JAGAN: A Framework for Complex Land Cover Classification Using Gaofen-5 AHSI Images

Abstract

Owing to their powerful feature extraction capabilities, deep learning-based methods have achieved significant progress in hyperspectral remote sensing classification. However, several issues still exist in these methods, including a lack of hyperspectral datasets for specific complicated scenarios and the need to improve the classification accuracy of land cover with limited samples. Thus, to highlight and distinguish effective features, we propose a hyperspectral classification framework based on a joint channel-space attention mechanism and generative adversarial network (JAGAN). To relearn featurebased weights, a higher priority was assigned to important features, which was developed by integrating a two-joint channelspace attention model to obtain the most valuable feature via the attention weight map. Additionally, two classifiers were designed in JAGAN: sigmoid was used to determine whether the input data were real or fake samples produced by the generator, while Softmax was adopted as a land cover classifier to yield the prediction type labels of the input samples. To test the classification performance of the JAGAN model, we used a selfconstructed complex land cover dataset based on GaoFen-5 AHSI images, which consists of mixed landscapes of mining and agricultural areas from the urban-rural fringe. Compared with other methods, the proposed model achieved the highest overall classification accuracy of 86.09%, the highest kappa amount of 79.41%, the highest F1 score of 85.86%, and the highest average accuracy of 82.30%, indicating the JAGAN can effectively improve the classification accuracy for limited samples in complex regional environments using GF-5 AHSI images.

Classification of Heterogeneous Mining Areas Based on ResCapsNet and Gaofen-5 Imagery

“…The training points used by JAGAN were with the same numbers as for our study. However, our selected data polygons only accounted for 10% of the total area, which was smaller than that of Chen et al [39] who used more test samples.…”

“…The prediction map by ResCapsNet is smoother. Moreover, the bare surface land was not classified by Chen et al [39].…”

“…We compared results with those of the JAGAN model [39] applied to a GF-5 image of the Wuhan study area. The OA, F1-score, and Kappa values of JAGAN were 86.09%, 85.86%, 79.41%, respectively.…”

“…The GF-5 images on 9 May 2018 and 25 September 2019 for the two areas were obtained. Radiometrically calibrated and orthorectification correction were conducted [39]. The GF-5 satellite, successfully launched in May 2018, can conduct comprehensive observations of the land and atmosphere at the same time, and can obtain the spectrum range from visible and near-infrared (VNIR) to shortwave infrared (SWIR) (i.e., from 400 to 2500 nm) with a width of 60 km and a spatial resolution of 30 m. There are 330 spectral channels.…”

“…In addition, recurrent neural networks (RNN) [36], DBNs [37], and generative adversarial networks (GAN) [38] have also been frequently used for HSI classification. Chen et al [39] used a joint channelspace attention mechanism and GAN (JAGAN) model and HSI to classify complex mining landscapes. However, CNNs are most widely used algorithms to capture deep features in HSI classification tasks.…”