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We present an innovative hyperspectral image (HSI) classification method addressing challenges posed by closely spaced wavelength bands. Our approach combines 3D-2D convolutional neural networks (CNNs) with multi-branch feature fusion for improved spectral-spatial feature extraction. Using segmented principal component analysis (Seg-PCA), we reduce HSIs' spectral dimensions into global and local intrinsic characteristics. The integration of 3D and 2D CNNs captures joint spectral-spatial features, while a multi-branch network extracts and merges diverse local features along the spectral dimension. Our method outperforms existing approaches, achieving remarkable accuracy of 99.27%, 100%, and 99.99% on Indian Pines, Salinas Scene, and University of Pavia datasets, respectively. [ABSTRACT FROM AUTHOR]

The use of hyperspectral imaging sensors has greatly improved the classification of remotely sensed data because of the abundant spectral information they offer. However, the numerous contiguous, tiny wavelength bands captured in hyperspectral images (HSIs) often hinder the classification process. To overcome the aforementioned problem, various feature reduction techniques, including feature extraction (FE) and feature selection (FS), are commonly employed to improve classification performance. Linear discriminant analysis (LDA) is a well-established approach that has been utilized for the FE of HSI. LDA's consideration of global characteristics and the variance accumulator for FS can lead to the poor reduction of HIS's intrinsic characteristics. Furthermore, LDA's limited ability to select a very low number of features, i.e. the number of classes minus one, restricts its effectiveness in HSI classification. Therefore, we introduce an FS method based on a non-linear information-theoretic measure, normalized mutual information (nMI) combined with minimum redundancy maximum relevance (mRMR). This approach is used to identify inherent features from the transformed space of our proposed correlation-based segmented-LDA (SLDA) and spectral region-based segmented-LDA (SSLDA) FE methods. We thoroughly compare the performance of the SLDA-mRMR and SSLDA-mRMR methods with the existing linear and non-linear state-of-the-art techniques, including unsupervised principal component analysis (PCA), PCA-based methods, supervised LDA, and LDA-based methods. Additionally, we explore the performance of nMI-based mRMR selection with all FE methods by incorporating a cumulative variance-based top features pick-up strategy. Based on the experimental results, we observe that SSLDA-mRMR and SLDA-mRMR achieve the highest classification accuracy, such as 91.91%, and 91.57% for agricultural Indian Pines, respectively, 97.66%, and 97.54%, respectively, for Kennedy Space Center, and 96.69%, and 96.57%, respectively, for Pavia University. In contrast, the classification accuracies using all original features of the HSIs are 71.39%, 70.01%, and 83.52%, respectively. Moreover, our proposed SSLDA-mRMR and SLDA-mRMR combinations outperformed all other FE and FS combinations that were examined using actual HSI datasets. [ABSTRACT FROM AUTHOR]

Hyperspectral images (HSIs) contain notable information of land objects by acquiring an immense set of narrow and contiguous spectral bands. Feature extraction (FE) and feature selection (FS) as dimensionality (band) reduction strategies are performed to enhance the classification result of HSI. Principal component analysis (PCA) is frequently exploited for the FE of HSI. However, it often possesses the inability to extract local and subtle HSI structures. As such, segmented-PCA (SPCA), spectrally segmented-PCA (SSPCA) and folded-PCA (FPCA) are presented for local and useful FE from the HSI. In this paper, we propose two FE methods called segmented-FPCA (SFPCA) and spectrally segmented-FPCA (SSFPCA). SFPCA exploits SPCA and FPCA while SSFPCA exploits SSPCA and FPCA together. In particular, SFPCA and SSFPCA apply FPCA on highly correlated and spectrally grouped HSI bands, respectively. We consider nonlinear methods Kernel-PCA (KPCA) and Kernel entropy component analysis (KECA) for extended comparison. For the experimented agricultural Indian Pine and urban Washington DC Mall HSIs, the results manifest that SFPCA (95.6262% for the agricultural HSI and 97.4782% for the urban HSI) and SSFPCA (96.3221% for the agricultural HSI and 98.0116% for the urban HSI) outperform the conventional methods. [ABSTRACT FROM AUTHOR]

The hyperspectral remote sensing images (HSIs) are acquired to encompass the essential information of land objects through contiguous narrow spectral wavelength bands. The classification accuracy is not often satisfactory in a cost-effective way using the entire original HSI for practical applications. To enhance the classification result of HSIs the band reduction strategies are applied which can be divided into feature extraction and feature selection methods. PCA (Principal Component Analysis), a linear unsupervised statistical transformation, is frequently adopted for the extraction of features from HSIs. In this paper, PCA and SPCA (Segmented-PCA), SSPCA (Spectrally Segmented-PCA), FPCA (Folded-PCA) and MNF (Minimum Noise Fraction) as linear variants of PCA together with KPCA (Kernel-PCA) and KECA (kernel Entropy Component Analysis) as nonlinear variants of PCA have been investigated. The top transformed features were picked out using accumulation of variance for all other feature extraction methods except for MNF and KECA. MNF uses SNR (Signal-to-Noise Ratio) values and KECA employs Renyi quadratic entropy measurement for this purpose. The studied approaches are equated and analyzed for Indian Pine agricultural and urban Washington DC Mall HSI classification using SVM (Support Vector Machine) classifier. The experiment illustrates that the costly effective and improved classification performance of the feature extraction approaches over the performance using the entire original dataset. MNF offers the highest classification accuracy and FPCA offers the least space and time complexity with satisfactory classification result. [ABSTRACT FROM AUTHOR]

Change detection (CD) of any surface using multitemporal remote sensing images is an important research topic since up-to-date information about earth surface is of great value. Abrupt changes are occurring in different earth surfaces due to natural disasters or man-made activities which cause damage to that place. Therefore, it is necessary to observe the changes for taking necessary steps to recover the subsequent damage. This paper is concerned with this issue and analyzes statistical similarity measure to perform CD using remote sensing images of the same scene taken at two different dates. A variation of normalized mutual information (NMI) as a similarity measure has been developed here using sliding window of different sizes. In sliding window approach, pixels’ local neighborhood plays a significant role in computing the similarity compared to the whole image. Thus the insignificant global characteristics containing noise and sparse samples can be avoided when evaluating the probability density function. Therefore, NMI with different window sizes is proposed here to identify changes using multitemporal data. Experiments have been carried out using two separate multitemporal remote sensing images captured one year apart and one month apart, respectively. Experimental analysis reveals that the proposed technique can detect up to 97.71% of changes which outperforms the traditional approaches. [ABSTRACT FROM AUTHOR]