Classification of Hyperspectral Image using SVM Post-Processing for Shape Preserving Filter and PCA

This paper is based on an experimentation to preserve shapes of the natural classes in a hyperspectral image post classification of the image using SVM. The classifier classifies the vegetation types present in the hyperspectral image and then estimates the crop types present in the image. In doing so it preserves the spatial shapes of the vegetation types spread in the image using an Edge-preserving filter. The shape-preserving filter was applied prior to dimension reduction where by the low information content spectral components are discarded using Principal Component Analysis. The classification of the features is performed using SVM. The result has been found very effective in characterizing significant spectral and spatial structures of objects in a scene.

Generalized differential morphological profiles for remote sensing image classification

Differential morphological profiles (DMPs) are widely used for the spatial/structural feature extraction and classification of remote sensing images. They can be regarded as the shape spectrum, depicting the response of the image structures related to different scales and sizes of the structural elements (SEs). DMPs are defined as the difference of morphological profiles (MPs) between consecutive scales. However, traditional DMPs can ignore discriminative information for features that are across the scales in the profiles. To solve this problem, we propose scale-span differential profiles, i.e., generalized DMPs (GDMPs), to obtain the entire differential profiles. GDMPs can describe the complete shape spectrum and measure the difference between arbitrary scales, which is more appropriate for representing the multiscale characteristics and complex landscapes of remote sensing image scenes. Subsequently, the random forest (RF) classifier is applied to interpret GDMPs considering its robustness for high-dimensional data and ability of evaluating the importance of variables. Meanwhile, the RF "out-of-bag" error can be used to quantify the importance of each channel of GDMPs and select the most discriminative information in the entire profiles. Experiments conducted on three well-known hyperspectral data sets as well as an additional World View-2 data are used to validate the effectiveness of GDMPs compared to the traditional DMPs. The results are promising as GDMPs can significantly outperform the traditional one, as it is capable of adequately exploring the multiscale morphological information

Spectral–Spatial Classification of Hyperspectral Imagery with 3D Convolutional Neural Network

Recent research has shown that using spectral–spatial information can considerably improve the performance of hyperspectral image (HSI) classification. HSI data is typically presented in the format of 3D cubes. Thus, 3D spatial filtering naturally offers a simple and effective method for simultaneously extracting the spectral–spatial features within such images. In this paper, a 3D convolutional neural network (3D-CNN) framework is proposed for accurate HSI classification. The proposed method views the HSI cube data altogether without relying on any preprocessing or post-processing, extracting the deep spectral–spatial-combined features effectively. In addition, it requires fewer parameters than other deep learning-based methods. Thus, the model is lighter, less likely to over-fit, and easier to train. For comparison and validation, we test the proposed method along with three other deep learning-based HSI classification methods—namely, stacked autoencoder (SAE), deep brief network (DBN), and 2D-CNN-based methods—on three real-world HSI datasets captured by different sensors. Experimental results demonstrate that our 3D-CNN-based method outperforms these state-of-the-art methods and sets a new record

Automatic Object-Oriented, Spectral-Spatial Feature Extraction Driven by Tobler’s First Law of Geography for Very High Resolution Aerial Imagery Classification

(This article belongs to the Special Issue Recent Trends in UAV Remote Sensing)Aerial image classification has become popular and has attracted extensive research efforts in recent decades. The main challenge lies in its very high spatial resolution but relatively insufficient spectral information. To this end, spatial-spectral feature extraction is a popular strategy for classification. However, parameter determination for that feature extraction is usually time-consuming and depends excessively on experience. In this paper, an automatic spatial feature extraction approach based on image raster and segmental vector data cross-analysis is proposed for the classification of very high spatial resolution (VHSR) aerial imagery. First, multi-resolution segmentation is used to generate strongly homogeneous image objects and extract corresponding vectors. Then, to automatically explore the region of a ground target, two rules, which are derived from Tobler’s First Law of Geography (TFL) and a topological relationship of vector data, are integrated to constrain the extension of a region around a central object. Third, the shape and size of the extended region are described. A final classification map is achieved through a supervised classifier using shape, size, and spectral features. Experiments on three real aerial images of VHSR (0.1 to 0.32 m) are done to evaluate effectiveness and robustness of the proposed approach. Comparisons to state-of-the-art methods demonstrate the superiority of the proposed method in VHSR image classification.Peer Reviewe

Vector attribute profiles for hyperspectral image classification

International audienceMorphological attribute profiles are among the most prominent spectral-spatial pixel description methods. They are efficient, effective and highly customizable multi-scale tools based on hierarchical representations of a scalar input image. Their application to multivariate images in general, and hyperspectral images in particular, has been so far conducted using the marginal strategy, i.e. by processing each image band (eventually obtained through a dimension reduction technique) independently. In this paper, we investigate the alternative vector strategy, which consists in processing the available image bands simultaneously. The vector strategy is based on a vector ordering relation that leads to the computation of a single max-and min-tree per hyperspectral dataset, from which attribute profiles can then be computed as usual. We explore known vector ordering relations for constructing such max-trees and subsequently vector attribute profiles, and introduce a combination of marginal and vector strategies. We provide an experimental comparison of these approaches in the context of hyperspectral classification with common datasets, where the proposed approach outperforms the widely used marginal strategy