DISCOVERING SIGNIFICANT EVOLUTION PATTERNS FROM SATELLITE IMAGE TIME SERIES

Satellite Image Time Series (SITS) is a data set that includes satellite images across several years with a high acquisition rate. Radiometric normalization is a fundamental and important preprocessing method for remote sensing applications using SITS due to the radiometric distortion caused by noise between images. Normalizing the subject image based on the reference image is a general strategy when using traditional radiometric normalization methods to normalize multi-temporal imagery (usually two or three scenes in different time phases). However, these methods are unsuitable for calibrating SITS because they cannot minimize the radiometric distortion between any pair of images in SITS. The existing relative radiometric normalization methods for SITS are based on linear assumptions, which cannot effectively reduce nonlinear radiometric distortion caused by continuously changing noise in SITS. To overcome this problem and obtain a more accurate SITS, we propose a nonlinear radiometric normalization model (NMAG) for SITS based on Artificial Neural Networks (ANN) and Greedy Algorithm (GA). In this method, GA is used to determine the correction order of SITS and calculate the error between the image to be corrected and normalized images, which avoids the selection of a single reference image. ANN is used to obtain the optimal solution of error function, which minimizes the radiometric distortion between different images in SITS. The SITS composed of 21 Landsat-8 images in Tianjin, China, from October 2017 to January 2019 was selected to test the method. We compared NMAG with other two contrast methods (Contrast Method 1 (CM1) and Contrast Method 2 (CM2)), and found that the average root mean square error (μRMSE) of NMAG (497.22) is significantly smaller than those of CM1 (641.39) and CM2 (543.47), and the accuracy of normalized SITS obtained using NMAG increases by 22.4% and 8.5% compared with CM1 and CM2, respectively. These experimental results confirm the effectiveness of NMAG in reducing radiometric distortion caused by continuously changing noise between images in SITS.

Compared with one single image, satellite image time series (SITS) can capture the dynamic changes in land cover types, thus achieving a more comprehensive and accurate land cover classification map. Due to decades of data acquisition and new high temporal resolution sensors, SITS is becoming more available. Corresponding SITS analysis techniques need to be further developed. Most satellite images are multispectral, namely, multivariate. However, multivariate time series analysis techniques are less mature compared with univariate time series. There seems to be a lack of a robust and accurate similarity measure between multivariate time series for SITS clustering. In this paper, we propose a novel method to transform multivariate SITS into univariate SITS while the useful information is kept as much as possible. And then advanced univariate time series similarity measures can be adopted to achieve SITS clustering. The proposed method is tested on Landsat-TM SITS dataset and shows a better clustering result than ordinary multivariate time series similarity measure. In addition, the overall computing time may be reduced due to dimension reduction.

Similarity analysis of frequent sequential activity pattern mining

In this paper, we present PaTSI, a tool for analyzing evolutions of objects in time series of satellite images. This tool is a plugin integrated in the KNIME Analytics Platform. PaTSI is a workflow composed of several nodes assembled together to form a whole KDD process (data selection, pre-processing, image segmentation, pattern mining and visualization). Input data consists of a time series of satellite images and GIS information on the studied area. This data is transformed in a single attributed directed acyclic graph (a-DAG), where nodes represent objects described by several attributes and edges represent temporal relationships. This graph is then mined to extract frequent evolutions (weighted path patterns) using an efficient graph mining algorithm. At the end of the process, extracted patterns can be filtered using regular expressions and displayed on the original images in order to facilitate the experts' interpretation of the results. In the present demo, the pertinence of PaTSI is illustrated through its application to soil erosion monitoring.

