Cloud cover determination in polar regions from satellite imagery

A definition is undertaken of the spectral and spatial characteristics of clouds and surface conditions in the polar regions, and to the creation of calibrated, geometrically correct data sets suitable for quantitative analysis. Ways are explored in which this information can be applied to cloud classifications as new methods or as extensions to existing classification schemes. A methodology is developed that uses automated techniques to merge Advanced Very High Resolution Radiometer (AVHRR) and Scanning Multichannel Microwave Radiometer (SMMR) data, and to apply first-order calibration and zenith angle corrections to the AVHRR imagery. Cloud cover and surface types are manually interpreted, and manual methods are used to define relatively pure training areas to describe the textural and multispectral characteristics of clouds over several surface conditions. The effects of viewing angle and bidirectional reflectance differences are studied for several classes, and the effectiveness of some key components of existing classification schemes is tested

Assessing the Dynamics of Ecological Provinces in the European Seas

The concept of oceanographic provinces has existed for almost a century, providing a useful framework for understanding the mechanisms controlling biological, physical and chemical processes in the ocean and their interactions. This work is an attempt to identify and map marine provinces using satellite observations related to biological processes such as phytoplankton primary production. The approach is based on fuzzy logic as a means of classifying the European Seas into objectively defined areas. The analysis has identified nine domains based on three important variables, surface chlorophyll concentration, sea surface temperature, and available radiation for photosynthesis. These domains were subsequently mapped over the European geographical window using satellite ocean colour and temperature data. The method displays correctly most important productive and unproductive zones, as well as captures the dynamic nature of the marine systems. This study has been conducted in the frame of the institutional project ECOMAR (Monitoring and Assessment of Marine Ecosystems, Action # 2121) within the Inland and Marine Unit of the Institute for Environment & Sustainabilility.JRC.H.5-Rural, water and ecosystem resource

Unsupervised classification of multilook polarimetric SAR data using spatially variant wishart mixture model with double constraints

This paper addresses the unsupervised classification problems for multilook Polarimetric synthetic aperture radar (PolSAR) images by proposing a patch-level spatially variant Wishart mixture model (SVWMM) with double constraints. We construct this model by jointly modeling the pixels in a patch (rather than an individual pixel) so as to effectively capture the local correlation in the PolSAR images. More importantly, a responsibility parameter is introduced to the proposed model, providing not only the possibility to represent the importance of different pixels within a patch but also the additional flexibility for incorporating the spatial information. As such, double constraints are further imposed by simultaneously utilizing the similarities of the neighboring pixels, respectively, defined on two different parameter spaces (i.e., the hyperparameter in the posterior distribution of mixing coefficients and the responsibility parameter). Furthermore, the variational inference algorithm is developed to achieve effective learning of the proposed SVWMM with the closed-form updates, facilitating the automatic determination of the cluster number. Experimental results on several PolSAR data sets from both airborne and spaceborne sensors demonstrate that the proposed method is effective and it enables better performances on unsupervised classification than the conventional methods

Multi-mode partitioning for text clustering to reduce dimensionality and noises

Co-clustering in text mining has been proposed to partition words and documents simultaneously. Although the main advantage of this approach may improve interpretation of clusters on the data, there are still few proposals on these methods; while one-way partition is even now widely utilized for information retrieval. In contrast to structured information, textual data suffer of high dimensionality and sparse matrices, so it is strictly necessary to pre-process texts for applying clustering techniques. In this paper, we propose a new procedure to reduce high dimensionality of corpora and to remove the noises from the unstructured data. We test two different processes to treat data applying two co-clustering algorithms; based on the results we present the procedure that provides the best interpretation of the data

Two-Phase Object-Based Deep Learning for Multi-Temporal SAR Image Change Detection

Change detection is one of the fundamental applications of synthetic aperture radar (SAR) images. However, speckle noise presented in SAR images has a negative effect on change detection, leading to frequent false alarms in the mapping products. In this research, a novel two-phase object-based deep learning approach is proposed for multi-temporal SAR image change detection. Compared with traditional methods, the proposed approach brings two main innovations. One is to classify all pixels into three categories rather than two categories: unchanged pixels, changed pixels caused by strong speckle (false changes), and changed pixels formed by real terrain variation (real changes). The other is to group neighbouring pixels into superpixel objects such as to exploit local spatial context. Two phases are designed in the methodology: (1) Generate objects based on the simple linear iterative clustering (SLIC) algorithm, and discriminate these objects into changed and unchanged classes using fuzzy c-means (FCM) clustering and a deep PCANet. The prediction of this Phase is the set of changed and unchanged superpixels. (2) Deep learning on the pixel sets over the changed superpixels only, obtained in the first phase, to discriminate real changes from false changes. SLIC is employed again to achieve new superpixels in the second phase. Low rank and sparse decomposition are applied to these new superpixels to suppress speckle noise significantly. A further clustering step is applied to these new superpixels via FCM. A new PCANet is then trained to classify two kinds of changed superpixels to achieve the final change maps. Numerical experiments demonstrate that, compared with benchmark methods, the proposed approach can distinguish real changes from false changes effectively with significantly reduced false alarm rates, and achieve up to 99.71% change detection accuracy using multi-temporal SAR imagery

Automated extraction of water bodies from NIR and RGB aerial imagery in northern Alaska using supervised and unsupervised machine learning techniques

Thawing and freezing of permafrost ground are affected by various reasons: air temperature, vegetation, snow accumulation, subsurface physical properties, and moisture. Due to the rising of air temperature, the permafrost temperature and the thermokarst activity increase. Thermokarst instability causes an imbalance for the hydrology system, topography, soils, sediment and nutrient cycle to lakes and streams. Hence the lakes and ponds are ubiquitous in permafrost region. The plants and animals fulfil their nutrient needs from water in the environment. Other animals acquire their needs from the plants and animals that they consume. Therefore the influence of degradation of lakes and ponds strongly affect biogeochemical cycles. This research aims to implement an automated workflow to map the water bodies caused by permafrost thawing. The scientific challenge is to test the machine learning techniques adaptability to assist the observation and mapping of the water bodies using aerial imagery. The study area is mainly located in northern Alaska and consists of five different locations: Ikpikpuk, Teschekpuk Central, Teshekpuk East, Tesheckpuk West, Meade East, and Meade West. To estimate the degradation of the high centred polygons distribution and potential degradation of ice wedges, I mapped the polygonal terrain and ice-wedge melt ponds using areal photogrammetry data of NIR and RGB bands captured by Thaw Trend Air 2019 flight campaign. The techniques used are unsupervised K-mean classification, supervised segment mean shift, and supervised random forest classification to model the water polygons from airborne photogrammetry. There are two phases to perform the machine learning classification; the first step is to test the accuracy of each technique and get to a conclusion about the most adapted method. The second is to prepare the Orthomosaic data, run the chosen workflow, and visualize the final results. The morphology filter with opening option application and clean boundary filters are practical before classification as they sharpen the image features. The conclusion is to use the Random forest classification as it was helpful in all NIR Orthomosaics; however, the RGB images required downsampling to provide adequate accuracy

Performance of Unsupervised Change Detection Method Based on PSO and K-means Clustering for SAR Images

This paper presents unsupervised change detection method to produce more accurate change map from imbalanced SAR images for the same land cover. This method is based on PSO algorithm for image segmentation to layers which classify by Gabor Wavelet filter and then K-means clustering to generate new change map. Tests are confirming the effectiveness and efficiency by comparison obtained results with the results of the other methods. Integration of PSO with Gabor filter and k-means will providing more and more accuracy to detect a least changing in objects and terrain of SAR image, as well as reduce the processing time