Deep Feature Learning for Spectral-Spatial Classification of Hyperspectral Remote Sensing Images

Fahim Irfan Alam, University, My, Jun Zhou
The recent advances in aerial- and satellite-based hyperspectral imaging sensor technologies have led to an increased availability of Earth's images with high spatial and spectral resolution, which opened the door to a large range of important applications. Hyperspectral imaging records detailed spectrum of the received light in each spatial position in the image, in which each pixel contains a highly detailed representation of the reflectance of the materials present on the ground, and a
more » ... characterization in terms of geometrical details. Since different substances exhibit different spectral signatures, the abundance of informative content conveyed in the hyperspectral images permits an improved characterization of different land coverage. Therefore, hyperspectral imaging emerged as a well-suited technology for accurate image classi fication in remote sensing. In spite of that, a signi ficantly increased complexity of the analysis introduces a series of challenges that need to be addressed on a serious note. In order to fully exploit the potential offered by these sensors, there is a need to develop accurate and effective models for spectral-spatial analysis of the recorded data. This thesis aims at presenting novel strategies for the analysis and classifi cation of hyperspectral remote sensing images, placing the focal point on the investigation on deep networks for the extraction and integration of spectral and spatial information. Deep learning has demonstrated cutting-edge performances in computer vision, particularly in object recognition and classi cation. It has also been successfully adopted in hyperspectral remote sensing domain as well. However, it is a very challenging task to fully utilize the massive potential of deep models in hyperspectral remote sensing applications since the number of training samples is limited which limits the representation capability of a deep model. Furthermore, the existing architectures of deep models need to be further investigated and modifi ed accordingly to bette [...]
doi:10.25904/1912/1943 fatcat:6jewidsvwjcjpf6s5yi77cbofm