Abstract. Frequent sequential pattern mining in item interval extended sequence database (iSDB) has been one of interesting task in recent years. Unlike classic frequent sequential pattern mining, the pattern mining in iSDB also consider the item interval between successive items; thus, it may extract more meaningful sequential patterns in real life. Most previous frequent sequential pattern mining in iSDB algorithms needs a minimum support threshold (minsup) to perform the mining. However, it’s not easy for users to provide an appropriate threshold in practice. The too high minsup value will lead to missing valuable patterns, while the too low minsup value may generate too many useless patterns. To address this problem, we propose an algorithm: TopKWFP – Top-k weighted frequent sequential pattern mining in item interval extended sequence database. Our algorithm doesn’t need to provide a fixed minsup value, this minsup value will dynamically raise during the mining process

Satellite Image Time Series (SITS) have become more accessible in recent years and SITS analysis has attracted increasing research interest. Given that labeled SITS training samples are time and effort consuming to acquire, clustering or unsupervised analysis methods need to be developed. Similarity measure is critical for clustering, however, currently established methods represented by Dynamic Time Warping (DTW) still exhibit several issues when coping with SITS, such as pathological alignment, sensitivity to spike noise, and limitation on capacity. In this paper, we introduce a new time series similarity measure method named time adaptive optimal transport (TAOT) to the application of SITS clustering. TAOT inherits several promising properties of optimal transport for the comparing of time series. Statistical and visual results on two real SITS datasets with two different settings demonstrate that TAOT can effectively alleviate the issues of DTW and further improve the clustering accuracy. Thus, TAOT can serve as a usable tool to explore the potential of precious SITS data.

Temporal trajectories of land-cover change provide important information on landscape dynamics that are critical to our understanding of complex human–environment adaptive systems. The increasing availability of long time series of satellite images, especially the recent free release of multi-decadal Landsat satellite archive, presents a great opportunity to improve our ability to detect land-cover change over multiple dates and advance land change science. In this article, a spatial-temporal modeling approach is developed for reconstructing land-cover change trajectories from time series of satellite images. The change detection method represents an enhancement to the conventional post-classification comparison. The key innovation lies in the use of Markov random field theory to model spatial-temporal contextual information explicitly in the classification of time series images. When evaluated using a time series of seven Landsat images in a case study of southeast Ohio, the spatial-temporal modeling approach yielded significantly more accurate and consistent trajectories of land-cover change than conventional non-contextual approaches. The results from the case study demonstrate the effectiveness of the change detection method in reconstructing land-cover change trajectories and also highlight the utility of spatial-temporal contextual information in improving the accuracy and consistency of land-cover classifications across space and time.

A spatiotemporal cube model for analyzing satellite image time series: Application to land-cover mapping and change detection

The dense Satellite Images Time Series (SITS) plays an important role in the agriculture semantic segmentation task. However, in real-world scenarios, cloud contamination and temporary sensor outages can lead to significant data missing in SITS, which declines the performance of models trained on ideal scenarios. A common approach is to reconstruct the complete SITS before the model’s prediction, where the reconstruction is independent of the prediction. This approach not only leads to the error accumulation from reconstruction to prediction, but also the detailed rebuilding of complete SITS may be redundant for the prediction. In this paper, we proposed a features reconstruction and prediction joint learning framework. The collaborative optimization of the two tasks aims to encourage the model to efficiently reconstruct complete features beneficial for prediction from incomplete SITS. Specifically, we simulate the data-missing scenarios with masks. The prediction task of masked data is supervised by labels. Meanwhile, by using the model that is well-trained on ideal scenarios as a teacher, we leverage its extracted temporal features from the data before masking as the target of the feature reconstruction task. The gradient flow of two tasks will be shared, which enables mutual supervision between them. Feature reconstruction prevents the model from acquiring incorrect reasoning ability caused by the shortest path problem during prediction, whereas prediction keeps reliability and reduces redundancy of reconstructed information. Furthermore, after training with the proposed framework, the model architecture remains unchanged and still maintains its robustness of complete SITS, which enhances the model's feasibility in practical applications. The experiments were conducted across multiple agricultural semantic segmentation datasets with incomplete SITS, sourced from Sentinel-2 and Planet satellites. We also validate its robustness for the common model architectures, and visualize the intermediate features to explore the mutual influence between the two tasks.

