Principle Component Analysis for Crop Discrimination using Hyperspectral Remote Sensing Data

Independent-component analysis for hyperspectral remote sensing imagery classification

Harold Szu, FELLOW SPIE Office of Naval Research Arlington, Virginia 22217 Abstract. We investigate the application of independent-component analysis ICA to remotely sensed hyperspectral image classification. We focus on the performance of two well-known and frequently used ICA algorithms: joint approximate diagonalization of eigenmatrices JADE and FastICA; but the proposed method is applicable...

Remote sensing based on hyperspectral data analysis

Drought Analysis of Alvand Boundary River Using Remote Sensing Data

Extended abstract 1- Introduction       The study of the behavior of rivers in the arid and dry areas is one of the most important tasks in the country. Because the area has increased the effects of drought due to the sensitivity of the area and rainfall shortage, it causes changes in the flow and sediment regime, water resources, agriculture, and so on. Since plants react more precisely to t...

Classifying Hyperspectral Remote Sensing Imagery With Independent Component Analysis

In this paper, we investigate the application of independent component analysis (ICA) to remotely sensed hyperspectral image classification. We focus on the performance of Joint Approximate Diagonalization of Eigenmatrices (JADE) algorithm, although the proposed method is applicable to other popular ICA algorithms. The major advantage of using ICA is its capability of classifying objects with u...

Kernel Principal Component Analysis for the Classification of Hyperspectral Remote Sensing Data over Urban Areas

Kernel Principal Component Analysis (KPCA) is investigated for feature extraction from hyperspectral remote-sensing data. Features extracted using KPCA are classified using linear support vector machines. In one experiment it is shown that kernel principal component features are more linearly separable than features extracted with conventional principal component analysis. In a second experimen...