Very high-resolution remote sensing images are used to detect the changes with finer results. Accompanying with plenty of details, the environment around targets also becomes more complex, which poses challenges for change detection. In recent years, hybrid methods based on transformer and CNN have been widely used. The two methods usually are adopted to extract the change information because they can take advantage of global semantic relations and long-range spatial dependencies at the same time. However, within some complex environments, there is still information loss and inaccurate detection because the features cannot be fully integrated. We proposed a new cross-level hybrid feature aggregation network for change detection to improve the performance of change detection, especially within a complex environment. Within the new network, a parallel hybrid CNN-Transformer structure is adopted to model globally and locally, which extracts the features of different levels and produces rich semantic features. Meanwhile, the multi-branch feature interaction is used to implement interaction and fusion for multiscale feature information. Furthermore, multiscale feature aggregation was applied to remove redundancy. Subsequently, CNN-Transformer change feature enhancement is used to enhance the representation. Compared with several state-of-the-art methods on three available datasets, the accuracy is increased by 0.09%, 1.12%, and 2.62%, respectively. The experiments indicate that the method proposed in this paper detects the changed targets as continuous and complete objects with clear edges. Within a complex environment, it suppresses pseudo-changes and extracts more small changed targets.
The application of High Performance Computing (HPC) technology to remote sensing data processing is one solution to
meet the requirements of remote sensing real- or near-real-time processing capabilities. We presented a cluster-based
parallel processing system for HJ-1 satellites data, named Cluster Pro. This paper presents the basic architecture and
implementation of the system. We did imagery mosaic experiment with the Cluster Pro, where the HJ-1 CCD data in
Beijing city was used. The experiments showed that the Cluster Pro was a useful system to improve the efficiency of data
processing. Further work would focus on the comprehensive parallel design and implementations of remote sensing data
processing.
The fusion effect on the high-resolution remote sensing image using the traditional fusion technique such as Principal Component Analysis(PCA), is not satisfying. Considering the well-developed technique of Minimum Noise Fraction(MNF) transform and the flexible ability of Wavelet transform, a new fusion method (MNFWT) integrating MNF and Wavelet transform was studied using multi-spectral (MS) IKONOS image at 4-m spatial resolution and panchromatic (PAN) IKONOS image at 1-m resolution. Compared with PCA fusion method, MNFWT approach performs more efficiently both in improving the spatial information and preserving the spectral information.
We present a new object-oriented land cover classification method integrating raster analysis and vector analysis, which adopted improved Color Structure Code (CSC) for segmentation and Support Vector Machine (SVM) for classification using Very High Resolution (VHR) QuickBird data. It synthesized the advantage of digital image processing, Geographical Information System (GIS) (vector-based feature selection) and Data Mining (intelligent SVM classification) to interpret image from pixels to segments and then to thematic information. Compared with the pixelbased SVM classification in ENVI 4.3, both of the accuracy of land cover classification by the proposed method and the computational performance for classification were improved. Moreover, the land cover classification map can update
GIS database in a quick and convenient way.
This paper describes techniques for improving the accuracy and stability of the optical voltage transformers (OVT), which are affected by temperature around circumstances. Higher accuracy and stability is achieved by two light- channels compensation method, which uses one polarizing beam splitter. Many test show that this compensation method can eliminate the effects of unwanted birefringence induced by temperature and around circumstances of OVT effectively. OVT which uses this compensation method has passed the type test with accuracy and stability within +/- 2 percent.
The stability of optical voltage transformers has been the main obstruction to its practical application. An optical voltage sensor is the core of an optical voltage transformer. This paper proposed a novel reflection-type transverse modulation optical voltage sensor with double light channels temperature compensation. The sensor is based on the Pockels effect in a Bi4Ge3O12 (BGO) crystal. The measured voltage is applied to the crystal along the <001> direction with light wave passing through the crystal along the <110> direction. Theoretical analysis shows that this is an optimum configuration for a reflection-type transverse modulation optical voltage sensor. The novel optical voltage sensor was employed to develop a novel optical voltage transformer (OVT) for 220 kV power systems. There isn't any capacitor divider in the OVT. The measured high voltage is applied to the optical voltage sensor directly. Experiments showed that the sensor has good double light channels temperature compensation characteristic and the stability of the OVT could reach +/- 0.4% during two weeks. The principle, structure and test results of the OVT are presented in this paper.
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