The unmanned aerial vehicle (UAV) sensors and platforms nowadays are being used in almost every application (e.g., agriculture, forestry, and mining) that needs observed information from the top or oblique views. While they intend to be a general remote sensing (RS) tool, the relevant RS data processing and analysis methods are still largely ad-hoc to applications. Although the obvious advantages of UAV data are their high spatial resolution and flexibility in acquisition and sensor integration, there is in general a lack of systematic analysis on how these characteristics alter solutions for typical RS tasks such as land-cover classification, change detection, and thematic mapping. For instance, the ultra-high-resolution data (less than 10 cm of Ground Sampling Distance (GSD)) bring more unwanted classes of objects (e.g., pedestrian and cars) in land-cover classification; the often available 3D data generated from photogrammetric images call for more advanced techniques for geometric and spectral analysis. In this paper, we perform a critical review on RS tasks that involve UAV data and their derived products as their main sources including raw perspective images, digital surface models, and orthophotos. In particular, we focus on solutions that address the “new” aspects of the UAV data including (1) ultra-high resolution; (2) availability of coherent geometric and spectral data; and (3) capability of simultaneously using multi-sensor data for fusion. Based on these solutions, we provide a brief summary of existing examples of UAV-based RS in agricultural, environmental, urban, and hazards assessment applications, etc., and by discussing their practical potentials, we share our views in their future research directions and draw conclusive remarks.

Cervical cancer remains an important public health problem worldwide. New and effective therapeutic strategies targeting cervical cancer are urgently needed. Long non-coding RNAs (lncRNAs) are newly identified regulators in tumorigenesis and tumor progression. To investigate the role of lncRNA MEG3 in the development of cervical cancer, we examined MEG3 expression in 18 pairs of cervical cancer and matched adjacent non-neoplastic tissues. Real-time quantitative RT-PCR (qRT-PCR) results showed high expression levels of MEG3 in non-neoplastic tissues, but markedly lower levels in cancer tissues. We further investigated whether the restoration of MEG3 expression might affect the proliferation of cervical carcinoma cells. Ectopic expression of MEG3 inhibited the proliferation of human cervical carcinoma cells HeLa and C-33A in vitro. On the other hand, knockdown of MEG3 promoted the growth of well-differentiated cervical carcinoma HCC94 cells. Further investigation into the mechanisms responsible for the growth inhibitory effects revealed that overexpression of MEG3 resulted in the induction of G2/M cell cycle arrest and apoptosis. These results identified an important role of MEG3 in the molecular etiology of cervical cancer and implicated the potential application of MEG3 in cervical cancer therapy.

Due to the unprecedented technology development of sensors, platforms and algorithms for 3D data acquisition and generation, 3D spaceborne, airborne and close-range data, in the form of image based, Light Detection and Ranging (LiDAR) based point clouds, Digital Elevation Models (DEM) and 3D city models, become more accessible than ever before. Change detection (CD) or time-series data analysis in 3D has gained great attention due to its capability of providing volumetric dynamics to facilitate more applications and provide more accurate results. The state-of-the-art CD reviews aim to provide a comprehensive synthesis and to simplify the taxonomy of the traditional remote sensing CD techniques, which mainly sit within the boundary of 2D image/spectrum analysis, largely ignoring the particularities of 3D aspects of the data. The inclusion of 3D data for change detection (termed 3D CD), not only provides a source with different modality for analysis, but also transcends the border of traditional top-view 2D pixel/object-based analysis to highly detailed, oblique view or voxel-based geometric analysis. This paper reviews the recent developments and applications of 3D CD using remote sensing and close-range data, in support of both academia and industry researchers who seek for solutions in detecting and analyzing 3D dynamics of various objects of interest. We first describe the general considerations of 3D CD problems in different processing stages and identify CD types based on the information used, being the geometric comparison and geometric-spectral analysis. We then summarize relevant works and practices in urban, environment, ecology and civil applications, etc. Given the broad spectrum of applications and different types of 3D data, we discuss important issues in 3D CD methods. Finally, we present concluding remarks in algorithmic aspects of 3D CD.

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There have been increasing demands for automatically monitoring urban areas in very high detail, and the Unmanned Aerial Vehicle (UAV) with auto-navigation (AUNA) system offers such capability. This study proposes an object-based hierarchical method to detect changes from UAV images taken at different times. It consists of several steps. In the first step, an octocopter with AUNA capability is used to acquire images at different dates. These images are registered automatically, based on SIFT (Scale-Invariant Feature Transform) feature points, via the general bundle adjustment framework. Thus, the Digital Surface Models (DSMs) and orthophotos can be generated for raster-based change analysis. In the next step, a multi-primitive segmentation method combining the spectral and geometric information is proposed for object-based analysis. In the final step, a multi-criteria decision analysis is carried out concerning the height, spectral and geometric coherence, and shape regularity for change determination. Experiments based on UAV images with five-centimeter ground resolution demonstrate the effectiveness of the proposed method, leading to the conclusion that this method is practically applicable for frequent monitoring.

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New aerial sensors and platforms (e.g., unmanned aerial vehicles (UAVs)) are capable of providing ultra-high resolution remote sensing data (less than a 30-cm ground sampling distance (GSD)). This type of data is an important source for interpreting sub-building level objects; however, it has not yet been explored. The large-scale differences of urban objects, the high spectral variability and the large perspective effect bring difficulties to the design of descriptive features. Therefore, features representing the spatial information of the objects are essential for dealing with the spectral ambiguity. In this paper, we proposed a dual morphology top-hat profile (DMTHP) using both morphology reconstruction and erosion with different granularities. Due to the high dimensional feature space, we have proposed an adaptive scale selection procedure to reduce the feature dimension according to the training samples. The DMTHP is extracted from both images and Digital Surface Models (DSM) to obtain complimentary information. The random forest classifier is used to classify the features hierarchically. Quantitative experimental results on aerial images with 9-cm and UAV images with 5-cm GSD are performed. Under our experiments, improvements of 10% and 2% in overall accuracy are obtained in comparison with the well-known differential morphological profile (DMP) feature, and superior performance is observed over other tested features. Large format data with 20,000 × 20,000 pixels are used to perform a qualitative experiment using the proposed method, which shows its promising potential. The experiments also demonstrate that the DSM information has greatly enhanced the classification accuracy. In the best case in our experiment, it gives rise to a classification accuracy from 63.93% (spectral information only) to 94.48% (the proposed method).