This correspondence proposes an edge embedded marker-based watershed algorithm for high spatial resolution remote sensing image segmentation. Two improvement techniques are proposed for the two key steps of maker extraction and pixel labeling, respectively, to make it more effective and efficient for high spatial resolution image segmentation. Moreover, the edge information, detected by the edge detector embedded with confidence, is used to direct the two key steps for detecting objects with weak boundary and improving the positional accuracy of the objects boundary. Experiments on different images show that the proposed method has a good generality in producing good segmentation results. It performs well both in retaining the weak boundary and reducing the undesired over-segmentation.

Multiscale segmentation is always needed to extract semantic meaningful objects for object-based remote sensing image analysis. Choosing the appropriate segmentation scales for distinct ground objects and intelligently combining them together are two crucial issues to get the appropriate segmentation result for target applications. With respect to these two issues, this paper proposes a simple scale-synthesis method which is highly flexible to be adjusted to meet the segmentation requirements of varying image-analysis tasks. The main idea of this method is to first divide the whole image area into multiple regions; each region consisted of ground objects that have similar optimal segmentation scale. Then, synthesize the suboptimal segmentations of each region to get the final segmentation result. The result is the combination of suboptimal scales of objects and is therefore more coherent to ground objects. To validate this method, the land-cover-category map is used to guide the scale synthesis of multiscale image segmentations for the Quickbird-image land-use classification. First, the image is coarsely divided into multiple regions; each region belongs to a certain land-cover category. Then, multiscale-segmentation results are generated by the Mumford-Shah function based region-merging method. For each land-cover category, the optimal segmentation scale is selected by the supervised segmentation-accuracy-assessment method. Finally, the optimal scales of segmentation results are synthesized under the guide of land-cover category. It is proved that the proposed scale-synthesis method can generate a more accurate segmentation result that benefits the latter classification. The land-use-classification accuracy reaches to 77.8%.

This paper proposes a systematic image mosaicking methodology to produce hyperspectral image for environment monitoring using an emerging UAV-based push-broom hyperspectral imager. The suitability of alternative methods in each step is assessed by experiments of an urban scape, a river course and a forest study area. First, the hyperspectral image strips were acquired by sequentially stitching the UAV images acquired by push-broom scanning along each flight line. Next, direct geo-referencing was applied to each image strip to get initial geo-rectified result. Then, with ground control points, the curved surface spline function was used to transform the initial geo-rectified image strips to improve their geometrical accuracy. To further remove the displacement between pairs of image strips, an improved phase correlation (IPC) and a SIFT and RANSAC-based method (SR) were used in image registration. Finally, the weighted average and the best stitching image fusion method were used to remove the spectral differences between image strips and get the seamless mosaic. Experiment results showed that as the GCPs‘ number increases, the mosaicked image‘s geometrical accuracy increases. In image registration, there exists obvious edge information that can be accurately extracted from the urban scape and river course area; comparative results can be achieved by the IPC method with less time cost. However, for the ground objects with complex texture like forest, the edges extracted from the image is prone to be inaccurate and result in the failure of the IPC method, and only the SR method can get a good result. In image fusion, the best stitching fusion method can get seamless results for all three study areas. Whereas, the weighted average fusion method was only useful in eliminating the stitching line for the river course and forest areas but failed for the urban scape area due to the spectral heterogeneity of different ground objects. For different environment monitoring applications, the proposed methodology provides a practical solution to seamlessly mosaic UAV-based push-broom hyperspectral images with high geometrical accuracy and spectral fidelity.

In object-oriented multi-source remote sensing imagery classification, it is an essential prerequisite to locate objects on different images. The spatial uncertainty of located object boundaries is unavoidable and may have a significant impact on the subsequent object feature calculation and classification. To seek the proper object location scheme, the image resampling and the transfer of object boundaries are studied by uncertainty impact analysis. Results indicate when images are resampled to high spatial resolution, the object statistical features and classification accuracy are little affected by the object boundary uncertainty; transfer of raster or vector object boundaries are both adoptable. Whereas when images are geo-registered without changing spatial resolution boundary uncertainty has a significant influence on the statistical value of texture feature and tends to induce the instability of classification results, the object locational uncertainty cannot be disregarded unless it is controlled in a certain limited range.

This paper designs an object-based confusion matrix (OCM) classification accuracy assessment scheme to accurately estimate the overall and individual category classification accuracy. The estimation protocol and the sample data collection procedures are both taken into account. On the one hand, the two commonly used OCM construction methods based on object element and weighted by object area are analyzed, which indicate the classifier's distinguish ability and the thematic map accuracy respectively. With consideration that the object location uncertainty introduces the reference category uncertainty and may lead to the bias of thematic map accuracy assessment result, a novel fuzzy OCM construction method is proposed to more accurately assess the classification accuracy. On the other hand, a simple sample data collection strategy is proposed and validated to collect representative accuracy assessment samples. The object-oriented Quickbird image land use classification accuracy assessment experiment results are analyzed to validate the applicability of the proposed schemes. Suggestions on how to use object-based confusion matrix method in classification accuracy assessment are given in conclusion.

A water quality parameter retrieval scheme based on the UAV push-broom hyperspectral images was designed and validated for assessing the ecological health of Zhang Wei Xin River in Dezhou distinct, China. First, a UAV carrying a push-broom hyperspectral imager that is lightweight and has a small size was used to acquire high spatial and hyperspectral resolution images. Then, the mosaicked reflectance data of the whole river were produced by a seamless image mosaicking method with high geometrical accuracy and spectral fidelity. Next, the in-field measurements of different parameters and the corresponding spectral reflectance from the mosaicked images at the sampling points were used to build the water quality parameter retrieval models for total phosphorus (TP), chlorophyll a (Chla), and total suspended solids (TSS). To validate the model, the retrieval results of the testing sampling points were compared with the measured parameters. The coefficients of determination R² of TP, Chla, and TSS were 0.886, 0.918, and 0.968, respectively. The retrieved TP, Chla, and TSS maps showed that the water pollution of Zhang Wei Xin River is serious, the total phosphorus exceeds the standard, and the water body is in a state of eutrophication. The UAV-based hyperspectral remote sensing technique provides a cost-effective method for inland water monitoring at a local scale with high accuracy.