Soil moisture is a key environmental variable, important to, e.g., farmers, meteorologists, and disaster management units. Here, we present a method to retrieve surface soil moisture (SSM) from the Sentinel-1 (S-1) sa...
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Soil moisture is a key environmental variable, important to, e.g., farmers, meteorologists, and disaster management units. Here, we present a method to retrieve surface soil moisture (SSM) from the Sentinel-1 (S-1) satellites, which carry C-hand Synthetic Aperture Radar (CSAR) sensors that provide the richest freely available SAR data source so far, unprecedented in accuracy and coverage. Our SSM retrieval method, adapting well-established change detection algorithms, builds the first globally deployable soil moisture observation data set with 1-km resolution. This paper provides an algorithm formulation to be operated in data cube architectures and high-performance computing environments. It includes the novel dynamic Gaussian upscaling method for spatial upscaling of SAR imagery, harnessing its field-scale information and successfully mitigating effects from the SAR's high signal complexity. Also, a new regression-based approach for estimating the radar slope is defined, coping with Sentinel-1's inhomogeneity in spatial coverage. We employ the S-1 SSM algorithm on a 3-year S-1 data cube over Italy, obtaining a consistent set of model parameters and product masks, unperturbed by coverage discontinuities. An evaluation of therefrom generated S-1 SSM data, involving a 1-km soil water balance model over Umbria, yields high agreement over plains and agricultural areas, with low agreement over forests and strong topography. While positive biases during the growing season are detected, the excellent capability to capture small-scale soil moisture changes as from rainfall or irrigation is evident. The S-1 SSM is currently in preparation toward operational product dissemination in the Copernicus Global Land Service.
This article presents the development of an active thermography algorithm capable of detecting defects in materials, based on the techniques of Thermographic Signal Reconstruction (TSR), Thermal Contrast (TC) and the ...
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This article presents the development of an active thermography algorithm capable of detecting defects in materials, based on the techniques of Thermographic Signal Reconstruction (TSR), Thermal Contrast (TC) and the physical principles of heat transfer. The results obtained from this algorithm are compared to the TSR technique and the raw thermogram obtained by stepped thermography inspection. Experimentally, a short thermal pulse is used and the surface temperature of the sample is monitored over time with an infrared camera. Due to the volume of data, the first step is data compression. Newton's law of cooling was used to store the normalized temperature data pixel-by-pixel over time and a compression ratio of 99% was obtained. The main contributions of the developed algorithm are: only four parameters for data compression and the concept of change in the direction of the heat flow to delimit the edges of the defects, where the borders are identified with a remarkable accuracy. Some well known image processing technique are also integrated to improve the thermal analysis: edge detection/interface between the sample and the image background;consolidation in a single image by aggregating the indicators referring to the concept of cooling/heating time constant, maximum thermal amplitude and contrast.
In order to reduce the influence of noise and obtain better changedetection effect, this paper proposes a method for SAR image changedetection based on mean shift pre-classification and fuzzy C-means. First, the ori...
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In order to reduce the influence of noise and obtain better changedetection effect, this paper proposes a method for SAR image changedetection based on mean shift pre-classification and fuzzy C-means. First, the original image is pre-classified based on mean shift clustering. As a clustering method with non-parametric density estimation, mean shift can effectively maintain the edge information of the object, and can smooth the pixel intensity of the same type of object to reduce the influence of noise on changedetection. Then, the difference map is generated by the log-ratio operator and classified into changed area, uncertain area, and unchanged area. After the adjustment, the pre-classification is performed by mean shift and the difference map is generated. Finally, the improved FCM algorithm is used to classify the difference map to generate changedetection result map. The effectiveness of the proposed method is verified by experiments with different contrast algorithms on real SAR image datasets.
changedetection in Remote sensing image is, in essence, to detect the changes of ground features with regard to time from remote sensing perspective. It is usually realized by analyzing and processing multi-temporal ...
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changedetection in Remote sensing image is, in essence, to detect the changes of ground features with regard to time from remote sensing perspective. It is usually realized by analyzing and processing multi-temporal high resolution images. changedetection based on fully connected conditional random field not only improves the detection accuracy of remote sensing image, but also achieves better robustness. However, with the growth of high-resolution data volumes, this algorithm consumes a huge amount of time and computational resources, and therefore needs to be improved accordingly. Spark is an open-source distributed general- purpose cluster-computing framework. It has powerful memory computing and efficient task scheduling capabilities for complex iterative calculations. Based on Spark, this paper proposes a distributed and parallel method of changedetection in remote sensing image based on Fully Connected Conditional Random Field that analyzes the data input form, and proposes a multi-temporal image reading strategy on cloud platforms. This method decomposes the algorithm flow, and performs distributed parallel processing on each stage and makes full use of the processing advantages of data locality to implement a reasonable intermediate data storage. Experimental results demonstrate that this parallel method achieves a promising speedup with high scalability, while guaranteeing remarkable detection accuracy.
