Abstract
Classification is a common objective when analyzing hyperspectral images, where each pixel is assigned to a predefined label. Deep learning-based algorithms have been introduced in the remote-sensing community successfully in the past decade and have achieved significant performance improvements compared with conventional models. However, research on the extraction of sequential features utilizing a single image, instead of multi-temporal images still needs to be further investigated. In this paper, a novel strategy for constructing sequential features from a single image in long short-term memory (LSTM) is proposed. Two pixel-wise-based similarity measurements, including pixel-matching (PM) and block-matching (BM), are employed for the selection of sequence candidates from the whole image. Then, the sequential structure of a given pixel can be constructed as the input of LSTM by utilizing the first several matching pixels with high similarities. The resulting PM-based LSTM and BM-based LSTM are appealing, as all pixels in the whole image are taken into consideration when calculating the similarity. In addition, BM-based LSTM also utilizes local spectral-spatial information that has already shown its effectiveness in hyperspectral image classification. Two common distance measures, Euclidean distance and spectral angle mapping, are also investigated in this paper. Experiments with two benchmark hyperspectral images demonstrate that the proposed methods achieve marked improvements in classification performance relative to the other state-of-the-art methods considered. For instance, the highest overall accuracy achieved on the Pavia University image is 96.20% (using both BM-based LSTM and spectral angle mapping), which is an improvement compared with 84.45% overall accuracy generated by 1D convolutional neural networks.
Highlights
Hyperspectral remote-sensing images (HSI) can entail both abundant spectral and spatial information, which generally provides enhanced capability of distinguishing different objects from one another, relative to multispectral images, and play an important role in a variety of research domains, such as precision agriculture [1], land-use monitoring [2,3], change detection [4,5], and environment measurements [6]
Class 15 (Vineyard_untrained, Violet) is the class with the lowest accuracy due to the high spectral and thematic similarity between this vineyard class and other grape fields, and the best classification result for this class is acquired from 1D CNN (1DCNN), with 59.34% accuracy
LSTM_BM_EU and LSTM_BM_SAM markedly improve classification accuracy by utilizing spatial features, where class 15 accuracies increase by 7.99% and 10.51%, respectively, compared with the 1DCNN result
Summary
Hyperspectral remote-sensing images (HSI) can entail both abundant spectral and spatial information, which generally provides enhanced capability of distinguishing different objects from one another, relative to multispectral images, and play an important role in a variety of research domains, such as precision agriculture [1], land-use monitoring [2,3], change detection [4,5], and environment measurements [6]. Shi et al [46] proposed another strategy to design the sequential data in RNN model instead of taking spectral vector from all bands as one sequential data, but taking advantage of spatial neighbors For this method, local spectral-spatial features were first extracted by exploiting a 3DCNN on a local image patch, and sequences were built based on an eight-directional construction. The action of re-ordering those pixels in terms of their similarity measures is to ensure that all obtained sequential features admit “comparable” formats in terms of monotonically-decreasing similarity to the original pixel (i.e., the target pixel, or the given pixel of interest) In this framework, the influence of unlabeled data in an HSI is enhanced compared with conventional supervised-learning methods due to our proposed spatial selection, where any pixel can be selected as a candidate to construct sequential features.
Sign-in/Register to view highlights & summary Sign-in/Register to access full text options 
Talk to us
Join us for a 30 min session where you can share your feedback and ask us any queries you have
Schedule a callDisclaimer: All third-party content on this website/platform is and will remain the property of their respective owners and is provided on "as is" basis without any warranties, express or implied. Use of third-party content does not indicate any affiliation, sponsorship with or endorsement by them. Any references to third-party content is to identify the corresponding services and shall be considered fair use under The CopyrightLaw.
