Remote sensing images with high-spatial and high-temporal (HSHT) resolution are difficult to be consistently achieved with a single polar orbit satellite because of the trade-off between spatial and temporal resolution. However, blending algorithms have been developed to synthesize HSHT images from low-spatial but high-temporal resolution images and high-spatial but low-temporal resolution images. One example is a widely used weight-function-based method known as the enhanced spatial and temporal adaptive reflectance fusion model (ESTARFM). Similar-pixels play a key role in the prediction accuracy of this model, and their identification is influenced by the method used, the number of classes, and the moving window size. However, influence of these parameters on the final prediction accuracy has not been thoroughly examined. Therefore, this study assesses the accuracy of ESTARFM when similar-pixels are identified by two separate methods using different numbers of classes and moving window sizes. The results indicated that the class-image-based method outperformed the threshold-based method in most cases and the increase of moving window size generally improved the accuracy for both methods. However, the improvement was minor and accuracy degraded in some cases if the moving window was too large. In addition, the ESTARFM program was optimized in this study, and its computational efficiency was improved compared with previous studies.
Anomaly detection of hyperspectral image is active topic in the field of remote sensing image processing. Reed-X
Detector (RXD) algorithm developed by Reed and Yu is a Constant False Alarm Rate (CFAR) anomaly detection
method founded on multivariate statistical analysis theory, as the same form with Mahalanobis distance. RX detector
could enable researchers to exploit targets that people particularly want from their surroundings according to the spectral
distinct. So RXD is practicable in real scenes, and then becomes a focus in the field of target detection.
RX detector has two common forms, Global-RX and Local-RX. They have different samples to estimate mean vector
and covariance matrix. PCA is a common preprocessing step for dimension reduction. Interestingly, because it can also
remove noises, performance could be improved by using principle components instead of all data. In addition, people
often assume that RX result values submit the chi-square distribution, which often leads to an unacceptable high false
alarm rate in setting χ2α,p as threshold. So, how to get threshold value has been a difficult problem. This paper proposes a
method based on multivariate statistical probability theory which can segment targets from image automatically. Instead
of a constant threshold value, this segmentation target approach use an initial threshold calculated by RX result value
histogram to separate backgrounds and targets samples, then calculate every pixel's posterior probabilities of
background or target by assuming they all submit multi-dimensional normal distribution. Generally, the higher
probability is considerable. The proposed method has been tested using AVIRIS data and the experimental results reveal
that segmentation target approach has higher detection probability and lower false alarm rate compared with the
traditional manual thresholding way.
Target detection is an important research content in hyperspectral remote sensing technology, which is widely
used in securities and defenses. Nowadays, many target detection algorithm have been proposed. One of the key
evaluation indicators of these algorithms performance is false-alarm rate. The feature-level fusion of different
target detection results is a simple and effective method to reduce false-alarm rate. But the different value ranges
of different algorithms bring difficulties for data fusion. This paper proposed a feature-level fusion method based
on RXD detector, which is to integrate multiple target detection results into a multi-bands image, and fuse
detection results using principal theory of abnormal detection. Experiments revealed that, this method is not
restricted by the quantity of target detection algorithms and not influenced by different value ranges of different
algorithms, which can reduce false-alarm rate effectively.
The simulation of remote sensing images is a useful tool for a variety of tasks, such as the definition of future Earth
Observation systems, the optimization and evaluation of instrument specifications, especially for a new type sensor, and
the development and validation of data processing algorithms. A scene simulator for optical hyperspectral data from
'HJ1A-HSI' is described in this paper. 'HJ1A-HSI' was carried on the Chinese small satellite HJ-1A, which was
successfully launched on September 6th, 2008. Different from common hyperspectral sensor, 'HJ1A-HSI' belongs to the
spatial imaging Fourier Transform spectrometer (IFTS). In contrast to the high-speed development of spatial IFTS, the
corresponding image simulator is still at the starting stage and the simulation data is very ideal in most cases. To
simulate more actual data, a simulation system is proposed in this paper, based on the analysis of spatial IFTS principle.
This system puts emphasis on simulating the effects of typical artifacts, and consists of four parts: the calculation of
input parameter, the radiance computation for one beam before interfered, the simulation of effects of typical artifacts
and the interferogram acquisition. The methodology applied to the complete scene simulation and some sample results
are presented and analyzed in this paper.
The secondary disasters induced by the Wenchuan earthquake of May 12, 2008, such as landslides, collapsing rocks, debris flows, floods, etc., have changed the local natural landscape tremendously and caused heavy soil erosion in the earthquake-hit areas. Using thematic mapper images taken before the earthquake and airborne images taken after the earthquake, we extracted information about the destroyed landscape by utilizing remote sensing and geographical information system techniques. Then, taking into account multi-year precipitation, vegetation cover, soil type, land use, and elevation data, we evaluated the soil erosion area and intensity using the revised universal soil loss equation. Results indicate that the soil erosion in earthquake-hit areas was exacerbated, with the severe erosion area increasing by 279.2 km2, or 1.9% of the total statistical area. Large amounts of soil and debris blocked streams and formed many barrier lakes over an area of more than 3.9 km2. It was evident from the spatial distribution of soil erosion areas that the intensity of soil erosion accelerated in the stream valley areas, especially in the valleys of the Min River and the Jian River.
Collapsing houses are one of the indicators to assess earthquake damage intensity. We monitored and analyzed the house collapse ratio and its spatial distribution in the Wenchuan Earthquake of May 12, 2008 by interpreting the aerial images. The results show that the houses were widely damaged, especially in Wenchuan County, Mianzhu City, Shifang City, and Pengzhou City in which Wenchuan County experienced the severest damage. We analyzed the spatial variation of the house collapse ratio and its relationship with earthquake intensity, geological structure, and stratum lithology. The results demonstrate that the house collapse ratio and the earthquake intensity have a positive relationship, which is controlled by the geological structure, stratum lithology, and building structure. Analysis of the collapsed houses over an extensive earthquake-damaged region using aerial images provides not only an effective assessment for the damages and losses, but also the foundation data for the analysis of earthquake intensity.
The Wenchuan earthquake on May 12, 2008, triggered many secondary disasters, among which the barrier lakes formed by landslides were extremely serious. We monitored the number and spatial distribution of the barrier lakes in the earthquake-hit area from ADS40 airborne images, which covered areas of about 23,700 km2. The results showed that there were 51 barrier lakes in the monitored area; among these, 10 were large-scale lakes and 14 were middle-sized lakes. The barrier lakes were distributed along the Longmen Mountain fault from the northeast to southwest direction. We used the dimensionless blockage index (DBI) to assess the potential risk of the barrier lakes. A small DBI value indicated a stable barrier lake, but the lake might have a higher risk with potential accumulative secondary disasters. Our study emphasized the monitoring and analysis of the high-risk Tangjiashan Barrier Lake from the multitemporal ADS40 airborne images acquired on May 16, 19, 23, and 27. We found that the water level at this barrier lake reached 66 m within 15 days after the barrier lake was formed, and the reservoir storage capacity reached 1.2 × 108 m3 with an increase of 8 × 106 m3 of water per day. Therefore, it faced a very real and urgent risk of dam break and overflow, considering the predicted storm rainfall and the continuous aftershocks. According to the analysis results, airborne remote sensing demonstrated the advantages of being intelligent, being able to maneuver, and providing high resolution. These advantages allowed us to quickly monitor and assess the distribution and dynamic changes of the barrier lakes in the earthquake-hit region using multitemporal airborne remote sensing images.
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