutionized the study of the global carbon cycle, free and open access to all available Landsat imagery is fundamentally changing how the Landsat record is being used to study ecosystems and ecological dynamics. This dissertation advances the use of Landsat time series for visualization, classification, and detection of changes in terrestrial ecological processes. More specifically, it includes new examples of how complex ecological patterns manifest in time series of Landsat observations, as well as novel approaches for detecting and quantifying these patterns. Exploration of the complexity of spectral-temporal patterns in the Landsat record reveals both seasonal variability and longer-term trajectories difficult to characterize using conventional bi-temporal or even annual observations. These examples provide empirical evidence of hypothetical ecosystem response functions proposed by Kennedy et al. (2014). Quantifying observed seasonal and phenological differences in the spectral reflectance of Massachusetts’ forest communities by combining existing harmonic curve fitting and phenology detection algorithms produces stable feature sets that consistently out-performed more traditional approaches for detailed forest type classification. This study addresses the current lack of species-level forest data at Landsat resolutions, demonstrating the advantages of spectral-temporal features as classification inputs. Development of a targeted change detection method using transformations of time series data improves spatial and temporal information on the occurrence of flood events in landscapes actively modified by recovering North American beaver (Castor canadensis) populations. These results indicate the utility of the Landsat record for the study of species-habitat relationships, even in complex wetland environments. Overall, this dissertation confirms the value of the Landsat archive as a continuous record of terrestrial ecosystem state and dynamics. Given the global coverage of remote sensing datasets, the time series visualization and analysis approaches presented here can be extended to other areas. These approaches will also be improved by more frequent collection of moderate resolution imagery, as planned by the Landsat and Sentinel-2 programs. In the modern era of global environmental change, use of the Landsat spectral-temporal domain presents new and exciting opportunities for the long-term large-scale study of ecosystem extent, composition, condition, and change

Operationalization of Remote Sensing Solutions for Sustainable Forest Management

The great potential of remote sensing technologies for operational use in sustainable forest management is addressed in this book, which is the reprint of papers published in the Remote Sensing Special Issue “Operationalization of Remote Sensing Solutions for Sustainable Forest Management”. The studies come from three continents and cover multiple remote sensing systems (including terrestrial mobile laser scanning, unmanned aerial vehicles, airborne laser scanning, and satellite data acquisition) and a diversity of data processing algorithms, with a focus on machine learning approaches. The focus of the studies ranges from identification and characterization of individual trees to deriving national- or even continental-level forest attributes and maps. There are studies carefully describing exercises on the case study level, and there are also studies introducing new methodologies for transdisciplinary remote sensing applications. Even though most of the authors look forward to continuing their research, nearly all studies introduced are ready for operational use or have already been implemented in practical forestry