Practical Digital Terrain Model Extraction Using Image Inpainting Techniques

In some applications such as construction planning and land surveying, an accurate digital terrain model (DTM) is essential. However, in urban and sub-urban areas, the terrain may be covered by trees and man-made structures. Although digital surface model (DSM) obtained by radar or LiDAR can provide a general idea of the terrain, the presence of trees, buildings, etc. conceals the actual terrain elevation. Normally, the process of extracting DTM involves a land cover classification followed by a trimming step that removes the elevation due to trees and buildings. In this chapter, we assume the land cover types have been classified and we focus on the use of image inpainting algorithms for DTM generation. That is, for buildings and trees, we remove those pixels from the DSM and then apply inpainting techniques to reconstruct the terrain pixels in those areas. A dataset with DSM and hyperspectral data near the U. Houston area was used in our study. The DTM from United States Geological Survey (USGS) was used as the ground truth. Objective evaluation results indicate that some inpainting methods perform better than others

An Accurate Vegetation and Non-Vegetation Differentiation Approach Based on Land Cover Classification

Accurate vegetation detection is important for many applications, such as crop yield estimation, landcover land use monitoring, urban growth monitoring, drought monitoring, etc. Popular conventional approaches to vegetation detection incorporate the normalized difference vegetation index (NDVI), which uses the red and near infrared (NIR) bands, and enhanced vegetation index (EVI), which uses red, NIR, and the blue bands. Although NDVI and EVI are efficient, their accuracies still have room for further improvement. In this paper, we propose a new approach to vegetation detection based on land cover classification. That is, we first perform an accurate classification of 15 or more land cover types. The land covers such as grass, shrub, and trees are then grouped into vegetation and other land cover types such as roads, buildings, etc. are grouped into non-vegetation. Similar to NDVI and EVI, only RGB and NIR bands are needed in our proposed approach. If Laser imaging, Detection, and Ranging (LiDAR) data are available, our approach can also incorporate LiDAR in the detection process. Results using a well-known dataset demonstrated that the proposed approach is feasible and achieves more accurate vegetation detection than both NDVI and EVI. In particular, a Support Vector Machine (SVM) approach performed 6% better than NDVI and 50% better than EVI in terms of overall accuracy (OA)

Performance of Change Detection Algorithms Using Heterogeneous Images and Extended Multi-attribute Profiles (EMAPs)

We present detection performance of ten change detection algorithms with and without the use of Extended Multi-Attribute Profiles (EMAPs). Heterogeneous image pairs (also known as multimodal image pairs), which are acquired by different imagers, are used as the pre-event and post-event images in the investigations. The objective of this work is to examine if the use of EMAP, which generates synthetic bands, can improve the detection performances of these change detection algorithms. Extensive experiments using five heterogeneous image pairs and ten change detection algorithms were carried out. It was observed that in 34 out of 50 cases, change detection performance was improved with EMAP. A consistent detection performance boost in all five datasets was observed with EMAP for Homogeneous Pixel Transformation (HPT), Chronochrome (CC), and Covariance Equalization (CE) change detection algorithms

Recent Advances in Image Restoration with Applications to Real World Problems

In the past few decades, imaging hardware has improved tremendously in terms of resolution, making widespread usage of images in many diverse applications on Earth and planetary missions. However, practical issues associated with image acquisition are still affecting image quality. Some of these issues such as blurring, measurement noise, mosaicing artifacts, low spatial or spectral resolution, etc. can seriously affect the accuracy of the aforementioned applications. This book intends to provide the reader with a glimpse of the latest developments and recent advances in image restoration, which includes image super-resolution, image fusion to enhance spatial, spectral resolution, and temporal resolutions, and the generation of synthetic images using deep learning techniques. Some practical applications are also included