The surface of the earth is rapidly changing every day due to certain natural reasons and other impacts by society. Over the last few decades, the hottest topics in the field of remote sensing and ...GIS (geographic information system) environments have evolved from observing the nature of the earth. Owing to the enlargement of several worldwide modifications related to the nature of the earth, land use/land cover (LU/LC) change is considered as the matter of utmost importance in the natural atmosphere, and it has also become an interesting area to be studied by the researchers. As there is a lack of review articles in the land use/land cover change analysis process, we presented a comprehensive review which may help the researchers to proceed further. This paper deals with the most frequent methods used by researchers on various processes like pre-processing, classification, and prediction of time series satellite images for analyzing the LU/LC changes using satellite images. The generic flow of the LU/LC change analysis process and the challenges faced during each process by the researchers are discussed. Varied resolutions of the environmental image captured by remote sensing satellites for analyzing the LU/LC changes are discussed. Various LU/LC classes depending on change in the earth’s surface are also studied and the constraint used in each application is stated. The importance of this review lies in the motivation for future researchers to work on the LU/LC change analysis problem effectively.
In this article, a comprehensive review of the state-of-art graph-based learning methods for classification of the hyperspectral images (HSI) is provided, including a spectral information based graph ...semi-supervised classification and a spectral-spatial information based graph semi-supervised classification. In addition, related techniques are categorized into the following sub-types: (1) Manifold representation based Graph Semi-supervised Learning for HSI Classification (2) Sparse representation based Graph Semi-supervised Learning for HSI Classification. For each technique, methodologies, training and testing samples, various technical difficulties, as well as performances, are discussed. Additionally, future research challenges imposed by the graph-based model are indicated.
Selection of useful bands plays a very important role in hyperspectral image classification. In the past decade, metaheuristic algorithms have been used as promising methods for solving this problem. ...However, many metaheuristic algorithms may provide unsatisfactory performance due to their slow or premature convergence. Therefore, how to develop algorithms well balancing the exploration and exploitation, and find the suitable bands precisely is still a challenge. In this paper, a new hybrid global optimization algorithm, which is based on the Wind Driven Optimization (WDO) and Cuckoo Search (CS) is proposed to solve hyperspectral band selection problems. Both WDO and CS have strong searching ability and require less control parameters, but easily suffer from premature convergence due to loss of diversity of population. The proposed approach uses the Chebyshev chaotic map to initialize the population at initial step. The population is divided into two subgroups and WDO and CS are adopted for these two subgroups independently. By division, these two subgroups can share suitable information and utilize each other’s pros, thus avoid premature convergence, and obtain best optimal solution. Furthermore, the Levy flight step size in CS algorithm is adaptively adjusted based on fitness value and current iteration number, which helps in boosting the convergence speed of algorithm. The experimental results on three standard benchmark datasets namely, Pavia University, Botswana and Indian Pines, prove the superiority of the proposed approach over standard WDO and CS approaches as well as the other traditional approaches in terms of classification accuracy with fewer bands.
Hyperspectral band selection is one of the efficacious ways to diminish the size of hyperspectral images. The process of selecting a few useful bands will be successful when two fundamental aspects ...are considered: information abundance and redundancy among the chosen bands. However, selecting the suitable number of bands in an ill-posed classification problem remains challenging. Overcoming this issue, a novel unsupervised multi-objective multi-verse optimizer-based band selection (MOMVOBS) approach is proposed. It explores optimal trade-offs among the different traits of the objective functions namely information richness, less redundancy and the number of bands to be selected. These three objective functions are optimized simultaneously using a multiverse optimizer (MVO) to obtain the best solutions. To evaluate the quality of selected bands, two widely used supervised classifiers are used, such as support vector machine (SVM) and K-nearest neighbour (KNN). Experimental results evidence for the superiority of the proposed approach over the recent multi-objective optimization-based band selection approaches by selecting the highly informative distinct bands that have better classification performance on four benchmark hyperspectral data sets. The proposed MOMVOBS have obtained 79.50% and 71.35% of overall accuracy for SVM and KNN classifier, respectively, in Indian Pines dataset with 10% of band retention, 93.06% and 88.88% of overall accuracy for SVM and KNN classifier, respectively, in Salinas dataset with 10% of band retention, 92.86% and 85.35% of overall accuracy for SVM and KNN classifier, respectively, in Pavia University dataset with 15% band retention, and 92.42% and 85.33% of overall accuracy for SVM and KNN classifier, respectively, in Botswana dataset with 11% band retention. The achievement of higher accuracy at less than 15% bands is significant.
