Forest resource inventory plays a key role in forest management and monitoring. Using photogrammetry and remote sensing techniques in forest resource inventory has obtained significant results such as getting the spatial information of forest. However, during the image capturing process, numerous influential factors hinder the quality of these images, such as the shadows caused by the different angle of the sun, terrain features, and surface object occlusion. Furthermore, the shadows in optical remote sensing images are regarded as image nuisances in numerous applications, specifically, change detection and image classification frequently affecting the accuracy of analytical results. Therefore, research on shadow processing in images is highly valued. In recent years, airborne multispectral aerial image devices have been developed high radiometric resolution data, including Leica ADS-40, Intergraph DMC. These devices are capable of capturing radiometric resolution images of 12 bits or higher, for example, a 12-bit digital number (DN) ranged from 0–4,095. The increased radiometric resolution of digital imagery provides more radiometric detail of potential use in classification or interpretation of land cover of shadow areas. Therefore, the classification of the shadow areas was tested by using four compensation methods (Method 1, used 13-bit spectral information in shadow area for classification; Method 2 used Linear Correlation Correction (LCC) before the classification; Method 3 used Histogram Matching (HM) before the classification, and Method 4 used Multi-Source Data Fusion (MSDF) to aid in classification of shadows.), and compared the benefits of those shadow compensation methods. The results indicated that Method 1 (13-bit high radiometric resolution images; no treatment) and method 2 (LCC) presented better land cover classification results and possessed significant accuracy (over 90% of overall accuracy), and this result further demonstrates that the 13 bit high radiometric resolution images (ADS-40) have sufficient information to satisfy classification of shadow areas.

This paper presents a Discrete Wavelet Transform (DWT) based new method for representation of image feature lines. Some tests are done by using the compactly supported orthonormal Haar and Daubechies wavelets, where image feature lines are automatically generated by an automatic edge detection method which utilizes wavelets-derived symmetric gradient operators and wavelets-based zero-crossing approach, and used as test data. Test results show that feature lines on a 2D image plane can be represented in a flexible manner by the proposed method. The representation can be done as accurately as needed, where the related parameters can be determined automatically. In addition, explicit and user-friendly formulas of DWT and Discrete Wavelet Reconstruction (DWR) are also given in this paper.

The objective of the present research is using Unmanned Aerial Vehicle System (UAVS) to probe the spatial and time variations of the pollutants in the air and water realm. In the air portion is to evaluate the performance of O3, NO2 and NMHC measurements using UAVs in Kaohsiung city. In the water portion, the pollutants discharged from factory waster water will be discussed. In this paper, a high-performance, long-endurance UAV system is developed to provide a platform for three-dimensional pollution measurement and monitoring requirements. The system includes carbon fiber composite airframe, GPS navigation, autopilot, RF data transmission and integrated air pollution sensing systems. The efficiency of each system are optimized and verified through numerical simulation or experiments, and interfaces between UAV and air pollution sensing systems are also considered to provide ideal air flow to the sensor under the extreme conditions at high altitude and high speed. Therefore, the UAV system can display air pollution measurement changes in real time and combine these results with GPS and geography data to monitor air quality in three dimension space. The air sensing results are compared with data from ground monitoring site. The result shows that the UAV system can offer real-time, 3-dimensional, and large area measuring results, which can obtain useful data for the government, industry, and research units. Also, when the water in our rivers, lakes, and oceans becomes polluted, the effects will be far reaching. It can endanger wildlife, make our drinking water unsafe. No doubt we need a more reliable tool to monitor the polluting water. Therefore a platform composed of the technologies of : Wing-In-Ground craft, the water-detecting sensors, the IT skill, and the cloud-computing service were bundled for this purpose . This platform can execute smart 3D UAV-based detecting technology of water pollution, and would be useful to make the coastal and river basin management more efficient.

Miniature Multiple Camera Array (MiniMCA) is a frame-based multispectral sensor, which compose of multiple cameras with different filters to acquire images range from visible light to near infrared. Due to its light weight and small size, it is suitable for mounting on an Unmanned Aerial Vehicle (UAV) for acquiring high spatial, high temporal, and multispectral imagery. However, since all cameras have different perspective centers and view directions as well as different lens distortion effects, which will result in significant band-misregistration phenomena on the original images. In this study, a band registration scheme based on perspective transformation is thus proposed for MiniMCA sensor. It starts from indoor camera calibration to obtain the interior orientation parameters (IOPs) and relative orientation parameters (ROPs). Next, the slave images are transferred into the master camera’s image space through perspective transformation, where the coefficients are directly acquired from the estimated IOPs and ROPs. In the end, a systematic error correction (including displacement removal and lens distortion correction) is adopted to minimize the band misregistration effect. Through visual comparison and quantitative accuracy assessment, the experimental results show that the average of band misregistration errors are between 0.2 - 0.5 pixels, which proves that the accuracy of the proposed scheme is accurate and satisfy the demand of remote sensing applications.

Volunteered Geographic Information (VGI) has become an important geospatial data source for GIS applications. The data quality of VGI is an important issue as it is produced by non-professional volunteers. The objectives of this study are to establish the evaluation procedure and analyze the data quality for 3D building models of National Chiao Tung University (NCTU) from Open Street Map (OSM). The reference 3D building models are measured by professional mapping agency using stereo aerial images. The evaluation procedure includes completeness, shape analysis, surface distance analysis and building story analysis. In the experiments, the completeness of building model reached 90%; the shape difference usually occurred on small building parts; only two buildings showed height difference larger than 1 floor; most of the surface distance was better than 30%. The experimental results indicated that the 3D building models from OSM may satisfy the needs of small scale mapping in particular areas.