Principal component analysis (PCA) is one of the most commonly adopted feature reduction techniques in remote sensing image analysis. However, it may overlook subtle but useful information if directly applied to the analysis of hyperspectral data, especially for the discrimination among different vegetation types. In order to accurately map an invasive plant species (horse tamarind, Leucaena le...

Current hyperspectral remote sensing imagery spatial-spectral classification methods mainly consider concatenating the spectral information vectors and spatial information vectors together. However, the combined spatial-spectral information vectors may cause information loss and concatenation deficiency for the classification task. To efficiently represent the spatial-spectral feature informati...

Integrating spectral and spatial information is proved effective in improving the accuracy of hyperspectral imagery classification. In recent studies, two kinds of approaches are widely investigated: (1) developing a multiple feature fusion (MFF) strategy; and (2) designing a powerful spectral-spatial feature extraction (FE) algorithm. In this paper, we combine the advantages of MFF and FE, and...

Remote sensing has played a crucial role in mapping and understanding of the spatial pattern of mangrove forests and changes in its areal extent caused by natural disasters and anthropogenic forces. So far traditional pixel-based classification of multispectral imagery has been widely applied for broad mapping of mangrove covers. But the recent and more advanced hyperspectral data taken from se...

PURPOSE: This technical note demribea the spectral and spatial characteristics of hyperspectral data and the potential application of these data for wetlands studi~ and monitoring applications. The advantages and disadvantages of these data for wetland evaluations are discussed. Spectral signatures extracted tim data squired by NASA’s collected Airborne Visible/Infked Imagery Spectrometer (AVIR...

The overall goal of this work is to refine, validate, and transition a spectrum-matching and look-uptable (LUT) technique for rapidly inverting remotely sensed hyperspectral reflectances to extract environmental information such as water-column optical properties, bathymetry, and bottom classification. The work also seeks to combine hyperspectral imagery and LIDAR bathymetry to improve the capa...

Hyperspectral remote sensing increases the detectability of pixeland subpixel-sized targets by exploiting the finer detail in the spectral signatures. In this paper, we describe a new unsupervised algorithm for the detection of both full pixel and mixed pixel targets in hyperspectral imagery. The proposed method automatically resolves targets by using extended mathematical morphology operations...

There are some prevalent problems in the classification of hyperspectral remote sensing imagery currently, such as many bands, large amount of data, high proportion of mixed pixels and lower spatial resolution and so no. In order to solve the above problems, the sequential minimal optimization (SMO) algorithm is researched, and a supervised classification method based on binary decision tree SM...

When confronted with limited labelled samples, most studies adopt an unsupervised feature learning scheme and incorporate the extracted features into a traditional classifier (e.g., support vector machine, SVM) to deal with hyperspectral imagery classification. However, these methods have limitations in generalizing well in challenging cases due to the limited representative capacity of the sha...

Hyperspectral imagery contains a wealth of spectral and spatial information that can improve target detection and recognition performance. Typically, spectral information is inferred pixel-based, while spatial information related to texture, context and geometry are deduced on a per-object basis. Existing feature extraction methods cannot fully utilize both the spectral and spatial information....