The standard orthorectification process takes a lot of manpower and time to obtain control points. To correctly represent the image geometry on satellite images and improve the efficiency of satellite image orthorectification, a novel method for automatic satellite image orthorectification is proposed. In this study, a robust satellite image matching process is processed to obtain image control points, which adopted. Different from traditional labor-intensive methods, a novel image matching method is adopted to find image control points both on target images and an orthorectified reference image, which is adopted self-supervised deep learning image matching algorithm. This strategy makes the ortho-rectification process become automatic, robust, and attempts to distinguish more salient features than traditional methods in satellite images. The experimental results show that the automatic orthorectification process is not only stable but also adaptable. The quantity assessment is performed using root mean square error, and the accuracy of satellite image orthorectification result is 2 to 4 pixels under the 2-meter spatial resolution of FORMOSAT-5 images.

Due to geographical and geological factors, typhoons hit Taiwan frequently. It may cause serious disasters, so monitoring the condition of landslide area is critical. Based on the concept of satellite image time series (SITS), this study is to establish a long-term monitoring model by Normalized Difference Vegetation Index (NDVI) calculations and Maximum Likelihood Classification (MLC). For the temporal view, long-term changes in geologically sensitive area can be found out. For the spatial view, the interpretation standard can also be identified, and the location of the new landslide can also be distinguished by “Image Differencing”. This study selects “Liouguei District” and “Meishan open-cut tunnel” as the regions of interest. Through the establishment of satellite image time series, the goal of analyzing regional temporal and spatial changes is achieved, making the application of geospatial information more comprehensive.

Over the past 30 years, Taiwan has been invaded by Mikania micrantha. In order to effectively and quickly understand its distribution, this study conducted pixel-based image analysis and object-based image analysis with unmanned aerial vehicle images through remote sensing detection, and detected Mikania micrantha with artificial interpretation. Criteria for image interpretation, according to which the supervised classification is carried out. According to the classification results of different spatial resolutions, the best classification spatial resolution is about 10-15 cm and the images must be taken during the flowering season. If the spatial resolution is less than 10 cm, the salt and pepper effect is easy to form. It is recommended to perform supervised classification after object based image analysis or to fuse high resolution images (<10 cm) into a new low resolution image (10-15 cm) for classification. On this basis, the distribution of Mikania micrantha can be studied with RGB UAVs.

Forests in Taiwan cover approximately 60% of the island. High-resolution aerial imagery is an important data source for quantifying forest resources in countries around the world, so there is a continuous demand for image recognition and map editing. In this study, an automated forest cover classification model was developed using a Convolutional Neural Network (CNN). A single classification model for tree species was designed using 16 grid features, and successfully identified 19 common afforestation tree species in Taiwan from 3,248 DMC images. The research achieved an overall accuracy of 74.6% and feature analysis revealed that near-infrared band, digital elevation model, and aspect had significant contributions. In order to support forest resource investigation and map editing in Taiwan, a "Forest Cover Recognition Tool" was also developed. The vector data produced by this tool can be used by image interpreters to improve the quality of map editing and revision.