Continuous monitoring of phytopigment concentrations and sea surface temperature in the ocean by space-borne methods makes possible to estimate ecological condition of biocenoses in critical areas. In the papers of the authors (Shevyrnogov A.P., Vysotskaya G.S., Gitelzon J.I. 1996) existence of zones, which are quasi-stationary with similar seasonal dynamics of chlorophyll concentration at surface layer of ocean, was shown. Results were obtained on the base of processing of time series of satellite images SeaWiFS. It was shown that fronts and frontal zones coincide with dividing lines between quasi-stationary areas, especially in areas of large oceanic streams. The usage of the seasonal dynamics gives a possibility to circumvent influence of high-frequency component in investigation of dynamics of spatial distribution of surface streams. In addition, an analyses of unstable ocean productivity phenomena, stood out time series of satellite images, showed existence of areas with different types of instability in the all Global ocean. They are observed as adjacent nonstationary zones of different size, which are associated by different ways with known oceanic phenomena. It is evident that dynamics of a spatial distribution of biological productivity and sea surface temperature can give an additional knowledge of complicated picture of surface oceanic layer hydrology.

1. The study of a desert landscape consisting of C3 shrublands and C4 grasslands demonstrated the feasibility of mapping desertification processes on a regional scale using time-series satellite images, and demonstrated the potential for monitoring desertification processes through time. 2. Fifteen meteorological satellite images of southern New Mexico, spread over the growing season of 1989, were processed to yield images of normalized difference vegetation indices. Computer classification of these images and comparison with extensive ground measurements confirmed the identification of the major classes of shrubland, grassland and mixed shrub and grass areas. 3. Our high-resolution temporal approach is shown to permit the accurate identification of vegetation types even under conditions of sparse vegetation cover. The phenologies of grassland and shrubland were revealed in the time series of satellite images. We used this feature of the data to overcome the tendency of the strong radiation signal from bare soil to mask the biomass signal. 4. Comprehensive comparisons with ground measurements confirmed the accuracy of the biomass identification for local areas within the Jornada Experimental Range, and justified extrapolation of the classification to a larger adjacent area.

Image time series can be interpreted on a higher level. We use the information-bottleneck principle to demonstrate our approach.

Assessment of radiometric correction techniques in analyzing vegetation variability and change using time series of Landsat images

Time series of satellite images are now massively available thanks to the existence of several constellations of recurrent satellites. We propose a method for detecting and measuring the duration of changes on such series. This approach is intended to be generic and independent of the type of satellite used, whether band limited or multispectral. It is based on a global analysis of the sequence. The statistical detection method is applied to a residual sequence computed from backward and forward novelty filters applied to all images in the series. Significant changes are computed with a guarantee on their number of false alarms (NFA). To establish the efficiency of the method, we have created an open database of 28 sequences of 20 images acquired by the Sentinel-2 satellite, in different regions of the world. We obtain satisfactory results, which are consistent with the visual observations.

Bangladesh occupies a major part of the Bengal Delta which has an area in excess of 100,000 m2. The river systems that build up the delta are very dynamic in nature and govern the lives, livelihoods, and safety of the inhabitants of riverine chars and coastal islands/chars. To study the dynamics of these systems, traditional spatial data such as topographic, bathymetric, and hydrographic surveys are sparse in terms of both spatial and temporal coverage. Over the last four decades, the use of remotely sensed satellite images has proven to be effective in studying the dynamic systems. This paper shows how the analysis of satellite images supports the generation of relevant information and knowledge about both static and dynamic systems. It describes the processing and analyses of the satellite images and their corresponding outcomes. A single image can provide static information such as river bankline, width, braiding index, and char and island geometry. However, a time series of satellite images can provide information and understanding of the dynamics of the system such as erosion and accretion of rivers, chars, estuary and coastal islands, stability of chars and islands, widening and narrowing of rivers.KeywordsRemote sensingRiverine charsCoastal islandsBengal Delta