Hyperspectral image (HSI) consists of hundreds of contiguous spectral bands, which can be used in the classification of different objects on the earth. The inclusion of both spectral as well as ...spatial features stands essential in order that high classification accuracy is achieved. However, incorporation of the spectral and spatial information without preserving the intrinsic structure of the data leads on to downscaling the classification accuracy. To address the issue aforementioned, the proposed method which involves using unsupervised spectral band selection based on three major constrains: (i) low reconstruction error with neighbourhood bands, (ii) low noise, (iii) high information entropy, is put forward. In addition, the structure-preserving recursive filter is used to extract spatial features. Finally, the classification is performed using convolutional neural networks (CNNs) with different sets of convolutional, pooling, and fully connected layers. To test the performance of the proposed method, experiments have been carried out with three benchmark HSI datasets Indian pines, University of Pavia, and Salinas. These experiments reveal that the proposed method offers better classification accuracy over the purportedly state-of-the-art methods in terms of standard metrics like overall accuracy, average accuracy, and kappa coefficient (K). The proposed method has attained OAs of 99.9, 98.9, and 99.93% for the three datasets, respectively.
Hyperspectral images usually contain hundreds of contiguous spectral bands, which can precisely discriminate the various spectrally similar classes. However, such high-dimensional data also contain ...highly correlated and irrelevant information, leading to the curse of dimensionality (also called the Hughes phenomenon). It is necessary to reduce these bands before further analysis, such as land cover classification and target detection. Band selection is an effective way to reduce the size of hyperspectral data and to overcome the curse of the dimensionality problem in ground object classification. Focusing on the classification task, this article provides an extensive and comprehensive survey on band selection techniques describing the categorisation of methods, methodology used, different searching approaches and various technical difficulties, as well as their performances. Our purpose is to highlight the progress attained in band selection techniques for hyperspectral image classification and to identify possible avenues for future work, in order to achieve better performance in real-time operation.
Hyperspectral imaging (HSI), measuring the reflectance over visible (VIS), near-infrared (NIR), and shortwave infrared wavelengths (SWIR), has empowered the task of classification and can be useful ...in a variety of application areas like agriculture, even at a minor level. Band selection (BS) refers to the process of selecting the most relevant bands from a hyperspectral image, which is a necessary and important step for classification in HSI. Though numerous successful methods are available for selecting informative bands, reflectance properties are not taken into account, which is crucial for application-specific BS. The present paper aims at crop mapping for agriculture, where physical properties of light and biological conditions of plants are considered for BS. Initially, bands were partitioned according to their wavelength boundaries in visible, near-infrared, and shortwave infrared regions. Then, bands were quantized and selected via metrics like entropy, Normalized Difference Vegetation Index (NDVI), and Modified Normalized Difference Water Index (MNDWI) from each region, respectively. A Convolutional Neural Network was designed with the finer generated sub-cube to map the selective crops. Experiments were conducted on two standard HSI datasets, Indian Pines and Salinas, to classify different types of crops from Corn, Soya, Fallow, and Romaine Lettuce classes. Quantitatively, overall accuracy between 95.97% and 99.35% was achieved for Corn and Soya classes from Indian Pines; between 94.53% and 100% was achieved for Fallow and Romaine Lettuce classes from Salinas. The effectiveness of the proposed band selection with Convolutional Neural Network (CNN) can be seen from the resulted classification maps and ablation study.
As the hyperspectral image consists of hundreds of highly correlated spectral bands, the selection of informative and highly discriminative bands is necessary for hyperspectral image classification. ...The recent growth of machine learning and artificial intelligence techniques play a major role in various domains of hyperspectral image processing. In this paper, a comprehensive survey of machine learning and artificial intelligence technique-based band selection strategies for hyperspectral image classification is given. As per the outcome of this study, we have identified the research challenges and research for future directions in band selection strategies for hyperspectral image classification.
•In this work, data normalization is used to manage the computational burden.•HSI classification method is introduced using spectral and spatial features.•Proposed method shows good accuracy even ...with limited training samples.•Efficacy of proposed method is proven by comparing different state-of-art methods.
Hyperspectral image (HSI) classification is very important task having numerous applications in the remote sensing field. Many methods have been proposed in the recent years. Among them Convolutional Neural Network (CNN) based algorithms have shown higher performance. But these algorithms need high computational power and storage capacity. This Paper presents an approach for remote sensing hyper spectral image classification based on data normalization and CNN. HSI data is first normalized by reducing its scalar values by retaining complete information. Then, spectral and spatial information is extracted using Probabilistic Principal Component Analysis (PPCA) and Gabor filtering respectively. Further, the spectral and spatial information is integrated to form fused features. Finally classification task is done using simply designed CNN framework. Experiments are performed on three benchmark hyperspectral datasets (Indian Pines, Pavia University and Salinas). The proposed approach has achieved significant performance over the state-of-art methods. This can be useful in real world applications like agriculture, forestry and food processing.
The technological advancements in spectroscopy give rise to acquiring data about different materials on earth's surface which can be utilized in a variety of potential applications. But, the hundreds ...of spectral bands are generally equipped with highly correlated information with limited training samples. This will degrade the Hyperspectral Image (HSI) classification accuracy. So Dimensionality Reduction (DR) has become inevitable and necessary step need to incorporate before HSI classification. The main contribution of this work lies in comparative study and review on dimensionality reduction techniques for Hyperspectral remote sensing image classification. The related challenges and research directions are also discussed. This study will help the researchers in the Hyperspectral remote sensing community to choose the appropriate DR technique for classification which can be useful in various real time applications.
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