I. INTRODUCTION

II. HYPERSPECTRAL IMAGE CLASSIFICATION (BACKGROUND AND CHALLENGES)

III. HSI REPRESENTATION

IV. LEARNING STRATEGIES

V. DEVELOPMENT OF DNNs (TYPES OF 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

XI. EXPERIMENTAL EVALUATION

XII. CONCLUSION AND FUTURE DIRECTIONS