I. INTRODUCTION

II. HYPERSPECTRAL IMAGE CLASSIFICATION (BACKGROUND AND CHALLENGES)

A. Traditional to DL Models

B. Hyperspectral Data Characteristics and DL Challenges

III. HSI REPRESENTATION

A. Spectral Representation

B. Spatial Representation

C. Spectral-Spatial Representation

IV. LEARNING STRATEGIES

A. Supervised Learning

B. Unsupervised Learning

C. Semi-supervised Learning

V. DEVELOPMENT OF DNNs (TYPES OF LAYERS)

A. Fully Connected Layers

B. Convolutional Layers

C. Activation Layers

D. Pooling or Sub-sampling Layers

VI. CONVOLUTIONAL NEURAL NETWORK (CNN)

VII. AUTOENCODERS (AE)

VIII. DEEP BELIEF NETWORK (DBN)

IX. RECURRENT NEURAL NETWORK (RNN)

X. STRATEGIES FOR LIMITED LABELED SAMPLES

A. Data Augmentation

B. Semi-Supervised/Unsupervised Learning

C. Generative Adversarial Network (GAN)

D. Transfer Learning

E. Active Learning

XI. EXPERIMENTAL EVALUATION

A. Experimental Datasets

B. Experimental Results on Disjoint Train/Test Samples

C. Experiments with Convolutional Feature Extractors

XII. CONCLUSION AND FUTURE DIRECTIONS