The mixed pixel problem of remote sensing imagery has made spectral mixture analysis (SMA) a predominant method in the accurate interpretation of urban surface materials. High spatial resolution imagery is very beneficial in the extraction of pure pixels in SMA, but its high intraclass variability has seriously affected the accuracy of SMA. The multiple endmember spectral mixture analysis (MESMA) provides a good solution for high intraclass variability. Previous studies, however, were basically pixel-based and spectral-based, and ignored the effects of neighboring pixels on endmember spectra. To solve this problem, this study took full advantage of spatial–spectral information and proposed a multiple endmember object spectral mixture analysis (MEOSMA) approach for high spatial resolution imagery. Combined with object-based image analysis, the segment-based endmember object extraction method was developed, which used both spatial and spectral attributes to extract “endmember objects.” Then, an endmember object optimization method considering spatial correlation was put forward to select different endmember object combinations for different pixels. Compared with MESMA and simple endmember SMA, the higher correct unmixed proportion and determination coefficient (R2) indicated that MEOSMA is more accurate and has great potential for applications in urban environmental monitoring.
The unprecedented amount and multiple applications of remote sensing image data have created a strong need for efficient data transmission. Commonly used in the transmission of various types of data, peer-to-peer (P2P) opens up new possibilities for the transmission of remote sensing image data. A considerable amount of work has been done toward the transmission of remote sensing data by using map tiles for fast online browsing. However, issues concerning the transmission of original image data require more volume and flexibility, which is indispensable in the application of remote sensing images. According to the spatial and band characteristics of remote sensing images, an approach using image subset blocks (ISBs) is proposed for P2P transmission of remote sensing image data. The method improves efficiency in two ways: transmitting ISBs on demand to reduce unnecessary transmission and using a P2P method to break the bandwidth bottleneck of the central server. The results of the performance evaluation reveal that compared with the traditional transmission method that uses the central server, the proposed method considerably enhances the efficiency to approach the level of general P2P file transmission.
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