Exploiting hyperspectral and multispectral images in the detection of tree species: A review

Abstract

Classification of tree species provides important data in forest monitoring, sustainable forest management and planning. The recent developments in Multi Spectral (MS) and Hyper Spectral (HS) Imaging sensors in remote sensing have made the detection of tree species easier and accurate. With this systematic review study, it is aimed to understand the contribution of using the Multi Spectral and Hyper Spectral Imaging data in the detection of tree species while highlighting recent advances in the field and emphasizing important directions together with new possibilities for future inquiries. In this review, researchers and decision makers will be informed in two different subjects: First one is about the processing steps of exploiting Multi Spectral and HS images and the second one is about determining the advantages of exploiting Multi Spectral and Hyper Spectral images in the application area of detecting tree species. In this way exploiting satellite data will be facilitated. This will also provide an economical gain for using commercial Multi Spectral and Hyper Spectral Imaging data. Moreover, it should be also kept in mind that, as the number of spectral tags that will be obtained from each tree type are different, both the processing method and the classification method will change accordingly. This review, studies were grouped according to the data exploited (only Hyper Spectral images, only Multi Spectral images and their combinations), type of tree monitored and the processing method used. Then, the contribution of the image data used in the study was evaluated according to the accuracy of classification, the suitable type of tree and the classification method.

Enhanced Hyperspectral Sharpening through Improved Relative Spectral Response Characteristic (R-SRC) Estimation for Long-Range Surveillance Applications

“…Over the past few decades, hyperspectral imaging (HSI) has been one of the most versatile and effective air-and space-borne remote sensing techniques for the exploration of ground properties, due to the rich spectral content of the imagery [1]. Recent advances in sensor hardware, software, and machine learning tools allow HSI to be widely used across multidisciplinary sectors [2,3], especially for airborne surveillance applications [4]. Despite the very rich spectral information inherent in the HSI, its spatial resolution is substantially lower than its spectral content.…”

“…Consequently, the most popular approach to SR research has been software based [5][6][7][8][9][10], which is capable of recovering the HRHSI scene even from a single input of LRHSI data through the estimation of the blurring kernel in the LRHSI scene [13] (see Equation (2) in Section 2). However, fusion of high-spatialresolution multispectral images (HRMSI) or high-resolution panchromatic images with low-spatial-resolution hyperspectral images (LRHSI) has been a highly popular and more effective approach for the recovery of HRHSI, with accuracy far better than that using single LRHSI as the input [3,[5][6][7][8][9][10][14][15][16]. There are a number of fusion schemes designed to implement this kind of SR: the spectral unmixing convex programming approach using coupled non-negative factorization (CNMF) [17][18][19][20], various forms of coupled matrix and tensor factorization optimizations (CMTF) [21][22][23], nonlinear unmixing through intrinsic and extrinsic priors [24], and the implementation of CNMF in deep learning (DL) network architectures [5,[25][26][27], which have been reported in the literature over the past couple of decades.…”