The main objective of this study is to introduce and evaluate a SAR-based flood mapping algorithm enabling the automatic generation of a large-scale flood record from the ENVISAT ASAR data archive. The flood mapping a...
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The main objective of this study is to introduce and evaluate a SAR-based flood mapping algorithm enabling the automatic generation of a large-scale flood record from the ENVISAT ASAR data archive. The flood mapping algorithm is based on a changedetection approach and requires an automatic selection of optimal reference images. The flood mapping algorithm is applied to selected pairs of images to sequentially generate a record of flood extent maps. False alarms caused by water-like areas are reduced using auxiliary data sources such as the Height Above Nearest Drainage (HAND) index derived from topography data. The proposed method is applied to several ENVISAT WS ASAR datasets acquired over the UK and results are validated with a flood extent map derived from aerial photography. Results presented in this paper demonstrate the effectiveness of the methodology.
Hyperspectral anomaly changedetection aims at finding rare and anomalous changes in multi-temporal hyperspectral images. There are existing many works about anomaly change detection algorithms, whereas they are all p...
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Hyperspectral anomaly changedetection aims at finding rare and anomalous changes in multi-temporal hyperspectral images. There are existing many works about anomaly change detection algorithms, whereas they are all proposed and evaluated on their own datasets. With the publication of “Viareggio 2013 Trial”, it is necessary to compare the state-of-the-art methods on this dataset with fully ground-truth references. In this paper, we compare 8 anomaly changedetection methods on the two multi-temporal pairs of “Viareggio 2013 Trial”. The experimental results indicate that slow feature analysis with LCRA obtains the best performance.
The development of satellites with the strong temporal repetitiveness and development of remote sensing techniques resulted in the advancement of changedetection techniques from geospatial imagery. The natural events...
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ISBN:
(数字)9781728140643
ISBN:
(纸本)9781728140650
The development of satellites with the strong temporal repetitiveness and development of remote sensing techniques resulted in the advancement of changedetection techniques from geospatial imagery. The natural events cause many modifications in the control process of the ecosystems. There is a necessity of using a method capable to map, categorize and monitor areas affected by natural events along time. In this article, a novel methodology of changedetection is proposed in order to improve the changedetection from multi-date satellite image using the source separation. The results obtained by our methodology are efficient and effective.
changedetection for remote sensing image is of great significance to a diverse range of applications. From the point of object-based method, this paper provides a changedetection algorithm based on co-saliency strat...
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changedetection for remote sensing image is of great significance to a diverse range of applications. From the point of object-based method, this paper provides a changedetection algorithm based on co-saliency strategy for multitemporal high resolution images. Firstly, the final difference image fused by difference feature and log difference feature, is generated, and feature image including spatial and contextual information is obtained by Gabor wavelet transform. Secondly, co-saliency strategy is performed via cluster-based method, combining the final difference image with feature difference image at each temporal data, and highlighting the common regions as the changed directly. Finally, actual change map is extracted by fuzzy local information C-means clustering algorithm (FLICM) and decision-voted method. The experiments show the method proposed in this paper has a superior performance in changedetection for high resolution remote sensing images.
Multi-temporal remote sensing image changedetection is one of the important contents of remote sensing image processing, and has important applications in many fields. Existing multi-temporal changedetection mainly ...
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ISBN:
(纸本)9781728152950
Multi-temporal remote sensing image changedetection is one of the important contents of remote sensing image processing, and has important applications in many fields. Existing multi-temporal changedetection mainly deals with bi-temporal images and extracts change information by ratio or difference method. This processing cannot effectively mine the change information between multiple temporal images or time series of remote sensing images. In this paper, a two-layer slow feature analysis network (SFANet) is proposed to realize effective changedetection with multiple temporal remote sensing images. The proposed method extends the existing slow feature analysis method to a two-layer network structure and forms a slow feature analysis network. On the basis of multi-temporal feature extraction in slow feature analysis network, changedetection is realized by threshold method. In this paper, multi-temporal high-resolution remote sensing images are used for experiments. The experimental results demonstrate that the proposed SFANet for changedetection is effective and better than several commonly used methods.
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