A Novel Densenet-324 Densely Connected Convolution Neural Network for Medical Crop Classification using Remote Sensing Hyperspectral Satellite Images

In the past few decades, importance of the medicinal Crops is extending to a large extent due to its benefits in treating life-threatening diseases. Medicinal Crop has excellent medicinal properties on its roots, stem, and leaves to prevent human and animal health. Particularly detection and identification of the Crop classes are effectively carried out using hyperspectral images as discrimination of the target feature or objects is simple and it contains rich information containing the spatial and temporal details of underlying the land cover. However, Crop classification using machine learning architectures concerning spectral characteristics obtained on the anatomical features and morphological features. Extracted features towards classification lead to several challenges such as large spatial and temporal variability and spectral signatures similarity between different objects. A further hyperspectral image poses several difficulties with changes in illumination, environment, and atmospheric aspects. To tackle those non-trivial challenges, DenseNet-324 Densely Connected convolution neural network architecture has been designed in this work to discriminate the crop and medical Crop effectively in the interested areas.  Initially, the Hyperspectral image is pre-processed against a large number of noises through the employment of the noise removal technique and bad line replacement techniques. Pre-processed image is explored to image segmentation using the global thresholding method to segment it into various regions based on spatial pieces of information on grouping the neighboring similar pixels intensity or textures. Further regions of the image are processed using principle component analysis to extract spectral features of the image. That extracted feature is employed to ant colony optimization technique to obtain the optimal features. Computed optimal features are classified using Convolution Neural Network with a hyper parameter setup. The convolution Layer of the CNN architecture process spatial, temporal, and spectral feature and generates the feature map in various context, generated feature map is max pooled in the pooling layer and classified into crops and medicinal Crop in the SoftMax layer. Experimental analysis of the proposed architecture is carried out on the Indiana Pines dataset using cross-fold validation to analyze the representation ability to discriminate the features with large variance between the different classes. From the results, it is confirmed that the proposed architecture exhibits higher performance in classification accuracy of 98.43% in classifying the Crop species compared with conventional approaches.&nbsp

Simultaneous Spectral-Spatial Feature Selection and Extraction for Hyperspectral Images

In hyperspectral remote sensing data mining, it is important to take into account of both spectral and spatial information, such as the spectral signature, texture feature and morphological property, to improve the performances, e.g., the image classification accuracy. In a feature representation point of view, a nature approach to handle this situation is to concatenate the spectral and spatial features into a single but high dimensional vector and then apply a certain dimension reduction technique directly on that concatenated vector before feed it into the subsequent classifier. However, multiple features from various domains definitely have different physical meanings and statistical properties, and thus such concatenation hasn't efficiently explore the complementary properties among different features, which should benefit for boost the feature discriminability. Furthermore, it is also difficult to interpret the transformed results of the concatenated vector. Consequently, finding a physically meaningful consensus low dimensional feature representation of original multiple features is still a challenging task. In order to address the these issues, we propose a novel feature learning framework, i.e., the simultaneous spectral-spatial feature selection and extraction algorithm, for hyperspectral images spectral-spatial feature representation and classification. Specifically, the proposed method learns a latent low dimensional subspace by projecting the spectral-spatial feature into a common feature space, where the complementary information has been effectively exploited, and simultaneously, only the most significant original features have been transformed. Encouraging experimental results on three public available hyperspectral remote sensing datasets confirm that our proposed method is effective and efficient

Locality and Structure Regularized Low Rank Representation for Hyperspectral Image Classification

Hyperspectral image (HSI) classification, which aims to assign an accurate label for hyperspectral pixels, has drawn great interest in recent years. Although low rank representation (LRR) has been used to classify HSI, its ability to segment each class from the whole HSI data has not been exploited fully yet. LRR has a good capacity to capture the underlying lowdimensional subspaces embedded in original data. However, there are still two drawbacks for LRR. First, LRR does not consider the local geometric structure within data, which makes the local correlation among neighboring data easily ignored. Second, the representation obtained by solving LRR is not discriminative enough to separate different data. In this paper, a novel locality and structure regularized low rank representation (LSLRR) model is proposed for HSI classification. To overcome the above limitations, we present locality constraint criterion (LCC) and structure preserving strategy (SPS) to improve the classical LRR. Specifically, we introduce a new distance metric, which combines both spatial and spectral features, to explore the local similarity of pixels. Thus, the global and local structures of HSI data can be exploited sufficiently. Besides, we propose a structure constraint to make the representation have a near block-diagonal structure. This helps to determine the final classification labels directly. Extensive experiments have been conducted on three popular HSI datasets. And the experimental results demonstrate that the proposed LSLRR outperforms other state-of-the-art methods.Comment: 14 pages, 7 figures, TGRS201