Novel pattern recognition methods for classification and detection in remote sensing and power generation applications

Novel pattern recognition methods for classification and detection in remote sensing and power generation application

Hierarchical Markov random fields for high resolution land cover classification of multisensor and multiresolution image time series

International audienc

Segmentation of remote sensing images using similarity measure based fusion-MRF model

Classifying segments and detecting changes in terrestrial areas are important and time-consuming efforts for remote sensing image analysis tasks, including comparison and retrieval in repositories containing multitemporal remote image samples for the same area in very different quality and details. We propose a multilayer fusion model for adaptive segmentation and change detection of optical remote sensing image series, where trajectory analysis or direct comparison is not applicable. Our method applies unsupervised or partly supervised clustering on a fused-image series by using cross-layer similarity measure, followed by multilayer Markov random field segmentation. The resulted label map is applied for the automatic training of single layers. After the segmentation of each single layer separately, changes are detected between single label maps. The significant benefit of the proposed method has been numerically validated on remotely sensed image series with ground-truth data

Automatic near real-time flood detection in high resolution X-band synthetic aperture radar satellite data using context-based classification on irregular graphs

This thesis is an outcome of the project “Flood and damage assessment using very high resolution SAR data” (SAR-HQ), which is embedded in the interdisciplinary oriented RIMAX (Risk Management of Extreme Flood Events) programme, funded by the Federal Ministry of Education and Research (BMBF). It comprises the results of three scientific papers on automatic near real-time flood detection in high resolution X-band synthetic aperture radar (SAR) satellite data for operational rapid mapping activities in terms of disaster and crisis-management support. Flood situations seem to become more frequent and destructive in many regions of the world. A rising awareness of the availability of satellite based cartographic information has led to an increase in requests to corresponding mapping services to support civil-protection and relief organizations with disaster-related mapping and analysis activities. Due to the rising number of satellite systems with high revisit frequencies, a strengthened pool of SAR data is available during operational flood mapping activities. This offers the possibility to observe the whole extent of even large-scale flood events and their spatio-temporal evolution, but also calls for computationally efficient and automatic flood detection methods, which should drastically reduce the user input required by an active image interpreter. This thesis provides solutions for the near real-time derivation of detailed flood parameters such as flood extent, flood-related backscatter changes as well as flood classification probabilities from the new generation of high resolution X-band SAR satellite imagery in a completely unsupervised way. These data are, in comparison to images from conventional medium-resolution SAR sensors, characterized by an increased intra-class and decreased inter-class variability due to the reduced mixed pixel phenomenon. This problem is addressed by utilizing multi-contextual models on irregular hierarchical graphs, which consider that semantic image information is less represented in single pixels but in homogeneous image objects and their mutual relation. A hybrid Markov random field (MRF) model is developed, which integrates scale-dependent as well as spatio-temporal contextual information into the classification process by combining hierarchical causal Markov image modeling on automatically generated irregular hierarchical graphs with noncausal Markov modeling related to planar MRFs. This model is initialized in an unsupervised manner by an automatic tile-based thresholding approach, which solves the flood detection problem in large-size SAR data with small a priori class probabilities by statistical parameterization of local bi-modal class-conditional density functions in a time efficient manner. Experiments performed on TerraSAR-X StripMap data of Southwest England and ScanSAR data of north-eastern Namibia during large-scale flooding show the effectiveness of the proposed methods in terms of classification accuracy, computational performance, and transferability. It is further demonstrated that hierarchical causal Markov models such as hierarchical maximum a posteriori (HMAP) and hierarchical marginal posterior mode (HMPM) estimation can be effectively used for modeling the inter-spatial context of X-band SAR data in terms of flood and change detection purposes. Although the HMPM estimator is computationally more demanding than the HMAP estimator, it is found to be more suitable in terms of classification accuracy. Further, it offers the possibility to compute marginal posterior entropy-based confidence maps, which are used for the generation of flood possibility maps that express that the uncertainty in labeling of each image element. The supplementary integration of intra-spatial and, optionally, temporal contextual information into the Markov model results in a reduction of classification errors. It is observed that the application of the hybrid multi-contextual Markov model on irregular graphs is able to enhance classification results in comparison to modeling on regular structures of quadtrees, which is the hierarchical representation of images usually used in MRF-based image analysis. X-band SAR systems are generally not suited for detecting flooding under dense vegetation canopies such as forests due to the low capability of the X-band signal to penetrate into media. Within this thesis a method is proposed for the automatic derivation of flood areas beneath shrubs and grasses from TerraSAR-X data. Furthermore, an approach is developed, which combines high resolution topographic information with multi-scale image segmentation to enhance the mapping accuracy in areas consisting of flooded vegetation and anthropogenic objects as well as to remove non-water look-alike areas

Floodplain Vegetation Productivity Response to Wetting and Drying: Testing the Adaptive Cycle Model

