A Convolutional Neural Network with Fletcher–Reeves Algorithm for Hyperspectral Image Classification

Abstract

Deep learning models, especially the convolutional neural networks (CNNs), are very active in hyperspectral remote sensing image classification. In order to better apply the CNN model to hyperspectral classification, we propose a CNN model based on Fletcher–Reeves algorithm (F–R CNN), which uses the Fletcher–Reeves (F–R) algorithm for gradient updating to optimize the convergence performance of the model in classification. In view of the fact that there are fewer optional training samples in practical applications, we further propose a method of increasing the number of samples by adding a certain degree of perturbed samples, which can also test the anti-interference ability of classification methods. Furthermore, we analyze the anti-interference and convergence performance of the proposed model in terms of different training sample data sets, different batch training sample numbers and iteration time. In this paper, we describe the experimental process in detail and comprehensively evaluate the proposed model based on the classification of CHRIS hyperspectral imagery covering coastal wetlands, and further evaluate it on a commonly used hyperspectral image benchmark dataset. The experimental results show that the accuracy of the two models after increasing training samples and adjusting the number of batch training samples is improved. When the number of batch training samples is continuously increased to 350, the classification accuracy of the proposed method can still be maintained above 80.7%, which is 2.9% higher than the traditional one. And its time consumption is less than that of the traditional one while ensuring classification accuracy. It can be concluded that the proposed method has anti-interference ability and outperforms the traditional CNN in terms of batch computing adaptability and convergence speed.

Automated Classification of Crop Types and Condition in a Mediterranean Area Using a Fine-Tuned Convolutional Neural Network

“…The use of CNN is opening new perspectives for crop classification, feature extraction is not necessary or may be drastically reduced, saving computing time and simplifying the classification procedure. However, compared to other methods, CNN needs to substantially increase the number of training samples to improve the results and have robustness (Chen et al, 2019). In addition, their architecture must be carefully designed to optimize the results, and when using different data sources like in our case (VHR orthoimages and Sentinel-2 time series), the merging of these datasets should be tuned depending on the crop types in the area.…”

“…In this sense, Hu et al (2018) proposed an improved CNN to automatically construct the training dataset and classify Landsat-8 images in generic land cover types, obtaining an overall accuracy improved by 5% and 14% compared to the support vector machine method and the maximum likelihood classification method, respectively. Chang et al (2019) applied CNN to forest classification, Chen et al (2019) to the classification of hyperespectral images, and Wang et al (2019) to classify VHR imagery, also in generic land cover types. However, CNN have not been studied yet for the specific classification of crops at parcel level using a combination of VHR, multispectral and time-series images.…”

Combining 3D-CNN and Squeeze-and-Excitation Networks for Remote Sensing Sea Ice Image Classification

“…At present, remote sensing technology has been widely used in sea ice detection. During recent years, the data sources commonly used include synthetic aperture radar [6], multispectral satellite images with medium or high-spatial resolution (e.g., MODIS and Landsat), and hyperspectral images [7][8][9]. Especially for multispectral and hyperspectral remote sensing data, they have the advantages of wide coverage, high resolution, rich spectral information and spatial information, multiple data sources, and low data cost, which provides abundant data support for sea ice detection [10].…”

An Encoder–Decoder Convolution Network With Fine-Grained Spatial Information for Hyperspectral Images Classification

“…With the development of Convolutional Neural Network (CNN), various CNN architectures [10]- [13] are performed on HSIs to extract high-level spectral, spatial and spectralspatial features [14]- [16], such as Google Inception [17], VGG, ResNet [18] and DenseNet [19]. These CNN-based methods [20]- [22] made an end-to-end training process with the supervision of high-level class labels [23]- [26].…”