Environmental Information Extraction from Satellite Remote Sensing Imagery

Environmental data can be classified into three categories: spatial data, temporal data, and spatio-temporal data. For example, a remote sensing image can be considered as spatial data; daily temperature observations of a weather station are temporal data, and multi-temporal images or a sequence of remote sensing images can be considered as spatio-temporal data. In recent years, the number of remote sensing satellites with high pixel resolution has increased significantly and remote sensing images were widely used for environmental monitoring and related applications. Therefore, extraction of useful information from satellite images acquired by various sensors is crucial for the success of such applications.
The main objective of this study is to develop techniques for extraction of land-surface information from satellite remote sensing images. Implementation of such techniques is demonstrated by three applications - image classification, image fusion, and landcover change detection.
Image classification
A nonparametric indicator kriging (IK) approach of remote sensing image classification is developed. The work of image classification is transformed into estimation of class-dependent probabilities in feature space using indicator kriging. Each pixel is then assigned to the class with maximum class probability. The IK classifier yields 100% classification accuracy for training data provided that colocated data in feature space do not exist. An example of landcover classification for the Taipei metropolitan area using SPOT images demonstrated that the proposed indicator kriging classifier is superior to the maximum likelihood classifier in terms of producer’s and user’s classification accuracies.
Image Fusion
A knowledge-based scale transfer (KBST) fusion technique was developed. Firstly, SPOT multispectral (XS) images were used for major categories (water, vegetation, and bare soil/built-up) landcover classification of the study area. Regression relationships between digital numbers of panchromatic (PAN) and XS images were then established and used for subsequent scale transfer. The class-specific regression models help to preserve spectral information during scale transfer. Finally, a scale transfer algorithm using class-specific regression models was adopted for fusion of SPOT XS and PAN images, yielding a multispectral, high-resolution image which offers more details of the study area than other spatial domain fusion techniques.
Change detection
A hypothesis-test approach for landcover change detection was developed using multispectral images acquired in two different dates. Major category landcover classification using multispectral images was implemented for both (pre- and post-) images to identify sets of no-change pixels. Using only no-change pixels, same-band digital numbers of the two images of specific landcover classes were assumed to form a bivariate normal distribution.
The work of change detection was then placed in the framework of hypothesis test with null hypothesis of no-change. Critical regions with respect to a chosen level of significance a were then determined for each landcover class using the bivariate normal distribution. Finally, landcover changed areas were determined with desired confidence levels. We demonstrated that the hypothesis-test approach of change detection is promising and deserve further investigation.