Dryland floodplains are characterized by highly variable flooding and drying regimes. The spatial and temporal variability in flooding plays a significant role in the productivity of these ecosystems and is a key influence on the composition and distribution of vegetation in these floodplains. Dryland floodplains have been perceived to be boom and bust systems, in which the boom is characterised by an inundated floodplain and the bust is characterised by a dry floodplain in moisture deficit. The boom stimulates great primary and secondary production where as the bust brings contraction of the ecosystem into refugia and a reduction in primary and secondary production. This relatively simple two state model may not account for the transitions that may occur between flooding (boom) and dry (bust) floodplain states. Understanding the patterns of response at different scales is critical to our ability to manage these complex dryland systems and to be able to make predictions about their future condition over time. This thesis applied an adaptive cycle model in order to understand change in floodplain vegetation productivity through multiple periods of flooding and drying. Adaptive cycles are a key component of resilience thinking. In this adaptive cycle model, vegetation productivity is the ecosystem responder and hydrology, or floodplain flooding and drying, the main driver of change. I derived a series of sequential hypotheses that explored the applicability of an adaptive cycle for the response of vegetation productivity in the Narran floodplain. The Normalized Difference Vegetation Index (NDVI) which measures vegetation greenness was used as a surrogate for vegetation productivity. In this adaptive cycle floodplain inundation was considered to drive vegetation productivity response through a cycle of exploitation, conservation, release and reorganization phases. The adaptive cycle starts as floodwater inundates the floodplain in the wetting phase. The wetting phase corresponds to the exploitation part of adaptive loop, where the area of vegetation productivity and quality will increase because of the availability of water as an exploitable resource. The wet phase is the phase of maximum inundation and corresponds to the conservation phase of the adaptive loop. The conservation phase is a period of increased vegetation productivity and a stability of vegetation productivity. The contraction of floodwater triggers the drying phase and corresponds to the release phase of an adaptive cycle. Further, desiccation of the floodplain occurs with the draining of floodwaters until the floodplain reaches a dry phase, a phase of no surface water availability. The dry phase corresponds to the reorganization phase of an adaptive cycle. The results of this thesis represents an advance on previous studies of dryland floodplains as an approach for characterising and understanding the response of vegetation communities in large floodplains. The findings of this thesis demonstrated there to be marked differences in NDVI class area, number of transitions, directions of transitions, probability of transitions and NDVI class diversity between the dry phase and the combined wetting, wet and drying phases of inundation. Overall an anti-clockwise hysteresis relationship occurred between flooding and vegetation productivity, indicating a cyclic nature of vegetation response to floodplain inundation through dry, wetting, wet and drying phases. These results support the hypothesised adaptive cycle model for the response of vegetation productivity and its appropriateness for understanding the complexity of dryland floodplain vegetation response to wetting and drying. These results were also repeated over four flood events of different size. Although the four events exhibited an adaptive cycle, the duration and the nature of vegetation within each phase of the adaptive cycle differed. Likewise, the four different vegetation communities also exhibited response patterns in relation to flooding and drying that fit the adaptive cycle model. However, differences were evident in the timing of transitions between adaptive cycle phases and the duration spent in those phases in each vegetation community. The woodland community types of the Narran floodplain showed a higher productivity response during the drying or release phase. By comparison the highest productivity response for the grassland and shrubland was observed during the wetting or exploitation phase. Overall, the results showed the four vegetation communities are sensitive at different points in the adaptive cycle. A unique finding of this study result was the location of the exit point from the adaptive cycle, which is the potential point for a state change. The exit point from an adaptive cycle is characterized by a period of enhanced high instability. In the Narran floodplain, the patterns of response in vegetation productivity to flooding and drying indicate this occurred between the conservation and release phases and not between the reorganization and exploitation phases as hypothesised by adaptive cycle theory. Thus, the potential for a change in state in dryland floodplains is highest between the wet (conservation) and drying (release) phases

Land Use And Land Cover Classification And Change Detection Using Naip Imagery From 2009 To 2014: Table Rock Lake Region, Missouri

Land use and land cover (LULC) of Table Rock Lake (TRL) region has changed over the last half century after the construction of Table Rock Dam in 1959. This study uses one meter spatial resolution imagery to classify and detect the change of LULC of three typical waterside TRL regions. The main objectives are to provide an efficient and reliable classification workflow for regional level NAIP aerial imagery and identify the dynamic patterns for study areas. Seven class types are extracted by optimal classification results from year 2009, 2010, 2012 and 2014 of Table Rock Village, Kimberling City and Indian Point. Pixel-based post-classification comparison generated from-to” confusion matrices showing the detailed change patterns. I conclude that object-based random trees achieve the highest overall accuracy and kappa value, compared with the other six classification approaches, and is efficient to make a LULC classification map. Major change patterns are that vegetation, including trees and grass, increased during the last five years period while residential extension and urbanization process is not obvious to indicate high economic development in the TRL region. By adding auxiliary spatial information and object-based post-classification techniques, an improved classification procedure can be utilized for LULC change detection projects at the region level

A CAUSAL HIERARCHICAL MARKOV FRAMEWORK FOR THE CLASSIFICATION OF MULTIRESOLUTION AND MULTISENSOR REMOTE SENSING IMAGES

Abstract. In this paper, a multiscale Markov framework is proposed in order to address the problem of the classification of multiresolution and multisensor remotely sensed data. The proposed framework makes use of a quadtree to model the interactions across different spatial resolutions and a Markov model with respect to a generic total order relation to deal with contextual information at each scale in order to favor applicability to very high resolution imagery. The methodological properties of the proposed hierarchical framework are investigated. Firstly, we prove the causality of the overall proposed model, a particularly advantageous property in terms of computational cost of the inference. Secondly, we prove the expression of the marginal posterior mode criterion for inference on the proposed framework. Within this framework, a specific algorithm is formulated by defining, within each layer of the quadtree, a Markov chain model with respect to a pixel scan that combines both a zig-zag trajectory and a Hilbert space-filling curve. Data collected by distinct sensors at the same spatial resolution are fused through gradient boosted regression trees. The developed algorithm was experimentally validated with two very high resolution datasets including multispectral, panchromatic and radar satellite images. The experimental results confirm the effectiveness of the proposed algorithm as compared to previous techniques based on alternate approaches to multiresolution fusion